Miracle-dz/UniFAR-FTUs huggingface dataset:
AugCSE: Contrastive Sentence Embedding with Diverse Augmentations

Data augmentation techniques have been proven useful in many applications in NLP fields. Most augmentations are task-specific, and cannot be used as a general-purpose tool. In our work, we present AugCSE, a unified framework to utilize diverse sets of data augmentations to achieve a better, general-purpose, sentence embedding model. Building upon the latest sentence embedding models, our approach uses a simple antagonistic discriminator that differentiates the augmentation types. With the finetuning objective borrowed from domain adaptation, we show that diverse augmentations, which often lead to conflicting contrastive signals, can be tamed to produce a better and more robust sentence representation. Our methods achieve state-of-the-art results on downstream transfer tasks and perform competitively on semantic textual similarity tasks, using only unsupervised data.
A Dynamical System View of Langevin-Based Non-Convex Sampling

Non-convex sampling is a key challenge in machine learning, central to non-convex optimization in deep learning as well as to approximate probabilistic inference. Despite its significance, theoretically there remain many important challenges: Existing guarantees (1) typically only hold for the averaged iterates rather than the more desirable last iterates, (2) lack convergence metrics that capture the scales of the variables such as Wasserstein distances, and (3) mainly apply to elementary schemes such as stochastic gradient Langevin dynamics. In this paper, we develop a new framework that lifts the above issues by harnessing several tools from the theory of dynamical systems. Our key result is that, for a large class of state-of-the-art sampling schemes, their last-iterate convergence in Wasserstein distances can be reduced to the study of their continuous-time counterparts, which is much better understood. Coupled with standard assumptions of MCMC sampling, our theory immediately yields the last-iterate Wasserstein convergence of many advanced sampling schemes such as proximal, randomized mid-point, and Runge-Kutta integrators. Beyond existing methods, our framework also motivates more efficient schemes that enjoy the same rigorous guarantees.
Does Medical Imaging learn different Convolution Filters?

Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through"spikey"distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected medical imaging models in more detail. We show that instead of fundamental differences, the outliers are due to specific processing in some architectures. Quite the contrary, for standardized architectures, we find that models trained on medical data do not significantly differ in their filter distributions from similar architectures trained on data from other domains. Our conclusions reinforce previous hypotheses stating that pre-training of imaging models can be done with any kind of diverse image data.
Follower–Followee Ratio Category and User Vector for Analyzing Following Behavior

Analyzing following behavior is important in many applications. Following behavior may depend on the main intention of the follower. Users may either follow their friends or they may follow celebrities to know more about them. It is difficult to estimate users’ intention from their following relationships. In this paper, we propose an approach to analyze following relationships. First, we investigated the similarity between users. Similar followers and followees are likely to be friends. However, when the follower and followee are not similar, it is likely that follower seeks to obtain more information on the followee. Second, we categorized users by the network structure. We then proposed analysis of following behavior based on similarity and category of users estimated from tweets and user data. We confirmed the feasibility of the proposed method through experiments. Finally, we examined users in different categories and analyzed their following behavior.
Population study for gamma-ray pulsars; II Millisecond pulsars

The population of $\gamma$-ray emitting millisecond pulsars (MSPs) is studied by using Monte-Carlo techniques. We simulate the Galactic distributions of the MSPs, and apply the outer gap model for the $\gamma$-ray emission from each simulated MSP. We take into account the dependence of the observed $\gamma$-ray flux on the viewing angle and inclination angle, which is the angle between the rotation axis and the magnetic axis, respectively. Using the sensitivity of the six-month long observation of the $Fermi$ telescope and radio sensitivities of existing pulsar surveys, 9-13 radio-selected and 22-35 $\gamma$-ray-selected pulsars are detected within our simulation. The statistical properties of the simulated population are consistent with the $Fermi$ observations. Scaling the observed sensitivity $\propto \sqrt{T}$, where $T$ is the length of observation time, the present model predicts that over the 5-year mission $Fermi$ would detect 15-22 radio-selected $\gamma$-ray MSPs, and 95-152 $\gamma$-ray-selected MSPs. Our simulation also predicts that about 100 (or 200-300) $\gamma$-ray MSPs with a flux larger $F\ge 10^{-11}~\mathrm{erg/cm^2 s}$ (or $5\times 10^{-12}~\mathrm{erg/cm^2 s}$) irradiate the Earth. With the present sensitivities of the radio surveys, most of them are categorized as $\gamma$-ray-selected pulsars, indicating that most of the $\gamma$-ray MSPs have been missed by the present $Fermi$ observations. We argue that the Galactic $Fermi$ unidentified sources located at high latitudes should be dominated by MSPs, whereas the sources in the galactic plane are dominated by radio-quiet canonical pulsars.
RCSED—A Value-added Reference Catalog of Spectral Energy Distributions of 800,299 Galaxies in 11 Ultraviolet, Optical, and Near-infrared Bands: Morphologies, Colors, Ionized Gas, and Stellar Population Properties

