AI

Opening — Why this matters now Stock prediction papers arrive with clockwork regularity, each promising to tame volatility with yet another hybrid architecture. Most quietly disappear after publication. A few linger—usually because they claim eye‑catching accuracy. This paper belongs to that second category, proposing a Neural Prophet + Deep Neural Network (NP‑DNN) stack that reportedly delivers over 93%–99% accuracy in stock market prediction. ...

Opening — Why this matters now Circuit simulation has always been an exercise in controlled compromise. We discretize time, linearize nonlinearity, and hope the numerical solver behaves. SPICE has done this extraordinarily well for decades—but it was built for an era where devices were mostly electrical, mostly local, and mostly cooperative. That era is ending. Ferroelectrics, photonics, thermal coupling in 3D ICs, and other strongly nonlinear or multi-physics effects are turning compact modeling into a brittle art. Against this backdrop, NeuroSPICE proposes something mildly heretical: stop stepping through time altogether. ...

Opening — Why this matters now AI has become remarkably good at reading emotions—just not the kind that actually matter in classrooms. Most sentiment models are trained on people being honest with their feelings: tweets, movie reviews, reaction videos. Teachers, unfortunately for the models, are professionals. They perform. They regulate. They smile through frustration and project enthusiasm on command. As a result, generic sentiment analysis treats classrooms as emotionally flat—or worse, mislabels them entirely. ...

Opening — Why this matters now Large Language Models are increasingly invited into optimization workflows. They write solvers, generate heuristics, and occasionally bluff their way through mathematical reasoning. But a more uncomfortable question has remained largely unanswered: do LLMs actually understand optimization problems—or are they just eloquent impostors? This paper tackles that question head‑on. Instead of judging LLMs by what they say, it examines what they encode. And the results are quietly provocative. ...

Opening — Why this matters now The ocean economy has quietly become one of the world’s fastest‑growing industrial frontiers. Oil and gas rigs, offshore wind farms, and artificial islands now populate the seas like metallic archipelagos. Yet, despite their scale and significance, much of this infrastructure remains poorly monitored. Governments and corporations rely on fragmented reports and outdated maps—while satellites see everything, but few know how to interpret the data. ...

Can machines grasp the shades of human sentiment? Traditional opinion-mining systems often fail when language becomes ambiguous — when a review says, “The battery life is okay but could be better,” is that positive or negative? The paper “Opinion Mining Based Entity Ranking using Fuzzy Logic Algorithmic Approach” (Kalamkar & Phakatkar, 2014) offers a compelling answer: use fuzzy logic to interpret the degree of sentiment, not just its direction. At its heart, this study bridges two previously separate efforts: fuzzy-based sentiment granularity (Samaneh Nadali, 2010) and opinion-based entity ranking (Ganesan & Zhai, 2012). The innovation lies in combining fuzzy logic reasoning with conditional random fields (CRFs) to classify reviews at multiple levels of sentiment intensity, then ranking entities accordingly. In essence, it transforms vague human opinions into structured data without flattening their complexity. ...

In the world of quantitative investing, the line between data and story has long been clear. Numbers ruled the models; narratives belonged to the analysts. But the recent paper “Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction” from RAM Active Investments argues that this divide is no longer useful—or profitable. Beyond Factors: Why Text Matters Quantitative factors—valuation, momentum, profitability—are the pillars of systematic investing. They measure what can be counted. But markets move on what’s talked about, too. Corporate press releases, analyst notes, executive reshuffles—all carry signals that often precede price action. Historically, this qualitative layer was hard to quantify. Now, LLMs can translate the market’s chatter into vectors of meaning. ...

TL;DR Neuro‑symbolic architecture (LLMs + Prolog) turns tax calculation from vibes to verifiable logic. The paper we analyze shows that adding a symbolic solver, selective refusal, and exemplar‑guided parsing can lower the break‑even cost of an AI tax assistant to a fraction of average U.S. filing costs. Even more interesting: chat‑tuned models often beat reasoning‑tuned models at few‑shot translation into logic — a counterintuitive result with big product implications. Why this matters for operators (not just researchers) Most back‑office finance work is a chain of (1) rules lookup, (2) calculations, and (3) audit trails. Generic LLMs are great at (1), decent at (2), and historically bad at (3). This work shows a practical path to auditable automation: translate rules and facts into Prolog, compute with a trusted engine, and price the risk of being wrong directly into your product economics. ...

The dream of generating a fully realized, minute-long video from a short text prompt has always run aground on three reefs: disjointed narratives, visual glitches, and characters that morph inexplicably between shots. MAViS (Multi-Agent framework for long-sequence Video Storytelling) takes aim at all three by treating video creation not as a single monolithic AI task, but as a disciplined production pipeline staffed by specialized AI “crew members.” The Problem with One-Shot Generators Single-pass text-to-video systems shine in short clips but crumble under the demands of long-form storytelling. They repeat motions, lose scene continuity, and often rely on users to do the heavy lifting—writing scripts, designing shots, and manually training models for character consistency. This is not just a technical shortcoming; it’s a workflow bottleneck that makes creative scaling impossible. ...

Most warnings about algorithmic collusion tell the same story: sellers using AI to set prices end up coordinating—without explicit communication—to keep prices higher than competition would allow. This is what regulators fear: supra-competitive prices, reduced consumer welfare, and harder-to-detect anti-competitive behavior. A new study, however, flips the narrative on its head. By analyzing multi-dimensional decision-making—where reinforcement learning (RL) agents set both prices and advertising bids on a platform like Amazon—the authors uncover a surprising outcome: in markets with high consumer search costs, algorithmic “collusion” can lower prices below competitive benchmarks. ...