Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-amplitude Solar-like Oscillations in the K5 V Star ϵ Indi A

We have detected solar-like oscillations in the mid-K-dwarf ϵ Indi A, making it the coolest dwarf to have measured oscillations. The star is noteworthy for harboring a pair of brown dwarf companions and a Jupiter-type planet. We observed ϵ Indi A during two radial velocity campaigns, using the high-resolution spectrographs HARPS (2011) and UVES (2021). Weighting the time series, we computed the power spectra and established the detection of solar-like oscillations with a power excess located at 5265 ± 110 μHz—the highest frequency solar-like oscillations so far measured in any star. The measurement of the center of the power excess allows us to compute a stellar mass of 0.782 ± 0.023 M ⊙ based on scaling relations and a known radius from interferometry. We also determine the amplitude of the peak power and note that there is a slight difference between the two observing campaigns, indicating a varying activity level. Overall, this work confirms that low-amplitude solar-like oscillations can be detected in mid-K-type stars in radial velocity measurements obtained with high-precision spectrographs.
Parendi: Thousand-Way Parallel RTL Simulation

Hardware development relies on simulations, particularly cycle-accurate RTL (Register Transfer Level) simulations, which consume significant time. As single-processor performance grows only slowly, conventional, single-threaded RTL simulation is becoming less practical for increasingly complex chips and systems. A solution is parallel RTL simulation, where ideally, simulators could run on thousands of parallel cores. However, existing simulators can only exploit tens of cores. This paper studies the challenges inherent in running parallel RTL simulation on a multi-thousand-core machine (the Graphcore IPU, a 1472-core machine). Simulation performance requires balancing three factors: synchronization, communication, and computation. We experimentally evaluate each metric and analyze how it affects parallel simulation speed, drawing on contrasts between the large-scale IPU and smaller but faster x86 systems. Using this analysis, we build Parendi, an RTL simulator for the IPU. It distributes RTL simulation across 5888 cores on 4 IPU sockets. Parendi runs large RTL designs up to 4x faster than a powerful, state-of-the-art x86 multicore system.
A generalization of quantum Lakshmibai-Seshadri paths for arbitrary weights

We construct an injective weight-preserving map (called the forgetful map) from the set of all admissible subsets in the quantum alcove model associated to an arbitrary weight. The image of this forgetful map can be explicitly described by introducing the notion of"interpolated quantum Lakshmibai-Seshadri (QLS for short) paths", which can be thought of as a generalization of quantum Lakshmibai-Seshadri paths. As an application, we reformulate, in terms of interpolated QLS paths, an identity of Chevalley type for the graded characters of Demazure submodules of a level-zero extremal weight module over a quantum affine algebra, which is a representation-theoretic analog of the Chevalley formula for the torus-equivariant $K$-group of a semi-infinite flag manifold.
Mastering Memory Tasks with World Models

Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps between actions and outcomes, or tasks demanding the recalling of distant observations to inform current actions. To improve temporal coherence, we integrate a new family of state space models (SSMs) in world models of MBRL agents to present a new method, Recall to Imagine (R2I). This integration aims to enhance both long-term memory and long-horizon credit assignment. Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze. At the same time, it upholds comparable performance in classic RL tasks, such as Atari and DMC, suggesting the generality of our method. We also show that R2I is faster than the state-of-the-art MBRL method, DreamerV3, resulting in faster wall-time convergence.
Improving Adversarial Training using Vulnerability-Aware Perturbation Budget

Adversarial Training (AT) effectively improves the robustness of Deep Neural Networks (DNNs) to adversarial attacks. Generally, AT involves training DNN models with adversarial examples obtained within a pre-defined, fixed perturbation bound. Notably, individual natural examples from which these adversarial examples are crafted exhibit varying degrees of intrinsic vulnerabilities, and as such, crafting adversarial examples with fixed perturbation radius for all instances may not sufficiently unleash the potency of AT. Motivated by this observation, we propose two simple, computationally cheap vulnerability-aware reweighting functions for assigning perturbation bounds to adversarial examples used for AT, named Margin-Weighted Perturbation Budget (MWPB) and Standard-Deviation-Weighted Perturbation Budget (SDWPB). The proposed methods assign perturbation radii to individual adversarial samples based on the vulnerability of their corresponding natural examples. Experimental results show that the proposed methods yield genuine improvements in the robustness of AT algorithms against various adversarial attacks.
Dynamic Cross Attention for Audio-Visual Person Verification

