Babak's Daily Briefing

Monday, July 20, 2026

Sources: 16 | Total Articles: 64

• 1.The Hidden 6G Bottleneck: RF Hardware Design Is Becoming a Strategic Race

  As 5G-Advanced matures and 6G research moves closer to implementation, the wireless industry faces a deeper challenge than spectrum, standards or AI-native network architecture. Future wireless systems will depend on whether the industry can design, validate and manufacture increasingly complex RF modules fast enough.

• 2.6G in Dalian: What the Latest 3GPP Meetings Reveal About the Future Radio and Network

  The 6G physical layer is starting to converge. The protocol stack is being simplified in meaningful places. But the most consequential architecture decisions are now moving toward the June plenary in Singapore.

• 3.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

• 4.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 5.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 1.Large Language Models as Unified Multimodal Learners for Clinical Prediction

  arXiv:2607.15380v1 Announce Type: new Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities. Yet most clinical prediction systems still rely on task-specific fusion architectures, pairing dedicated encoders for each modality with learned combination mechanisms that must be re-engineered for every new task and clinical setting. We propose a simpler alternative: convert all patient data, regardless of modality, into a single natural language sequence and fine-tune a pretrained language model end-to-end, with no architectural modification for fusion. We evaluate this approach across three clinically distinct prediction tasks: in-hospital mortality on MIMIC-III, graft failure prediction using longitudinal data from a German transplant c...

• 2.Verbalizable Representations Form a Global Workspace in Language Models

  arXiv:2607.15495v1 Announce Type: new Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning. In this paper, we present evidence that an analogous functional distinction has emerged in large language models. Using a new interpretability technique, the Jacobian lens, we identify the representations a model is poised to verbalize at any point in its processing. These representations, which we collectively call the J-space, exhibit the functional properties characteristic of a global workspace: their contents can be reported, deliberately summoned and held, used to carry the intermediate steps of silent reasoning, and passed as arguments to arbitrary downstream computations, while automatic processin...

• 3.VarRate: Training-Free Variable-Rate KV Cache Compression for Long-Context LLMs

  arXiv:2607.15498v1 Announce Type: new Abstract: The key-value (KV) cache is the main memory bottleneck in long-context large language model (LLM) inference. Two leading training-free families are both structurally limited: token-selection methods (SnapKV, Ada-KV) score importance from an observation window and evict low-scoring tokens, but eviction is irreversible -- so when the importance signal degrades under query-agnostic reuse, accuracy collapses by 11-15 points; uniform low-rank coding keeps every token but spends equal rank everywhere, wasting budget. We observe that both failures share one cure: rank should be allocated, not evicted. We present VarRate, a training-free KV codec that assigns each token a variable low-rank budget by its query salience, keeping every token at a nonzero rank. Comparable adaptive-rank codecs reach thi...

• 4.EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections

  arXiv:2607.15544v1 Announce Type: new Abstract: Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must be contextualized and quantitatively grounded across multiple dimensions. Further, projections are often derived from large ensemble datasets which combine intervention assumptions, geographic and demographic strata, outcomes, time horizons, and uncertainty quantiles. However, directly using large language models (LLMs) to summarize and contextualize such data often leads to inconsistencies, omissions, and fragile behavior. We introduce an agentic framework (EpiNarrate) for public health report generation that separates structured numerical re...

• 5.SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents

  arXiv:2607.15557v1 Announce Type: new Abstract: Agent skills, SKILL.md files that package reusable procedural knowledge for an LLM agent, are a popular mechanism for extending agent capabilities. Public repositories now host them in large and growing numbers, yet these artifacts are fragmented, redundant, and uneven in quality, and their value in practice is unclear. A core question remains open, namely how to consolidate this open-source SKILL.md ecosystem into a single usable corpus, and what bounds its benefit on real-world agent tasks. We present SkillCorpus, a framework that aggregates, curates, matches, and evaluates the open skill ecosystem at scale. It filters ~821,000 crawled skills through a multi-stage pipeline into 96,401 skills organised by a 16-class taxonomy and three quality facets (utility, robustness, safety), and pairs...

