Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the highdimensional setting. For data with inliers generated from a Gaussian with known covariance, we find experimentally that several robust mean estimation techniques can practically improve upon the sample mean, with the quantum entropy scaling approach from Dong et.al.

Deep Neural Networks (DNNs) have demonstrated strong capacity in supporting a wide variety of applications. Shapley value has emerged as a prominent tool to analyze feature importance to help people understand the inference process of deep neural models. Computing Shapley value function requires choosing a baseline to represent feature's missingness. However, existing random and conditional baselines could negatively influence the explanation. In this paper, by analyzing the suboptimality of different baselines, we identify the problematic baseline where the asymmetric interaction between $\bm{x}'_i$ (the replacement of the faithful influential feature) and other features has significant directional bias toward the model's output, and conclude that $p(y|\bm{x}'_i) = p(y)$ potentially minimizes the asymmetric interaction involving $\bm{x}'_i$. We further generalize the uninformativeness of $\bm{x}'_i$ toward the label space $L$ to avoid estimating $p(y)$ and design a simple uncertainty-based reweighting mechanism to accelerate the computation process. We conduct experiments on various NLP tasks and our quantitative analysis demonstrates the effectiveness of the proposed uncertainty-based reweighting mechanism. Furthermore, by measuring the consistency of explanations generated by explainable methods and human, we highlight the disparity between model inference and human understanding.

President Trump announces the U.S. Stargate investment alongside three artificial intelligence industry leaders. BREAKING GROUND: Stargate, the massive artificial intelligence (AI) infrastructure project recently unveiled by President Donald Trump, has begun production in Texas -- with data center construction in other states expected to be announced in the coming months. ON ONE CONDITION: Elon Musk will withdraw his unsolicited bid of 97.4 billion to take over OpenAI if its board of directors stops the company's conversion into a for-profit entity. EXISTENTIAL THREAT: OPINION: Our socioeconomic system is facing an existential threat from AI. In our capitalist society, most people depend on jobs to sustain themselves.

Multi-View Photometric Stereo (MVPS) is a popular method for fine-detailed 3D acquisition of an object from images. Despite its outstanding results on diverse material objects, a typical MVPS experimental setup requires a well-calibrated light source and a monocular camera installed on an immovable base. This restricts the use of MVPS on a movable platform, limiting us from taking MVPS benefits in 3D acquisition for mobile robotics applications. To this end, we introduce a new mobile robotic system for MVPS. While the proposed system brings advantages, it introduces additional algorithmic challenges. Addressing them, in this paper, we further propose an incremental approach for mobile robotic MVPS. Our approach leverages a supervised learning setup to predict per-view surface normal, object depth, and per-pixel uncertainty in model-predicted results. A refined depth map per view is obtained by solving an MVPS-driven optimization problem proposed in this paper. Later, we fuse the refined depth map while tracking the camera pose w.r.t the reference frame to recover globally consistent object 3D geometry. Experimental results show the advantages of our robotic system and algorithm, featuring the local high-frequency surface detail recovery with globally consistent object shape. Our work is beyond any MVPS system yet presented, providing encouraging results on objects with unknown reflectance properties using fewer frames without a tiring calibration and installation process, enabling computationally efficient robotic automation approach to photogrammetry. The proposed approach is nearly 100 times computationally faster than the state-of-the-art MVPS methods such as [1, 2] while maintaining the similar results when tested on subjects taken from the benchmark DiLiGenT MV dataset [3].

Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization. While this effectively densifies the matrix by assuming users and movies can be represented by linearly dependent latent features, it does not capture more complicated interactions. For example, vector representations struggle with set-theoretic relationships, such as negation and intersection, e.g. recommending a movie that is "comedy and action, but not romance". In this work, we formulate the problem of personalized item recommendation as matrix completion where rows are set-theoretically dependent. To capture this set-theoretic dependence we represent each user and attribute by a hyper-rectangle or box (i.e. a Cartesian product of intervals). Box embeddings can intuitively be understood as trainable Venn diagrams, and thus not only inherently represent similarity (via the Jaccard index), but also naturally and faithfully support arbitrary set-theoretic relationships. Queries involving set-theoretic constraints can be efficiently computed directly on the embedding space by performing geometric operations on the representations. We empirically demonstrate the superiority of box embeddings over vector-based neural methods on both simple and complex item recommendation queries by up to 30 \% overall.

In this paper, we introduce the Akan Conversation Emotion (ACE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of resources for low-resource languages in emotion recognition research. ACE, developed for the Akan language, contains 385 emotion-labeled dialogues and 6,162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. The presence of prosodic labels in this dataset also makes it the first prosodically annotated African language dataset. We demonstrate the quality and utility of ACE through experiments using state-of-the-art emotion recognition methods, establishing solid baselines for future research. We hope ACE inspires further work on inclusive, linguistically and culturally diverse NLP resources.

Elon Musk launched a 97.4 billion bid to take control of OpenAI. The Wall Street Journal reported a group of investors led by Musk's xAI submitted an unsolicited offer to the company's board of directors on Monday. The group wants to buy the nonprofit that controls OpenAI's for-profit arm. When asked for comment, an OpenAI spokesperson pointed Engadget to an X post from CEO Sam Altman. "No thank you but we will buy twitter for 9.74 billion if you want," Altman wrote on the social media platform Musk owns.

Just as there are cat people and dog people, there are cat filmmakers and dog filmmakers. Sean Baker, who brought his pup Bunsen to Cannes along with his Palme d'Or–winning Anora, is a dog filmmaker. That's not to say one can't appreciate both, whether we're talking movies or pets. I myself am a cat person who currently has two dogs. But I think that on some level you are either drawn primarily to the sly, withholding spirit of cat movies or the energetic emotionality of dog movies, and nothing can alter that fundamental orientation.

The Guardian Media Group, owner of The Guardian and The Observer newspapers, is partnering with OpenAI. The deal will see reporting from The Guardian appear as a news source within ChatGPT, alongside article extracts and short summaries. In return, OpenAI will provide the Guardian Media Group with access to ChatGPT Enterprise, which the company says it will use to develop new products, features and tools. "This new partnership with OpenAI reflects the intellectual property rights and value associated with our award-winning journalism, expanding our reach and impact to new audiences and innovative platform services," said Keith Underwood, chief financial and operating officer of the Guardian Media Group. The Guardian Media Group joins a growing list of news publishers that are now working with OpenAI after an initial period of uncertainty over the company and its business model.

AI Expert Marva Bailer explains to Fox News Digital Hank Azaria's opinion piece about humanity and AI matters. "The Simpsons" star Hank Azaria has voiced his fears over artificial intelligence in a new opinion piece. The actor, who has been with the show since 1989, wrote an opinion essay for The New York Times, worrying AI "will be able to recreate the sounds of the more than 100 voices I created for characters on'The Simpsons.'" He continued, "It makes me sad to think about it. Not to mention, it seems just plain wrong to steal my likeness or sound -- or anyone else's."