Although person or identity verification has been predominantly explored using individual modalities such as face and voice, audio-visual fusion has recently shown immense potential to outperform unimodal approaches. Audio and visual modalities are often expected to pose strong complementary relationships, which plays a crucial role in effective audio-visual fusion. However, they may not always strongly complement each other, they may also exhibit weak complementary relationships, resulting in poor audio-visual feature representations. In this paper, we propose a Dynamic Cross-Attention (DCA) model that can dynamically select the cross-attended or unattended features on the fly based on the strong or weak complementary relationships, respectively, across audio and visual modalities. In particular, a conditional gating layer is designed to evaluate the contribution of the cross-attention mechanism and choose cross-attended features only when they exhibit strong complementary relationships, otherwise unattended features. Extensive experiments are conducted on the Voxceleb1 dataset to demonstrate the robustness of the proposed model. Results indicate that the proposed model consistently improves the performance on multiple variants of cross-attention while outperforming the state-of-the-art methods.
Cooper pairing, flat-band superconductivity and quantum geometry in the pyrochlore-Hubbard model

We investigate the impacts of the quantum geometry of Bloch states, specifically through the band-resolved quantum-metric tensor, on Cooper pairing and flat-band superconductivity in a three-dimensional pyrochlore-Hubbard model. First we analyze the low-lying two-body spectrum exactly, and show that the pairing order parameter is uniform in this four-band lattice. This allowed us to establish direct relations between the superfluid weight of a multiband superconductor and ($i$) the effective mass of the lowest-lying two-body branch at zero temperature, ($ii$) the kinetic coefficient of the Ginzburg-Landau theory in proximity to the critical temperature, and ($iii$) the velocity of the low-energy Goldstone modes at zero temperature. Furthermore, we perform a comprehensive numerical analysis of the superfluid weight and Goldstone modes, exploring both their conventional and geometric components at zero temperature.
Analytic torsion for fibred boundary metrics and conic degeneration

We study the renormalized analytic torsion of complete manifolds with fibred boundary metrics, also referred to as $\phi$-metrics. We establish invariance of the torsion under suitable deformations of the metric, and establish a gluing formula. As an application, we relate the analytic torsions for complete $\phi$- and incomplete wedge-metrics. As a simple consequence we recover a result by Sher and Guillarmou about analytic torsion under conic degeneration.
RL-CFR: Improving Action Abstraction for Imperfect Information Extensive-Form Games with Reinforcement Learning

Effective action abstraction is crucial in tackling challenges associated with large action spaces in Imperfect Information Extensive-Form Games (IIEFGs). However, due to the vast state space and computational complexity in IIEFGs, existing methods often rely on fixed abstractions, resulting in sub-optimal performance. In response, we introduce RL-CFR, a novel reinforcement learning (RL) approach for dynamic action abstraction. RL-CFR builds upon our innovative Markov Decision Process (MDP) formulation, with states corresponding to public information and actions represented as feature vectors indicating specific action abstractions. The reward is defined as the expected payoff difference between the selected and default action abstractions. RL-CFR constructs a game tree with RL-guided action abstractions and utilizes counterfactual regret minimization (CFR) for strategy derivation. Impressively, it can be trained from scratch, achieving higher expected payoff without increased CFR solving time. In experiments on Heads-up No-limit Texas Hold'em, RL-CFR outperforms ReBeL's replication and Slumbot, demonstrating significant win-rate margins of $64\pm 11$ and $84\pm 17$ mbb/hand, respectively.
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.
"Did They Consent to That?": Safer Digital Intimacy via Proactive Protection Against Image-Based Sexual Abuse

As many as 8 in 10 adults share intimate content such as nude or lewd images. Sharing such content has significant benefits for relationship intimacy and body image, and can offer employment. However, stigmatizing attitudes and a lack of technological mitigations put those sharing such content at risk of sexual violence. An estimated 1 in 3 people have been subjected to image-based sexual abuse (IBSA), a spectrum of violence that includes the nonconsensual distribution or threat of distribution of consensually-created intimate content (also called NDII). In this work, we conducted a rigorous empirical interview study of 52 European creators of intimate content to examine the threats they face and how they defend against them, situated in the context of their different use cases for intimate content sharing and their choice of technologies for storing and sharing such content. Synthesizing our results with the limited body of prior work on technological prevention of NDII, we offer concrete next steps for both platforms and security&privacy researchers to work toward safer intimate content sharing through proactive protection.
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference

