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CoSeP: Complementary Separability Pruning via Class-Separability Clustering
arXiv:2505.13225v2 Announce Type: replace Abstract: Neural network pruning aims to compress models for efficient deployment, yet two fundamental challenges remain. First, many methods rely on per-component importance scores, selecting filters or neurons independently and ignoring redundancy: the retained set may include multiple components capturing similar discriminative patterns while missing others entirely.
Strengthening Polymorphic Prompt Assembling: Dynamic Separator Generation Against Emerging Prompt Injection Attacks
arXiv:2605.30534v1 Announce Type: new Abstract: Polymorphic Prompt Assembling (PPA) defends LLM agents against prompt injections by randomly selecting separator pairs from a fixed pool to isolate user input from system instructions. Although effective, static pool reuse exposes a blast-radius vulnerability: once a separator leaks, it can be exploited in future requests. We propose a dynamic per-request separator generation using domain-separated SHA-256 digests keyed on the timestamp,...
Boy, 11, reunited with mom after year of heartbreak — but fears another separation
Boy, 11, reunited with mom after year of heartbreak — but fears another separation An Associated Press investigation reveals that dozens of children who were separated under the first Trump administration have been re-separated, despite a judge's order to reunite them - Bookmark Eleven-year-old Ederson Galicia Alva had just stepped off the plane and into the Miami airport’s dim hallways when federal agents pulled his mother aside for questioning. A familiar panic surged. His excitement at...
Separation Power of Equivariant Neural Networks
arXiv:2406.08966v3 Announce Type: replace Abstract: The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing the separation power of a family of models is a necessary condition to obtain fine-grained universality results. In this paper, we analyze the separation power of equivariant neural networks, such as convolutional and permutation-invariant networks.
Couple separated, lived 15 years apart: SC says amounts to cruelty to both
The Supreme Court has directed that a prolonged period of separation between spouses, with the absence of any genuine effort to rebuild the marriage, can amount to mental cruelty and it becomes a valid ground for divorce under the Hindu Marriage Act. The judgment came in a case where the couple, both doctors in government service had been living separately for over 15 years after a marriage that lasted barely two to three months of actual cohabitation. The Supreme Court upheld the divorce...
Utah is so bone dry amid drought that a squirrel and kids with matches started separate brush fires
Utah is so bone dry amid drought that a squirrel and kids with matches started separate brush fires The two fires were sparked within hours on Tuesday - Bookmark - CommentsGo to comments Utah is suffering from such dry conditions that a group of kids with matches and a squirrel managed to start separate brush fires within hours, according to local media reports. The ongoing severe drought in Utah has created the perfect conditions for destructive fires, prompting officials to warn that this...
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
Announce Type: new Abstract: We propose a novel neural network architecture, termed Multi-Scale Separable Fourier Neural Networks (MS-SFNN), for the accurate and efficient solution of linear and nonlinear high-frequency partial differential equations (PDEs). MS-SFNN exploits a separable representation: given a $d$-dimensional input, it employs $d$ independent subnetworks -- each acting on a single coordinate -- and constructs basis functions via element-wise multiplication of their outputs....
A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References
arXiv:2508.14623v2 Announce Type: replace-cross Abstract: This paper examines the implications of using the Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) as both evaluation and training objective in supervised speech separation, when the training references contain noise, as is the case with the de facto benchmark WSJ0-2Mix. A derivation of the SI-SDR with noisy references reveals that noise limits the achievable SI-SDR, or leads to undesired noise in the separated outputs. To address...
Enhancing Blind Source Separation with Dissociative Principal Component Analysis
arXiv:2411.12321v2 Announce Type: replace Abstract: Principal component analysis (PCA) and its sparse variants (sPCA) are widely used as a precursor to independent component analysis (ICA) for blind source separation (BSS). However, sPCA typically relies on a deflation strategy that extracts components sequentially and imposes orthogonality between them. When the underlying sources overlap, this discards the cross component structure that ICA depends on, degrading separation.
A Geometric Measure of Linear Separability for Neural Representations
Announce Type: new Abstract: Modern neural classifiers commonly rely on linear readouts, yet predictive metrics alone do not characterize the class-wise geometry of the representations on which such readouts operate. We introduce the directional linear separability measure (LSM), a finite-sample diagnostic for one-sided affine separability. For a target class A and a competing set B, LSM searches over affine halfspaces that contain all samples in A and measures the smallest competing-sample...