A former Israeli soldier has created a video game based on the Gaza war, which he says aims to'humanise' Israeli troops. Scenes from the game's promo video depict the destruction in Gaza, which rights groups say Israeli soldiers already treat as if it were a video game. Israel wants to'destroy Gaza City, not occupy it'

What does survival look like inside Gaza City? 'How to stop Israel from starving Gaza' Israel wants to'destroy Gaza City, not occupy it' NewsFeed Israel wants to'destroy Gaza City, not occupy it' The level of destruction happening in Gaza City suggests Israel's goal is not to occupy it but to destroy it completely, says this Palestinian analyst.

What does survival look like inside Gaza City? 'How to stop Israel from starving Gaza' Video shows the moment an Israeli drone laden with explosives was flown into the minaret of a mosque in Deir el-Balah, central Gaza. Israel wants to'destroy Gaza City, not occupy it'

Research into whether drugs like ayahuasca can mitigate the effects of traumatic brain injury is in its infancy. Pro athletes like the Buffalo Bills' Jordan Poyer are forging ahead anyway. Roam the wide-open halls and cavernous showrooms of the Colorado Convention Center during Psychedelic Science, the world's largest psychedelics conference, and you'll see exhibitors hawking everything from mushroom jewelry, to chewable gummies containing extracts of the psychoactive succulent plant kanna, to broad flat-brim baseball caps emblazoned with "MDMA" and "IBOGA." Booths publicize organizations such as the Ketamine Taskforce and the Psychedelic Parenthood Community, and even, a live-action feature film looking to attract investors. It's a motley, multifarious symposium where indigenous-plant-medicine healers mingle with lanyard-clad pharma-bros, legendary underground LSD chemists, and workaday stoners tottering around in massive red and white toadstool hats that make them look like that cute little mushroom guy from . And yet, oddest among such oddities may be the sight of enormously burly NFL tough guys talking candidly about their feelings.

Colombia has been submerged in decades of armed conflict, yet until recently, the systematic documentation of violence was not a priority for the Colombian government. This has resulted in a lack of publicly available conflict information and, consequently, a lack of historical accounts. This study contributes to Colombia's historical memory by utilizing GPT, a large language model (LLM), to read and answer questions about over 200,000 violence-related newspaper articles in Spanish. We use the resulting dataset to conduct both descriptive analysis and a study of the relationship between violence and the eradication of coca crops, offering an example of policy analyses that such data can support. Our study demonstrates how LLMs have opened new research opportunities by enabling examinations of large text corpora at a previously infeasible depth.

Counting mitotic figures is time-intensive for pathologists and leads to inter-observer variability. Artificial intelligence (AI) promises a solution by automatically detecting mitotic figures while maintaining decision consistency. However, AI tools are susceptible to domain shift, where a significant drop in performance can occur due to differences in the training and testing sets, including morphological diversity between organs, species, and variations in staining protocols. Furthermore, the number of mitoses is much less than the count of normal nuclei, which introduces severely imbalanced data for the detection task. In this work, we formulate mitosis detection as a pixel-level segmentation and propose a teacher-student model that simultaneously addresses mitosis detection (Track 1) and atypical mitosis classification (Track 2). Our method is based on a UNet segmentation backbone that integrates domain generalization modules, namely contrastive representation learning and domain-adversarial training. A teacher-student strategy is employed to generate pixel-level pseudo-masks not only for annotated mitoses and hard negatives but also for normal nuclei, thereby enhancing feature discrimination and improving robustness against domain shift. For the classification task, we introduce a multi-scale CNN classifier that leverages feature maps from the segmentation model within a multi-task learning paradigm. On the preliminary test set, the algorithm achieved an F1 score of 0.7660 in Track 1 and balanced accuracy of 0.8414 in Track 2, demonstrating the effectiveness of integrating segmentation-based detection and classification into a unified framework for robust mitosis analysis.

To avoid the featuri zation bottleneck of traditional descriptors, we also leverage GNNs to generate fast, latent-space approximations of MatMiner (ℓ-MM) and Orbital Field Matrix (ℓ-OFM) features. Finally, we augment this feature set with new descriptors derived via symbolic regression. This multifac eted strategy aims to create a more robust, accurate, and versatile featurizer that capitalizes on the distinct strengths of each approach to be useful for a wider range of dataset sizes. To simplify the generation of all those features, a package was developed named MatterVial standing for MATerials fea T uR e E xtraction Via I nterpretable Artificial L earning, which, besides producing all latent-space features from the GNN models, aids i n obtaining the interpretable chemical descriptors that correlate to these high-level features. This is achieved through techniques such as SHapley Additive exPlanations (SHAP) analysi s in surrogate models and symbolic regression via Sure Independence Screening and Sparsifying Operator (SISSO) to obtain an approximate formula from the most important features. Our re sults demonstrate an overall improvement in all analyzed datasets compare d with the baseline MatMiner featurizer. In addition, it surpassed the performance of the individua l GNN models in several cases, indicating that the combination of traditional and l atent-space features leads to a more robust generalization.

An AI company is to reconstruct the missing portions of Orson Welles' legendary mutilated masterwork The Magnificent Ambersons, it has been announced. According to the Hollywood Reporter, the Showrunner platform is planning to use its AI tools to assist in a recreation of the lost 43 minutes of Welles' 1942 film, removed and subsequently destroyed by Hollywood studio RKO. Edward Saatchi, CEO of interactive AI film-making studio Fable, which operates Showrunner, said in a statement to IndieWire: "We're starting with Orson Welles because he is the greatest storyteller of the last 200 years … So many people are rightly skeptical of AI's impact on cinema – but we hope that this gives people a sense of a positive contribution that AI can make for storytelling." Reports suggest that Showrunner is partnering with film-maker Brian Rose, who has been working since 2019 on an attempt to reconstruct the missing portions using animated sequences, as well as VFX expert Tom Clive. Welles started production in 1942 on The Magnificent Ambersons, an adaptation of Booth Tarkington's celebrated novel about a midwestern family in decline, as a follow-up to his Oscar-winning debut Citizen Kane.

The camera zooms too close to the president's face; the table at which the tech executives are seated seems far too long. Mark Zuckerberg is there, and Bill Gates and Tim Cook and Satya Nadella and Sam Altman and on and on, a baker's dozen or so of Silicon Valley's most powerful people--cutthroat competitors all--united here to pledge allegiance to Donald Trump. The introduction from Trump is characteristically both overgilded and confusing: "It's an honor to be here with this group of people. And then, about 90 seconds in, the pandering begins. This was Donald Trump's dinner with tech leaders at the State Dining Room in the White House on Thursday evening, broadcast in part for all to see on C-SPAN.

We investigate partially identifiable queries in a class of causal models. We focus on acyclic Structural Causal Models that are quasi-Markovian (that is, each endogenous variable is connected with at most one exogenous confounder). We look into scenarios where endogenous variables are observed (and a distribution over them is known), while exogenous variables are not fully specified. This leads to a representation that is in essence a Bayesian network where the distribution of root variables is not uniquely determined. In such circumstances, it may not be possible to precisely compute a probability value of interest. We thus study the computation of tight probability bounds, a problem that has been solved by multilinear programming in general, and by linear programming when a single confounded component is intervened upon. We present a new algorithm to simplify the construction of such programs by exploiting input probabilities over endogenous variables. For scenarios with a single intervention, we apply column generation to compute a probability bound through a sequence of auxiliary linear integer programs, thus showing that a representation with polynomial cardinality for exogenous variables is possible. Experiments show column generation techniques to be superior to existing methods.