Given time series data, how can we answer questions like"what will happen in the future? "and"how did we get here? "These sorts of probabilistic inference questions are challenging when observations are high-dimensional. In this paper, we show how these questions can have compact, closed form solutions in terms of learned representations. The key idea is to apply a variant of contrastive learning to time series data. Prior work already shows that the representations learned by contrastive learning encode a probability ratio. By extending prior work to show that the marginal distribution over representations is Gaussian, we can then prove that joint distribution of representations is also Gaussian. Taken together, these results show that representations learned via temporal contrastive learning follow a Gauss-Markov chain, a graphical model where inference (e.g., prediction, planning) over representations corresponds to inverting a low-dimensional matrix. In one special case, inferring intermediate representations will be equivalent to interpolating between the learned representations. We validate our theory using numerical simulations on tasks up to 46-dimensions.
Symmetry Considerations for Learning Task Symmetric Robot Policies

Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively. Often, the learned behaviors fail to achieve the desired transformation invariances and suffer from motion artifacts. For instance, a quadruped may exhibit different gaits when commanded to move forward or backward, even though it is symmetrical about its torso. This issue becomes further pronounced in high-dimensional or complex environments, where DRL methods are prone to local optima and fail to explore regions of the state space equally. Past methods on encouraging symmetry for robotic tasks have studied this topic mainly in a single-task setting, where symmetry usually refers to symmetry in the motion, such as the gait patterns. In this paper, we revisit this topic for goal-conditioned tasks in robotics, where symmetry lies mainly in task execution and not necessarily in the learned motions themselves. In particular, we investigate two approaches to incorporate symmetry invariance into DRL -- data augmentation and mirror loss function. We provide a theoretical foundation for using augmented samples in an on-policy setting. Based on this, we show that the corresponding approach achieves faster convergence and improves the learned behaviors in various challenging robotic tasks, from climbing boxes with a quadruped to dexterous manipulation.
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders

Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conversational tasks. However, traditional evaluation benchmarks rely solely on task metrics that don't particularly measure gaps related to semantic understanding. Thus, we propose an intent semantic toolkit that gives a more holistic view of intent embedding models by considering three tasks-- (1) intent classification, (2) intent clustering, and (3) a novel triplet task. The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature. We observe that current embedding models fare poorly in semantic understanding of these concepts. To address this, we propose a pre-training approach to improve the embedding model by leveraging augmentation with data generated by an auto-regressive model and a contrastive loss term. Our approach improves the semantic understanding of the intent embedding model on the aforementioned linguistic dimensions while slightly effecting their performance on downstream task metrics.
A Coreset for Approximate Furthest-Neighbor Queries in a Simple Polygon

Let $\mathcal{P}$ be a simple polygon with $m$ vertices and let $P$ be a set of $n$ points inside $\mathcal{P}$. We prove that there exists, for any $\varepsilon>0$, a set $\mathcal{C} \subset P$ of size $O(1/\varepsilon^2)$ such that the following holds: for any query point $q$ inside the polygon $\mathcal{P}$, the geodesic distance from $q$ to its furthest neighbor in $\mathcal{C}$ is at least $1-\varepsilon$ times the geodesic distance to its further neighbor in $P$. Thus the set $\mathcal{C}$ can be used for answering $\varepsilon$-approximate furthest-neighbor queries with a data structure whose storage requirement is independent of the size of $P$. The coreset can be constructed in $O\left(\frac{1}{\varepsilon} \left( n\log(1/\varepsilon) + (n+m)\log(n+m)\right) \right)$ time.
Pairwise interaction of spherical particles aligned in oscillatory flow