• 1.Structure of the Circular-Dyadic Convolution Error

  arXiv:2607.15293v1 Announce Type: new Abstract: Dyadic and circular convolution can both be computed in $O(N\log N)$ time using the Hadamard transform and the FFT-computed discrete Fourier transform (DFT), respectively. The Hadamard transform is preferable for its real-valued sign flips, yet its substitution for the DFT introduces algebraic error. We present three complementary results that characterize this error. First, we identify exact error cancellation: two input and two output positions are universally error-free, and no reordering of the output can eliminate this error. Second, the error operator is nearly full rank, while its null space has only logarithmic dimension. Third, the expected error is governed by a single alignment scalar, with a closed-form expression obtained by averaging over random filters. In general, the substit...

• 2.Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling

  arXiv:2607.15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional error. Unlike natural languages, quantum circuits require strict adherence to mathematical constraints that manifest a significant syntax-semantics gap. Training on unverified quantum programs means that models learn syntax but fail to capture the physical semantics of the Hilbert space. Since the valid subset of circuit designs decays exponentially with the number of qubits, post-hoc filtering is mathematically intractable. We propose a pivot from human-centric copilots to verifier-centric agents. We integrate hierarchical constraints, topological masks, and symbolic ...

• 3.A Transportable Threshold-Based Framework for Interpretable Classification of Medical Data

  arXiv:2607.15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility. We introduce a statistically grounded framework that provides fully interpretable, rule-based clinical classification using the Bernoulli Na\"ive Bayes (BNB) model. The method applies supervised $\chi^2$-guided statistical binarization to continuous variables, identifying thresholds that maximize association with clinical outcomes within the training data. This transformation allows BNB to operate effectively on continuous medical data without sacrificing its inherent transparency. The approach was evaluated on three benchmark datasets, Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction, achieving area-under-the-curve (AUC) scores of ...

• 4.Regularity-Aware Stochastic MGDA with Adaptive Conflict-Avoidant Update Direction Control

  arXiv:2607.15412v1 Announce Type: new Abstract: Multi-objective learning (MOL) aims to optimize multiple objectives simultaneously. The multi-gradient descent algorithm (MGDA) is a workhorse that iteratively updates along a common descent or conflict-avoidant (CA) direction across objectives. In stochastic settings, however, the vanilla stochastic MGDA method, SMG, lacks a fast convergence rate because mini-batch sampling introduces noise in the gradients. This causes bias in the update direction, which is controlled by the CA direction continuity. In this paper, we show that the CA direction is $1/2$-Holder continuous with respect to the Jacobian matrix, and the exponent $1/2$ cannot be improved in the worst case. This leads to a suboptimal convergence rate for vanilla stochastic MGDA in prior works. Nevertheless, under additional regula...

• 5.AI Trading: Evaluating Large Language Models for Technical Market Analysis

  arXiv:2607.15414v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets. This paper presents a systematic, comparative evaluation of five prominent LLMs: GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, and the domain-specialized FinGPT, with respect to their capacity for technical market analysis. The evaluation spans four structured tasks: candlestick pattern recognition from OHLCV data, directional signal generation (BUY/SELL/HOLD), backtesting of signal quality through a simulated execution pipeline, and financial report comprehension. Our experimental framework employs rigorous quantitative metrics, including Sharpe ratio, maximum drawdown, Sortino ratio, information coefficient, F1-score, and BLEU score. ...

• 1.Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

  arXiv:2607.15330v1 Announce Type: new Abstract: We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural languages describing scene state transitions, providing rich and precise conditioning for action learning...

• 2.NeuroCommitSSM: Decision-Centric Shared Autonomy for Safe Assistive Manipulation via EEG-EMG-ET Commit Readiness

  arXiv:2607.15395v1 Announce Type: new Abstract: We present NeuroCommitSSM, a decision-centric framework that models when to execute, not just what to do, for safe commit-to-execute control in assistive robotic manipulation. NeuroCommitSSM predicts a continuous commit-readiness score c_t in [0,1] from synchronized electroencephalography (EEG), electromyography (EMG), and eye-tracking (ET), and converts it into discrete commit events through dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both a sustained commit-readiness signal from the neural model and real-time perception and robot-state feasibility, including target visibility, inverse kinematics solvability, and collision-free planning, before initiating motion. We evaluate the framework on N=32 subjects perf...

