Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for comparing selection strategies -- a gap intensified by large language models (LLMs), which generate plausible scientific proposals without reliable downstream evaluation. We introduce the Budget-Sensitive Discovery Score (BSDS), a formally verified metric -- 20 theorems machine-checked by the Lean 4 proof assistant -- that jointly penalizes false discoveries (lambda-weighted FDR) and excessive abstention (gamma-weighted coverage gap) at each budget level. Its budget-averaged form, the Discovery Quality Score (DQS), provides a single summary statistic that no proposer can inflate by performing well at a cherry-picked budget. As a case study, we apply BSDS/DQS to: do LLMs add marginal value to an existing ML pipeline for drug discovery candidate selection? We evaluate 39 proposers -- 11 mechanistic variants, 14 zero-shot LLM configurations, and 14 few-shot LLM configurations -- using SMILES representations on MoleculeNet HIV (41,127 compounds, 3.5% active, 1,000 bootstrap replicates) under both random and scaffold splits. Three findings emerge. First, the simple RF-based Greedy-ML proposer achieves the best DQS (-0.046), outperforming all MLP variants and LLM configurations. Second, no LLM surpasses the Greedy-ML baseline under zero-shot or few-shot evaluation on HIV or Tox21, establishing that LLMs provide no marginal value over an existing trained classifier. Third, the proposer hierarchy generalizes across five MoleculeNet benchmarks spanning 0.18%-46.2% prevalence, a non-drug AV safety domain, and a 9x7 grid of penalty parameters (tau >= 0.636, mean tau = 0.863). The framework applies to any setting where candidates are selected under budget constraints and asymmetric error costs.

Cyberbullying on social media is inherently multilingual and multi-faceted, where abusive behaviors often overlap across multiple categories. Existing methods are commonly limited by monolingual assumptions or single-task formulations, which restrict their effectiveness in realistic multilingual and multi-label scenarios. In this paper, we propose HMS-BERT, a hybrid multi-task self-training framework for multilingual and multi-label cyberbullying detection. Built upon a pretrained multilingual BERT backbone, HMS-BERT integrates contextual representations with handcrafted linguistic features and jointly optimizes a fine-grained multi-label abuse classification task and a three-class main classification task. To address labeled data scarcity in low-resource languages, an iterative self-training strategy with confidence-based pseudo-labeling is introduced to facilitate cross-lingual knowledge transfer. Experiments on four public datasets demonstrate that HMS-BERT achieves strong performance, attaining a macro F1-score of up to 0.9847 on the multi-label task and an accuracy of 0.6775 on the main classification task. Ablation studies further verify the effectiveness of the proposed components.

Practitioners monitoring deployed probabilistic models face a fundamental trap: any fixed-sample test applied repeatedly over an unbounded stream will eventually raise a false alarm, even when the model remains perfectly stable. Existing methods typically lack formal error guarantees, conflate alarm time with changepoint location, and monitor indirect signals that do not fully characterize calibration. We present PITMonitor, an anytime-valid calibration-specific monitor that detects distributional shifts in probability integral transforms via a mixture e-process, providing Type I error control over an unbounded monitoring horizon as well as Bayesian changepoint estimation. On river's FriedmanDrift benchmark, PITMonitor achieves detection rates competitive with the strongest baselines across all three scenarios, although detection delay is substantially longer under local drift.

The InfoNCE loss in contrastive learning depends critically on a temperature parameter, yet its dynamics under fixed versus annealed schedules remain poorly understood. We provide a theoretical analysis by modeling embedding evolution under Langevin dynamics on a compact Riemannian manifold. Under mild smoothness and energy-barrier assumptions, we show that classical simulated annealing guarantees extend to this setting: slow logarithmic inverse-temperature schedules ensure convergence in probability to a set of globally optimal representations, while faster schedules risk becoming trapped in suboptimal minima. Our results establish a link between contrastive learning and simulated annealing, providing a principled basis for understanding and tuning temperature schedules.

In this paper, we consider the problem of black-box optimization with noisy feedback revealed in batches, where the unknown function to optimize has a bounded norm in some Reproducing Kernel Hilbert Space (RKHS). We refer to this as the Batched Kernelized Bandits problem, and refine and extend existing results on regret bounds. For algorithmic upper bounds, (Li and Scarlett, 2022) shows that $B=O(\log\log T)$ batches suffice to attain near-optimal regret, where $T$ is the time horizon and $B$ is the number of batches. We further refine this by (i) finding the optimal number of batches including constant factors (to within $1+o(1)$), and (ii) removing a factor of $B$ in the regret bound. For algorithm-independent lower bounds, noticing that existing results only apply when the batch sizes are fixed in advance, we present novel lower bounds when the batch sizes are chosen adaptively, and show that adaptive batches have essentially same minimax regret scaling as fixed batches. Furthermore, we consider a robust setting where the goal is to choose points for which the function value remains high even after an adversarial perturbation. We present the robust-BPE algorithm, and show that a suitably-defined cumulative regret notion incurs the same bound as the non-robust setting, and derive a simple regret bound significantly below that of previous work.

Training large-scale image recognition models is computationally expensive. This raises the question of whether there might be simple ways to improve the test performance of an already trained model without having to re-train or fine-tune it with new data. Here, we show that, surprisingly, this is indeed possible. The key observation we make is that the layers of a deep network close to the output layer contain independent, easily extractable class-relevant information that is not contained in the output layer itself. We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time.