We present a systematic simulation campaign to investigate the pairwise interaction of two mobile, monodisperse particles submerged in a viscous fluid and subjected to monochromatic oscillating flows. To this end, we employ the immersed boundary method to geometrically resolve the flow around the two particles in a non-inertial reference frame. We neglect gravity to focus on fluid-particle interactions associated with particle inertia and consider particles of three different density ratios aligned along the axis of oscillation. We systematically vary the initial particle distance and the frequency based on which the particles show either attractive or repulsive behavior by approaching or moving away from each other, respectively. This behavior is consistently confirmed for the three density ratios investigated, although particle inertia dictates the overall magnitude of the particle dynamics. Based on this, threshold conditions for the transition from attraction to repulsion are introduced that obey the same power law for all density ratios investigated. We furthermore analyze the flow patterns by suitable averaging and decomposition of the flow fields and find competing effects of the vorticity induced by the fluid-particle interactions. Based on these flow patterns, we derive a circulation-based criterion that provides a quantitative measure to categorize the different cases. It is shown that such a criterion provides a consistent measure to distinguish the attractive and repulsive arrangements.
OGMP: Oracle Guided Multimodal Policies for Agile and Versatile Robot Control

Amidst task-specific learning-based control synthesis frameworks that achieve impressive empirical results, a unified framework that systematically constructs an optimal policy for sufficiently solving a general notion of a task is absent. Hence, we propose a theoretical framework for a task-centered control synthesis leveraging two critical ideas: 1) oracle-guided policy optimization for the non-limiting integration of sub-optimal task-based priors to guide the policy optimization and 2) task-vital multimodality to break down solving a task into executing a sequence of behavioral modes. The proposed approach results in highly agile parkour and diving on a 16-DoF dynamic bipedal robot. The obtained policy advances indefinitely on a track, performing leaps and jumps of varying lengths and heights for the parkour task. Corresponding to the dive task, the policy demonstrates front, back, and side flips from various initial heights. Finally, we introduce a novel latent mode space reachability analysis to study our policies' versatility and generalization by computing a feasible mode set function through which we certify a set of failure-free modes for our policy to perform at any given state.
Minimizing the Thompson Sampling Regret-to-Sigma Ratio (TS-RSR): a provably efficient algorithm for batch Bayesian Optimization

This paper presents a new approach for batch Bayesian Optimization (BO), where the sampling takes place by minimizing a Thompson Sampling approximation of a regret to uncertainty ratio. Our objective is able to coordinate the actions chosen in each batch in a way that minimizes redundancy between points whilst focusing on points with high predictive means or high uncertainty. We provide high-probability theoretical guarantees on the regret of our algorithm. Finally, numerically, we demonstrate that our method attains state-of-the-art performance on a range of nonconvex test functions, where it outperforms several competitive benchmark batch BO algorithms by an order of magnitude on average.
Hydrodynamics of electron-hole fluid photogenerated in a mesoscopic two-dimensional channel

The dynamics of the diffusion flow of holes photoinjected into a mesoscopic GaAs channel of variable width, where they, together with background electrons, form a hydrodynamic electron-hole fluid, is studied using time-resolved microphotoluminescence. It is found that the rate of recombination of photoinjected holes, which is proportional to the rate of their flow, decreases when holes pass through the expanded sections of the channel. In fact, this is the Venturi effect, which consists in a decrease in the velocity of the fluid in the expanded sections of the pipe. Moreover, a non-uniform diffusion velocity profile is observed, similar to the parabolic Hagen-Poiseuille velocity profile, which indicates a viscous hydrodynamic flow. It is shown that in argeement with a theory, the magnetic field strongly suppresses the viscosity of the electron-hole fluid. Additional evidence of the viscous nature of the studied electron-hole fluid is the observed increase in the recombination rate with increasing temperature, which is similar to the decrease in the electrical resistance of viscous electrons with temperature.
Online Adaptation of Language Models with a Memory of Amortized Contexts

Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. Due to this crucial need to keep models updated, online learning has emerged as a critical necessity when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank. When answering questions, our model attends to and extracts relevant knowledge from this memory bank. To learn informative modulations in an efficient manner, we utilize amortization-based meta-learning, which substitutes the optimization process with a single forward pass of the encoder. Subsequently, we learn to choose from and aggregate selected documents into a single modulation by conditioning on the question, allowing us to adapt a frozen language model during test time without requiring further gradient updates. Our experiment demonstrates the superiority of MAC in multiple aspects, including online adaptation performance, time, and memory efficiency. Code is available at: https://github.com/jihoontack/MAC.