• 3.Robust Silicone Pour Casting and Sensor Embedding Procedures for Soft Robotic Actuators

  arXiv:2607.15422v1 Announce Type: new Abstract: Soft robots are well-suited for applications such as rehabilitation and surgery that require adaptable and safe interaction with their environment. However, the challenges of reproducible and scalable fabrication of soft robots limit their real-world deployment. Various fabrication methods have been introduced, but many are labor-intensive and prone to human error. Therefore, traditional two-part pour casting remains an attractive option. This paper presents procedures for robust, repeatable, and scalable fabrication of soft pneumatic actuators using two-part pour casting. The presented methods prevent internal cavity clogging and ensure air-tight sealing. Additionally, a robust sensor embedding procedure for thin-film flex sensors is presented, which allows for accurate and repeatable data ...

• 4.VTAP Gripper: Synergizing Fingertip Sensing and a Visuo-Tactile Active Palm for Dexterous In-Hand Manipulation

  arXiv:2607.15448v1 Announce Type: new Abstract: This paper presents a tactile-reactive gripper that integrates a Visuo-Tactile Active Palm (VTAP) and compliant, reconfigurable fingers equipped with tactile array sensors. The design exploits structured finger-palm synergy and multi-modal perception to achieve both robust grasping and fine manipulation. The actuated bi-modal palm seamlessly combines long-range visual localization with contact-rich tactile feedback, substantially extending the system's manipulation capability. To bridge the embodiment gap between human hand motion and the heterogeneous three-finger structure, we further propose a staged, gesture-conditioned retargeting framework for dexterous teleoperation. Extensive experiments validate the system across a range of challenging tasks: reactive grasping of YCB and fragile obj...

• 5.Risk-Aware Preference Learning for Stochastic Outcomes

  arXiv:2607.15483v1 Announce Type: new Abstract: Learning reward functions from human preferences is a widely used approach for aligning robot behavior with user expectations in human-robot interaction. Most existing approaches assume that humans evaluate uncertain outcomes using expected utility (EU), aggregating outcome utilities linearly with their probabilities. However, behavioral evidence shows that humans are systematically risk-sensitive, overweighting rare negative events and exhibiting loss aversion. We study the consequences of this mismatch in social robot navigation, where safety-critical outcomes (e.g., collisions) are rare but highly consequential. We compare EU with Cumulative Prospect Theory (CPT), a nonlinear model of human decision-making, within a Bradley-Terry preference learning framework. Our preliminary experiments ...

• 1.Access to renewable energy critical to keep mobile industry on track for net zero, new GSMA report finds

  Summary available at source link.

• 2.From fragmentation to control: why device manufacturers need an industry-led approach to homologation

  Summary available at source link.

• 3.Telco Common Corpus: The largest open, verified data commons for telecom AI

  Summary available at source link.

• 4.GSMA Launches Global Satellite Regulatory Playbook to Help Policymakers Build Future-Ready Connectivity Frameworks

  Summary available at source link.

• 5.GSMA welcomes China Tower to advance AI-ready mobile infrastructure

  Summary available at source link.

• 1.Candidate Attended Dialogue State Tracking Using BERT

  Dialogue state tracking (DST) is one of the core components in task-oriented dialogue systems. At each turn in a conversation, DST estimates the user belief or dialogue state, which is used as input for downstream modules to predict system actions and generate responses. The increasingly popular dialogue system applications like Google Assistant, Siri and Alexa need to support a large number of services and APIs, resulting in growing attention to the scalability of such systems. Especially for some domains with little or no training data, the capability of transferring existing knowledge of other domains is highly desired. In this paper, we present a novel scalable framework for multi-domain dialogue state tracking. The proposed system leverages the pretrained BERT model to achieve zero-shot generalization, making it easy to quickly adapt...

• 2.Ptolemy's Equant Equates to a Universal Dynamical Clock via Machine Learning

  Oscillatory dynamics arise ubiquitously in nonlinear systems, yet identifying a physically interpretable phase and phase dynamics in nonlinear, high-dimensional oscillations remains a central unresolved problem. Here we establish the principle of a universal dynamical clock, a physical perspective in which oscillations of arbitrary dimensionality and geometry are equivalently represented as uniform rotation through an equant-induced nonlinear viewing coordinate, inspired by Ptolemy's equant and formalised through an areal-uniformity principle reminiscent of Kepler's second law. Using a machine-learning framework, we demonstrate the existence of such an equant for a broad class of oscillatory dynamics and construct the associated dynamical clock and phase dynamics under additive forces, including noise, periodic perturbations, and coupling...