Neural Information Processing Systems http://nips.cc/

Secret of the booming Florida county desired by families that's seeing influx of residents Conniving couple whose greedy'pervert' plot'drove innocent disabled man to suicide' given stunningly short sentences I've FINALLY found something that works on my rosacea: This new anti-ageing treatment beloved of celebrities really is a game-changer Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN Mass cancellations as Southwest Airlines pulls out of two of America's biggest airports Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite We were the picture-perfect family of faith. The evil of sex was drilled into me... then finally I gave in to every depraved urge Trump says he's not ready to make a deal with Iran despite them asking for a ceasefire: 'The terms aren't good enough yet!' JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Kim Jong Un is joined by his heir-apparent teenage daughter for'invasion rehearsal' drills Obama Center asks for 100 unpaid volunteers despite hiring the former president's'close friend' as CEO on $740K Insane moment NYC cab plows into pedestrians... and the miracle that saved them from death Angelina Jolie and George Clooney lead stars who have fled the US for France as Paris is dubbed'Frollywood' Harry Styles kisses male SNL star in wild opening monologue as he confronts woke'queerbaiting' claims head on Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare America's most expensive ZIP codes revealed - and the surprising state that dominates the list I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. Immature men are to blame for Britain's'missing babies', report warns - because they delay responsibilities until later in life Britain's'missing babies' can be blamed on immature men who are delaying responsibilities until later in life, according to a report. Research from the Centre for Social Justice (CSJ) think tank predicts around 600,000 young women may miss out on motherhood partly because men don't feel ready for children until they get older. In the report, called Baby Bust, the organisation says there are a range of reasons for falling birth rates including the cost of childcare, wanting to move into a larger house, prioritising a career or not finding the right partner.

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Sharon Osbourne tells Hollywood elites to save politics'for your platform' when accepting awards Zach Braff shuts down rumors he has an AI chatbot girlfriend: 'Please update all gossip sites' 'Yellowstone' star Luke Grimes targeted by Montana locals as move from LA sparks small-town fury Kurt Russell reveals why he was'glad' when son Wyatt turned to acting after hockey career Morrissey cancels concert after festival noise leaves him in'catatonic state' from sleep deprivation Gina Gershon reveals'creepy' encounter with man who later murdered Playboy Playmate Dorothy Stratten Walker Hayes says kids had'front-row seat' to struggles, including'alcoholic dad' Last person to see JFK Jr. alive reveals chilling premonition about his fatal flight that night'Mormon Wives' star weighs just 99 pounds at 5' 10, admits GLP-1 addiction'The Madison' cast praises Michelle Pfeiffer, Kurt Russell, says co-stars are'best of the best' Michelle Pfeiffer calls Kurt Russell team-up long overdue in'The Madison' Korie Robertson says Willie's clean-shaven 20s were his'rebellious' phase Willie Robertson says he'never would have dreamed' of joining family business in his 20s Aviation expert explains JFK Jr.'s final flight risks'The Madison' star Beau Garrett admits nerves before working with Michelle Pfeiffer Donny Osmond says singing with AI-generated 14-year-old self'never gets old' Inside the party that changed Prince William, Kate Middleton's future Jack Wagner says he would'totally' compete on'The Masked Singer' again Rihanna's Beverly Hills home targeted by gunfire; woman arrested as dispatch audio reveals chilling details Oscars Amanda Seyfried questions Oscars' importance as expert likens awards show to'the tire industry' Oscars'aren't what they used to be' because they're out of touch with audiences: expert Fox News contributor Raymond Arroyo explains why the Oscars are growing disconnected from audiences when they ignore popular films.

Conniving couple whose greedy'pervert' plot'drove innocent disabled man to suicide' given stunningly short sentences Obama Center asks for 100 unpaid volunteers despite hiring the former president's'close friend' as CEO on $740K Harry Styles shares gay kiss with SNL star in wild opening monologue as he addresses'queerbaiting' claims Housing nightmare in America's'best state to buy a home' as banks suddenly seize thousands of properties Insufferable blowhard Stephen Colbert is being taken out like the trash... and thank God! What he's done is so diabolical: MAUREEN CALLAHAN JFK Jr's mortifying night of phone sex... day Sarah Jessica Parker ditched her underwear to seduce him in public... and the girlfriend he REALLY wanted to marry: All the women before Carolyn Mass cancellations as Southwest Airlines pulls out of two of America's biggest airports Truth about'super secretive' Michael B. Jordan's love life... and real reason he is perpetually single: Years of private'heartache' and'loneliness' laid bare Beloved young dad and inspiring female'Air Force superstar' among US heroes killed in Iran mission crash as all six are named Insane moment NYC cab plows into pedestrians... and the miracle that saved them from death Caitlin Clark goes viral for bizarre behavior after Team USA's win over Italy: 'What are you doing? We fled Trump to chase the REAL American dream in the most idyllic European hotspot... here's why we're coming back to a red state I looked like a monster after a car accident burned off my face... but a pioneering face transplant gave me my life back. Furious flower farm owner blasts'feral' customers after they trampled tulips to get perfect photos Harry and Meghan hit back at new book claims she was accused by Camilla of'brainwashing' him - dismissing accusations as'deranged conspiracy' Extramarital sex with witches, cursed bloodlines and possessed politicians: DC's chief exorcist reveals the potent stench of evil among America's elite Iran's deadly drone arsenal is a'wake-up call for America': Expert warns US defenses may be unprepared for swarm attacks Iran's foreign minister admits Islamic Republic is receiving military support from Russia and China Hollywood costume designer names VILE A-Listers including pervert James Bond star, slob female sitcom icon... and details the hilarious evil of Shannen Doherty Scampi and chips could soon be off the menu! Woke scientists call for Brits to ditch the seaside staple - amid fears it carries a'hidden climate cost' Woke scientists are calling for Brits to take scampi and chips off the menu, amid fears that the seaside staple carries a'hidden climate cost'. Scampi is a traditional chip shop treat made from the breaded and fried tails of Norway lobsters, also known as langoustines.