Interstellar Arc is an immersive sci-fi experience that uses recent advances in headset tech to break new ground in virtual reality. And it all happens inside an empty-looking room in Las Vegas. It feels like I've been transported into a scene straight out of a science fiction movie. I'm walking around on a giant centrifuge in space, which I can see the outlines of at the edge of my vision. Beyond it, I see the planet we're orbiting. The pathways I walk on stretch endlessly above and below me, giving me the feeling I'm in an absolutely massive structure.

High-fidelity gaze redirection is critical for generating augmented data to improve the generalization of gaze estimators. 3D Gaussian Splatting (3DGS) models like GazeGaussian represent the state-of-the-art but can struggle with rendering subtle, continuous gaze shifts. In this paper, we propose DiT-Gaze, a framework that enhances 3D gaze redirection models using a novel combination of Diffusion Transformer (DiT), weak supervision across gaze angles, and an orthogonality constraint loss. DiT allows higher-fidelity image synthesis, while our weak supervision strategy using synthetically generated intermediate gaze angles provides a smooth manifold of gaze directions during training. The orthogonality constraint loss mathematically enforces the disentanglement of internal representations for gaze, head pose, and expression. Comprehensive experiments show that DiT-Gaze sets a new state-of-the-art in both perceptual quality and redirection accuracy, reducing the state-of-the-art gaze error by 4.1% to 6.353 degrees, providing a superior method for creating synthetic training data. Our code and models will be made available for the research community to benchmark against.

Large Language Model (LLM) unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent misuse. However, recent studies highlight its limited efficacy in real-world scenarios, hindering practical adoption. In this study, we identify a pervasive issue underlying many downstream failures: the effectiveness of existing unlearning methods heavily depends on the form of training samples and frequently fails to generalize to alternate expressions of the same knowledge. We formally characterize this problem as Form-Dependent Bias and systematically investigate its specific manifestation patterns across various downstream tasks. To quantify its prevalence and support future research, we introduce ORT, a novel benchmark designed to evaluate the robustness of unlearning methods against variations in knowledge expression. Results reveal that Form-Dependent Bias is both widespread and severe among current techniques. We argue that LLM unlearning should be form-independent to address the endless forms of downstream tasks encountered in real-world security-critical scenarios. Towards this goal, we introduce Rank-one Concept Redirection (ROCR), a novel training-free method, as a promising solution path. ROCR performs unlearning by targeting the invariants in downstream tasks, specifically the activated dangerous concepts. It is capable of modifying model parameters within seconds to redirect the model's perception of a specific unlearning target concept to another harmless concept. Extensive experiments demonstrate that ROCR significantly improves unlearning effectiveness compared to traditional methods while generating highly natural outputs.

Optimization remains a fundamental pillar of machine learning, yet existing methods often struggle to maintain stability and adaptability in dynamic, non linear systems, especially under uncertainty. We introduce AERO (Adversarial Energy-based Redirection Optimization), a novel framework inspired by the redirection principle in Judo, where external disturbances are leveraged rather than resisted. AERO reimagines optimization as a redirection process guided by 15 interrelated axioms encompassing adversarial correction, energy conservation, and disturbance-aware learning. By projecting gradients, integrating uncertainty driven dynamics, and managing learning energy, AERO offers a principled approach to stable and robust model updates. Applied to probabilistic solar energy forecasting, AERO demonstrates substantial gains in predictive accuracy, reliability, and adaptability, especially in noisy and uncertain environments. Our findings highlight AERO as a compelling new direction in the theoretical and practical landscape of optimization.

Machine unlearning is studied for a multitude of tasks, but specialization of unlearning methods to particular tasks has made their systematic comparison challenging. To address this issue, we propose a conceptual space to characterize diverse corrupted data unlearning tasks in vision classifiers. This space is described by two dimensions, the discovery rate (the fraction of the corrupted data that are known at unlearning time) and the statistical regularity of the corrupted data (from random exemplars to shared concepts). Methods proposed previously have been targeted at portions of this space and-we show-fail predictably outside these regions. We propose a novel method, Redirection for Erasing Memory (REM), whose key feature is that corrupted data are redirected to dedicated neurons introduced at unlearning time and then discarded or deactivated to suppress the influence of corrupted data. REM performs strongly across the space of tasks, in contrast to prior SOTA methods that fail outside the regions for which they were designed.

Mental-health therapy involves a complex conversation flow in which patients and therapists continuously negotiate what should be talked about next. For example, therapists might try to shift the conversation's direction to keep the therapeutic process on track and avoid stagnation, or patients might push the discussion towards issues they want to focus on. How do such patient and therapist redirections relate to the development and quality of their relationship? To answer this question, we introduce a probabilistic measure of the extent to which a certain utterance immediately redirects the flow of the conversation, accounting for both the intention and the actual realization of such a change. We apply this new measure to characterize the development of patienttherapist relationships over multiple sessions in a very large, widely-used online therapy platform. Our analysis reveals that (1) patient control of the conversation's direction generally increases relative to that of the therapist as their relationship progresses; and (2) patients who Figure 1: Top: Examples of attempted redirection, both have less control in the first few sessions are realized (left) and unrealized (right). Bottom: Example significantly more likely to eventually express where redirection is not attempted.

This page is a compilation of common mistakes made by bash users. Each example is flawed in some way. Yes, it would be great if you could just treat the output of ls or find as a list of filenames and iterate over it. This entire approach is fatally flawed, and there is no trick that can make it work. You must use an entirely different approach. If a filename contains whitespace, it undergoes WordSplitting. Assuming we have a file named 01 - Don't Eat the Yellow Snow.mp3 in the current directory, the for loop will iterate over each word in the resulting file name: 01, -, Don't, Eat, etc. If a filename contains glob characters, it undergoes filename expansion ("globbing"). If ls produces any output containing a * character, the word containing it will become recognized as a pattern and substituted with a list of all filenames that match it. If the command substitution returns multiple filenames, there is no way to tell where the first one ends and the second one begins. Pathnames may contain any character except NUL. Depending on which platform you're on, which arguments you used (or didn't use), and whether its standard output is pointing to a terminal or not, ls may randomly decide to replace certain characters in a filename with "?", or simply not print them at all. Never try to parse the output of ls. It's an external command whose output is intended specifically to be read by a human, not parsed by a script. That may seem desirable since ls adds a newline, but if the last filename in the list ends with a newline, ... or $() will remove that one also. In the ls examples, if the first filename starts with a hyphen, it may lead to pitfall #3. This causes the entire output of ls to be treated as a single word. Instead of iterating over each file name, the loop will only execute once, assigning to f a string with all the filenames rammed together. Nor can you simply change IFS to a newline.

Damasio, Picard, and others have misinterpreted that too little emotion also can Likewise, it is now commonplace the evidence about brain wreak havoc (p.