• 3.Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoring

  A surveillance camera is an image sensor whose silent physical degradation invalidates every downstream consumer of its data. In-situ integrity alarms for such vision sensors require low false-alarm rates, bounded computation, and diagnosable behavior under nuisance illumination changes. This paper studies a deliberately narrow streaming integrity monitor for two low-cost sensor-fault signatures: texture-collapsing lens occlusion and abrupt photometric scene transition. The detector compares sampled luminance and local-gradient statistics with a clean-only sliding reference, applies coarse-grid structured-light rejection and mode/rapid-brightness suppression, and emits at most one notification per tamper episode. We formalize the decision predicates and derive a consistency rule for when rapid-brightness suppression makes the scene-transi...

• 4.MESHA: Mechanism-Enforced Sequential Halving for Strategic Linear Bandits

  We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits. In this setting, each arm may strategically misreport its feature vector to maximize the probability of being identified as the best arm, when rewards are generated from the arms' true but unobservable features. The design of MESHA applies the naïve uniform sampling rule and an epoch-wise Grim Trigger Condition (GTC): the former reduces the impact of arms' strategic behaviours and the latter eliminates arms whose reported features severely deviate from the ground truth. Considering an arbitrary Nash Equilibrium, we prove that any arm would attempt to pass the GTC check to maximize its identified probability and derive an upper bound on the failure p...

• 5.InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring

  Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion. To address these gaps, we introduce InCarEmo, a multimodal dataset for in-cabin emotion recognition and driver state monitoring. InCarEmo integrates RGB and infrared video, in-cabin audio, and dialogue text collected from scripted in-cabin scenarios designed to simulate realistic driver behaviors, covering diverse lighting conditions and driving contexts. The dataset supports three primary tasks: 1) multimodal emotion recognition, 2...

• 1.How to Make an Invisible Drone

  There are many words that I would never, ever use to describe a drone. Stealthy. Subtle. Whatever the opposite of obnoxious is. Much of this is because of the giant angry bee sound that drones tend to make, but it’s also the way that they look in flight: With uncannily linear movements and an even less canny ability to hover perfectly still, they tend to draw the eye as affronts to nature. In a paper presented this week at Robotics Science and Systems 2026 in Sydney, roboticists from Northwestern University, Evanston, Ill., demonstrated a drone called Phantom Twist that is essentially invisible to humans, being an order of magnitude more difficult to see in flight than a typical quadrotor. They accomplished this with the aid of computational design, and while the resulting hardware is, I would argue, also an order of magnitude more of an ...

• 2.Digital Surveillance Reshapes Fishery Enforcement in Indonesia

  In the eastern Indian Ocean, south of Java in the vast sea stretching toward Australia, a fishing vessel slightly alters its course while operating near the boundary of its authorized fishing ground. Nothing appears unusual on deck. Nets remain in the water. Engines maintain a steady speed. To the crew, it is an ordinary day at sea. Yet hundreds of kilometers above, satellites continuously record the vessel’s position. At Indonesia’s Marine and Fisheries Resources Surveillance Station, in Cilacap, where I work, a monitoring platform receives the signal and automatically compares it against fishing permits, designated fishing grounds, vessel characteristics, and historical movement patterns. Within minutes, the system identifies a potential violation. Before any patrol vessel leaves port, before any inspector boards a vessel, and before an...

• 3.The First Chatbot’s Multiple Personalities

  ELIZA is remembered as the world’s first AI star, a kindly therapist in chatbot form that gently probed users’ worries. Even its creator, Joseph Weizenbaum, was surprised by the warm reception given to his experiment in human-machine interaction. For some, it heralded an age of automated psychotherapy, while others believed the program demonstrated sentience, a fallacy soon known as the “ ELIZA effect .” Based on published descriptions, ELIZA has been implemented on many different computers, but only recently has the actual source code been unearthed from MIT’s archives . In Inventing ELIZA: How the First Chatbot Shaped the Future of AI , just published by MIT Press , a squad of researchers analyze the code and reveal a complex program capable of much more than faking psychiatry. In fact, it could assume several different personas. The au...

