A.1 Additional Experiments on CIFAR102 We expanded our experiments on the CIFAR10 dataset by utilizing weights pretrained for 1003 iterations with a batch size of 128 per iteration. The CIFAR10 dataset consists of 50,000 training4 images and 10,000 testing images, divided into 10 different classes. The results of these experiments5 are summarized in Table 1.6 We observed performance improvement relative to baseline. However, compared to other modes of7 pretraining for CIFAR10, certain PaI generators exhibited higher-than-expected standard deviation and8 lower average performance, indicating some instability in generating sparse structures. Specifically,9 we observed this trend with GraSP in ResNet18 and SNIP in ResNet34.10

Technology AI Texas Instruments' newest calculator is intentionally dumb The $160 device is not powered by AI, won't send annoying notifications, and can't connect to Wi-Fi. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The new TI-84 keeps the good old-fashioned physical buttons. Breakthroughs, discoveries, and DIY tips sent six days a week. In a world drowning in notifications and devices that want to be everything all at once, calculator giant Texas Instruments (TI) is going back to basics.

The details of multiple datasets for OIQA task are presented in Table A. For the dataset that contains scanpath coordinates, we can directly sample viewport sequences from it and use our network to predict the quality scores. However, it is challenging and costly to record user scanpath data for every ODI in realistic scenarios. The scanpath information is likely unavailable when evaluating the quality of a panorama. Therefore, we propose a generalized Recursive Probability Sampling (RPS) method to generate multiple pseudo viewport sequences for the panorama, which assists the network to predict an accurate quality score in a way that is similar to the observer's actual scoring process. In JUFE and JXUFE, each ODI consists of 300 viewport coordinates, recorded using a head-mounted display (HMD).

Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing advancement of virtual reality (VR) technology. However, the quality assessment of ODIs is severely hampered by the fact that the existing BOIQA pipeline lacks the modeling of the observer's browsing process. To tackle this issue, we propose a novel multi-sequence network for BOIQA called Assessor360, which is derived from the realistic multi-assessor ODI quality assessment procedure. Specifically, we propose a generalized Recursive Probability Sampling (RPS) method for the BOIQA task, combining content and details information to generate multiple pseudo viewport sequences from a given starting point.

Aleksandra Mendoza, aka Alorah Ziva, alleges that the 20-year-old influencer injected her with drugs on a livestream and had nonconsensual sex with her while she was underage. An 18-year-old woman who promotes herself as the "#1 female looksmaxxer" is suing the highly controversial streamer Braden Eric Peters, aka Clavicular, for fraud, battery, and alleged sexual assault. In the suit, which was filed in Miami-Dade County court and obtained by WIRED, Aleksandra Mendoza, who goes by the name @zahloria, or Alorah Ziva, on Instagram, alleges that she first encountered Peters in May 2025, when she was just 16 years old. According to the complaint, Peters promised Mendoza he could make her "the female face of looksmaxxing," the online trend of using surgery or drugs to enhance one's facial features. Eager to grow her social media following, Mendoza agreed to make four looksmaxxing videos for Peters in exchange for a $1,000 payment, court documents say.

A.1 Shower shape variables452 We extend the list of shower shape variables described in Sec. Marginals of each point feature by considering the set all the points from all454 the point clouds together.455 Layer Energy Ei. Denotes the total energy deposited in layer i of the shower. Total energy across all layers of the shower. The layer lateral widths can be interpreted as the spread around the center of energy in the lateral462 plane in respective dimensions.

Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard GEANT4 are computationally intensive, and current deep generative architectures work on discretized, lower resolution versions of the detailed simulation. The development of models that work at higher spatial resolutions is currently hindered by the complexity of the full simulation data, and by the lack of simpler, more interpretable benchmarks. Our contribution is SUPA, the SUrrogate PArticle propagation simulator, an algorithm and software package for generating data by simulating simplified particle propagation, scattering and shower development in matter. The generation is extremely fast and easy to use compared to GEANT4, but still exhibits the key characteristics and challenges of the detailed simulation. The proposed simulator generates thousands of particle showers per second on a desktop machine, a speed up of up to 6 orders of magnitudes over GEANT4, and stores detailed geometric information about the shower propagation. SUPA provides much greater flexibility for setting initial conditions and defining multiple benchmarks for the development of models. Moreover, interpreting particle showers as point clouds creates a connection to geometric machine learning and provides challenging and fundamentally new datasets for the field.

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb.

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments. In this work, we consider the natural question of how the interplay of models and strategic interactions affects the relationship between performance at equilibrium and the expressivity of model classes. We find that strategic interactions can break the conventional view--meaning that performance does not necessarily monotonically improve as model classes get larger or more expressive (even with infinite data). We show the implications of this result in several contexts including strategic regression, strategic classification, and multi-agent reinforcement learning. In particular, we show that each of these settings admits a Braess' paradox-like phenomenon in which optimizing over less expressive model classes allows one to achieve strictly better equilibrium outcomes. Motivated by these examples, we then propose a new paradigm for model selection in games wherein an agent seeks to choose amongst different model classes to use as their action set in a game.