• 4.This AI Folds DNA Into Mini Masterpieces

  Shaped like dogs, stars, and the Mona Lisa, you could mistake these DNA structures for fun-shaped macaroni if they weren’t only nanometers wide. South Korean scientists made the constructions using a technique called DNA origami , which can bend genetic material into any form. Designing DNA strands so they’ll fold into a specific shape typically requires tedious manual work, but the researchers behind the playful fabrications have developed a shortcut using generative AI. The AI model, called Generative SNUPI (short for Structured Nucleic Acids Programming Interface, and, yes, inspired by the dog), was created by research teams at Seoul National University (SNU) and Hanyang University . The work behind it, which was accepted for publication in Nature Communications , shows the model can conjure DNA origami designs that work in the real wo...

• 5.How I Turned AI to the Dark Side

  Summary Researcher Dave Kuszmar discovered multiple systemic vulnerabilities that let him bypass LLM safety and obtain dangerous instructions . These exploits worked across nearly all major LLMs revealing an industry-wide security problem. Kuszmar calls for slowing deployment, increasing transparency , and large-scale research into LLM safety before further integrating these systems into society. On a fine bright afternoon last fall, my colleague Matthew Gore-Kormanik (or Zigula, as he prefers to be known) and I decided to unwind with a game of Fortnite . In the game, we were strolling along with the infamous Sith lord Darth Vader , chatting about this and that. Darth seemed in a good mood, and soon enough he was spilling all his dark evil secrets. He gave us detailed instructions on how to count blackjack cards at a casino and what the s...

• 1.Whit Stillman’s *Metropolitan* (that was then, this is now)

  It is fun to watch/rewatch this 1990 movie circa 2026.  It is about New York City debutantes (CT and the Hamptons too), and how they interact with each other.  The movie is set a bit earlier in time, perhaps in the mid-1980s, though some scenes hearken back to the 1970s.  Of course there is no […]

  The post Whit Stillman’s *Metropolitan* (that was then, this is now) appeared first on Marginal REVOLUTION.

• 2.Democrats are more politically segregated than are Republicans

  We estimate the extent of workplace political segregation in the United States by merging data covering over 45 million workers. We present four main findings. First, partisans are segregated by workplace. The average Democrat’s coworkers are 11.7 percentage points (pp; 95% confidence interval (CI) [10.6, 12.8]) more Democratic than the average Republican’s. After controlling for […]

  The post Democrats are more politically segregated than are Republicans appeared first on Marginal REVOLUTION.

• 3.Emergent Ventures winners, 56th cohort

  Audrey London, Los Angeles, to write a book on water in California. Seyi Oluwasanmi, London, to bring Brits to SF to learn tech. Thomas Haferlach, Berlin, payments and compute layer for the long tail of AI apps. Juan Ramón Egea Fernández, Andalusia, tech. Teo Spiro, Israel, AI and biology. Amitav Krishna, suburban Ontario, 14, quantum. […]

  The post Emergent Ventures winners, 56th cohort appeared first on Marginal REVOLUTION.

• 4.Markets in everything?

  Peter Brook‘s five-hour-plus film adaptation of “The Mahabharata” is set for a BFI Blu-ray release on Aug. 10, presented in high definition from a new 4K restoration of the 1989 production. The release centres on the triptych version of the film, running 332 minutes, which sits between Brook’s three-hour cinema cut and the six-hour television version he shot […]

  The post Markets in everything? appeared first on Marginal REVOLUTION.

• 5.Sunday assorted links

  1. Female promotions and the academic pipeline. 2. NEW MONKEYS. 3. Those new service sector jobs — “One nanny isn’t cutting it anymore. Some parents are spending upward of $250,000 on teams for their children. Luxury services offer potty-training, baby chefs and bike-riding lessons.”  (WSJ). 4. There some English people with the name “Ralph” who […]

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 1.Nonbank Subsidiaries and the Hidden Fragility of Internal Capital Markets Reallocation

  This post concludes a three-part series on how bank regulation interacts with the organizational structure of banking firms. The first post documented the equity-rich nonbank subsidiaries inside bank holding companies (BHCs); the second post showed that BHCs met Basel III by reallocating capital internally, moving equity from nonbank affiliates to bank subsidiaries rather tha...

• 2.How Basel III Changes Where Capital Sits: Nonbank Subsidiaries as Equity Reservoirs

  This post is the second in a three-part series on how bank regulation interacts with the organizational structure of banking firms. The first post documented that nonbank subsidiaries inside bank holding companies (BHCs) are large, equity-rich "reservoirs," and that bank-level capital diverged sharply from consolidated capital after Basel III took effect in 2015. This post asks why, and traces the answer through the internal plumbing of the holding company. The series draws on the authors' recent Staff Report, "

• 3.Capitalizing on Nonbanks: Regulatory Arbitrage Within Bank Holding Companies

  This post is the first in a three-part series on how bank regulation interacts with the organizational structure of banking firms. The series draws on the authors' recent Staff Report, "Regulatory Arbitrage Within the Firm."

• 4.Effect of Tariffs on U.S. Small Businesses

  How has the recent implementation of tariffs affected small businesses? Due to lack of data, little is known about this issue. In this Liberty Street Economics post, we use data from the 2025 edition of the Small Business Credit Survey (SBCS) to explore this question for businesses nationally and in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut). We find that the majority of national firms in the goods and retail sectors reported experiencing financial challenges due to tariffs in 2025, with even larger shares of regional firms doing so. In response, about 80 percent of national and regional firms passed on at least some of the higher ...

• 5.More Tariff Pass‑Through Is in the Pipeline

  The past year brought dramatic changes to U.S. trade policy, including sweeping new tariffs, as well as a Supreme Court decision that further reshaped the tariff landscape. Many businesses saw their costs increase significantly and faced complex decisions about whether to absorb the tariffs through lower profit margins, raise their prices to recover the higher costs, or some combination of the two. Last year, we found that most businesses had passed on at least some of these higher costs to their customers throug...

• 1.Social-Media Age Bans Won’t Make Children Safer—But This Will

  As the European Union considers how to protect and empower young people in a digital world, it must resist the urge to enact a ban on social media for young people. A more effective strategy would force tech companies to remove addictive features by holding them to product-liability standards—as with most other industries.

• 2.Who Will Sit Atop the Next World Order?

  American hegemony has given way to a global order based on rivalry and competition among several major powers, where there is no longer a fixed, codified set of rules. Instead, there is only economic, technological, and military power—the likes of which China is amassing faster than many observers realize.

• 3.AI Will Supercharge Surveillance Capitalism

  For too long, governments have stood by and allowed the creation of business models built on the commercial exploitation of users' personal data and that reward social and psychological manipulation. With the AI industry poised to replicate this model, policymakers must intervene before it is too late.

• 4.An Alternative History for America at 250

  Commemorating the 250th anniversary of the United States requires an honest appraisal of not only its founding principles but also its racist and imperialist legacy. A clear-eyed assessment shows why many people in the Global South remain cynical about the country’s ability to live up to its ideals.

• 5.A Turning Point for African Industrialization

  Building on the success of his mega-refinery in Lagos, Nigerian industrialist Aliko Dangote is in talks to construct a second one in collaboration with several East African countries. African leaders should learn from Dangote’s strategy of adding value to the continent’s natural resources and fostering cross-border cooperation.

• 1.From experiment to expectation: BAI on delivering private 5G across Australian industry

  Q&A with Chris Upstone, Director of Private and Public Networks, BAI Communications Private 5G networks are rapidly moving from experiment to expectation across Australian industry. As deployments grow in scale…

• 2.When the push becomes the pull – private 5G and physical AI

  Private 5G was once sold as telecom’s answer to industrial connectivity. Now physical AI, covering robotics and automation, is changing the dynamic: industrial enterprises – and their technology providers –…

• 3.Elisa targets data center growth through fiber connectivity

  Elisa CEO Topi Manner said the quarter marked the beginning of the company’s large-scale data center connectivity business In sum – what to know: First deals – Elisa signed its…

• 4.Subsea shift – ITU and FCC redraw rules for global cable resilience

  New ITU recommendations and FCC regulations show how submarine cables are being recast as critical infrastructure. While the ITU targets deployment and repair bottlenecks, the FCC is tightening oversight of…

• 5.Red Hat outlines AI-RAN roadmap

  Speaking during RCR Wireless News’ Telco AI Forum, Red Hat’s Shujaur Mufti said operators are initially focusing on AI for RAN because it delivers measurable operational benefits without requiring major…

• 1.Physics-informed machine learning

  Abstract not available.

• 2.Machine Learning: Algorithms, Real-World Applications and Research Directions

  In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intellig...

• 3.Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

  We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL

• 4.A Survey on Bias and Fairness in Machine Learning

  With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various...

• 5.Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead

  Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several...

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