We present RCSED, the value-added Reference Catalog of Spectral Energy Distributions of galaxies, which contains homogenized spectrophotometric data for 800,299 low- and intermediate-redshift galaxies ( ) selected from the Sloan Digital Sky Survey spectroscopic sample. Accessible from the Virtual Observatory (VO) and complemented with detailed information on galaxy properties obtained with state-of-the-art data analysis, RCSED enables direct studies of galaxy formation and evolution over the last 5 Gyr. We provide tabulated color transformations for galaxies of different morphologies and luminosities, and analytic expressions for the red sequence shape in different colors. RCSED comprises integrated k-corrected photometry in up to 11 ultraviolet, optical, and near-infrared bands published by the GALEX, SDSS, and UKIDSS wide-field imaging surveys; results of the stellar population fitting of SDSS spectra including best-fitting templates, velocity dispersions, parameterized star formation histories, and stellar metallicities computed for instantaneous starburst and exponentially declining star formation models; parametric and non-parametric emission line fluxes and profiles; and gas phase metallicities. We link RCSED to the Galaxy Zoo morphological classification and galaxy bulge+disk decomposition results of Simard et al. We construct the color–magnitude, Faber–Jackson, and mass–metallicity relations; compare them with the literature; and discuss systematic errors of the galaxy properties presented in our catalog. RCSED is accessible from the project web site and via VO simple spectrum access and table access services using VO-compliant applications. We describe several examples of SQL queries to the database. Finally, we briefly discuss existing and future scientific applications of RCSED and prospective catalog extensions to higher redshifts and different wavelengths.
Simplified State Storage Rent for EVM Blockchains

—Uncontrolled growth of blockchain state can ad- versely affect client performance, decentralization and security. Previous attempts to introduce duration-based state storage pric- ing or storage rent in Ethereum have stalled, partly because of complexity. We present a new approach with ﬁner granularity to “ spread ” rent payments across peers. Our proposal shifts the burden of state rent from ‘owners’ or contracts to transaction senders in a ‘quasi-random’ manner. This proposal offers a simple path for initial adoption on Ethereum Virtual Machine (EVM) compatible chains, and serve as a foundation to address remaining challenges.
BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling Parametric BSDFs

Parametric BSDFs (Bidirectional Scattering Distribution Functions) are per-vasively used because of their flexibility to represent a large variety of material appearances by simply tuning the parameters. While efficient evaluation of parametric BSDFs has been well-studied, high-quality importance sampling techniques for parametric BSDFs are still scarce. Existing sampling strategies either heavily rely on approximations and result in high variance, or solely perform sampling on a portion of the whole BSDF slice. Moreover, many of the sampling approaches are specifically paired with certain types of BSDFs. In this paper, we seek an efficient and general way for importance sampling parametric BSDFs. We notice that the nature of importance sampling is the mapping between a uniform distribution and the target distribution. Specifically, when BSDF parameters are given, the mapping that performs importance sampling on a BSDF slice can be simply recorded as a 2D image that we name as importance map. Following this observation, we accurately precompute the importance maps using a mathematical tool named optimal transport. Then we propose a lightweight neural network to efficiently compress the precomputed importance maps. In this way, we have completely brought parametric BSDF importance sampling to the precomputation stage, avoiding heavy runtime computation. Since this process is similar to light baking where a set of images are precomputed, we name our method importance baking. Together with a BSDF evaluation network and a PDF (probability density function) query network, our method enables full multiple importance sampling (MIS) without any revision to the rendering pipeline. Our method essentially performs perfect importance sampling. Compared with previous methods, we demonstrate reduced noise levels on rendering results with a rich set of appearances, including both conductors and dielectrics with anisotropic roughness. CCS Concepts: • Computing methodologies → Rendering .
Gaussian Mean Testing Made Simple

We study the following fundamental hypothesis testing problem, which we term Gaussian mean testing. Given i.i. d. samples from a distribution $p$ on $\mathbb{R}^d$, the task is to distinguish, with high probability, between the following cases: (i) $p$ is the standard Gaussian distribution, $\mathcal{N}(0,I_d)$, and (ii) $p$ is a Gaussian $\mathcal{N}(\mu,\Sigma)$ for some unknown covariance $\Sigma$ and mean $\mu \in \mathbb{R}^d$ satisfying $\|\mu\|_2 \geq \epsilon$. Recent work gave an algorithm for this testing problem with the optimal sample complexity of $\Theta(\sqrt{d}/\epsilon^2)$. Both the previous algorithm and its analysis are quite complicated. Here we give an extremely simple algorithm for Gaussian mean testing with a one-page analysis. Our algorithm is sample optimal and runs in sample linear time.
Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper, showing the variability of noise-related distractions, thermal comfort, and associated context. The results show that participants experienced at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and the collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants’ needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.
Stable Deep MRI Reconstruction Using Generative Priors

Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classical variational approach yields high-quality reconstructions irrespective of the sub-sampling pattern. In addition, the model shows stable behavior when confronted with out-of-distribution data in the form of contrast variation. Furthermore, a probabilistic interpretation provides a distribution of reconstructions and hence allows uncertainty quantification. To reconstruct parallel MRI, we propose a fast algorithm to jointly estimate the image and the sensitivity maps. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods, while preserving the flexibility with respect to sub-sampling patterns and allowing for uncertainty quantification.
Highly Efficient Real-Time Streaming and Fully On-Device Speaker Diarization with Multi-Stage Clustering

While recent research advances in speaker diarization mostly focus on improving the quality of diarization results, there is also an increasing interest in improving the efficiency of diarization systems. In this paper, we demonstrate that a multi-stage clustering strategy that uses different clustering algorithms for input of different lengths can address multi-faceted challenges of on-device speaker diarization applications. Specifically, a fallback clusterer is used to handle short-form inputs; a main clusterer is used to handle medium-length inputs; and a pre-clusterer is used to compress long-form inputs before they are processed by the main clusterer. Both the main clusterer and the pre-clusterer can be configured with an upper bound of the computational complexity to adapt to devices with different resource constraints. This multi-stage clustering strategy is critical for streaming on-device speaker diarization systems, where the budgets of CPU, memory and battery are tight.
Evidence for the line-of-sight structure in the Hubble Frontier Field cluster, MACSJ0717.5+3745

MACS J0717 is the most massive and extended of the Hubble Frontier Field clusters. It is one of the more difficult clusters to model, and we argue that this is in part due to the line of sight structure (LoS) at redshifts beyond 2. We show that the Grale mass reconstruction based on sources at 3<z_s<4.1 has fewx10^{13}M_sun more mass than that based on nearby sources, z_s<2.6, and attribute the excess mass to a putative LoS, which is at least 75" from the cluster center. Furthermore, the lens-model fitted z_s's of the recent Kawamata et al. reconstruction are biased systematically low compared to photometric z_s's, and the bias is a function of images' distance from the cluster center. We argue that these mimic the effect of LoS. We conclude that even in the presence of 100-200 images, lens-model adjusted source redshifts can conceal the presence of LoS, demonstrating the existence of degeneracies between z_s and (sub)structure. Also, a very good fit to image positions is not a sufficient condition for having a high fidelity mass map: Kawamata et al. obtain an rms of 0.52" for 173 images of 60 sources; our Grale reconstruction of the exact same data yields a somewhat different map, but similarly low rms, 0.62". In contrast, a Grale model that uses reasonable, but fixed z_s gives a worse rms of 1.28" for 44 sources with 126 images. Unaccounted for LoS can bias the mass map, affecting the magnification and luminosity function estimates of high redshift sources.
Isomorphic mesh generation from point clouds with multilayer perceptrons

We propose a new neural network, called isomorphic mesh generator (iMG), which generates isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects have a unified mesh structure even though the objects belong to different classes. This unified representation enables surface models to be handled by DNNs. Moreover, the unified mesh structure of isomorphic meshes enables the same process to be applied to all isomorphic meshes; although in the case of general mesh models, we need to consider the processes depending on their mesh structures. Therefore, the use of isomorphic meshes leads to efficient memory usage and calculation time compared with general mesh models. As iMG is a data-free method, preparing any point clouds as training data in advance is unnecessary, except a point cloud of the target object used as the input data of iMG. Additionally, iMG outputs an isomorphic mesh obtained by mapping a reference mesh to a given input point cloud. To estimate the mapping function stably, we introduce a step-by-step mapping strategy. This strategy achieves a flexible deformation while maintaining the structure of the reference mesh. From simulation and experiments using a mobile phone, we confirmed that iMG can generate isomorphic meshes of given objects reliably even when the input point cloud includes noise and missing parts.
Bit Error and Block Error Rate Training for ML-Assisted Communication

Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention. In this paper, we show that the commonly used binary cross-entropy (BCE) loss is a sensible choice in uncoded systems, e.g., for training ML-assisted data detectors, but may not be optimal in coded systems. We propose new loss functions targeted at minimizing the block error rate and SNR deweighting, a novel method that trains communication systems for optimal performance over a range of signal-to-noise ratios. The utility of the proposed loss functions as well as of SNR deweighting is shown through simulations in NVIDIA Sionna.
An application of the generalized Morse index to travelling wave solutions of a competitive reaction-diffusion model

Abstract : The existence of travelling wave solutions of a diffusion reaction-system is studied via the generalized Morse index of isolated invariant sets. This index theory is analogous to degree theory, and the method of proof follows lines familiar from the latter theory. The equations in question are 'deformed' to a 'standard' system where the index can be easily computed, and the existence theorem follows from the 'non-triviality' of the index. The index theory has been described in other papers; here the main job is to construct 'isolating neighborhoods' which are analogous (in the degree theory) to open sets with no critical points on the boundary. Some novel means of location such neighborhoods are described.
MICP-L: Fast parallel simulative Range Sensor to Mesh registration for Robot Localization

—Triangle mesh-based maps have proven to be a powerful 3D representation of the environment, allowing robots to navigate using universal methods, indoors as well as in challenging outdoor environments with tunnels, hills and varying slopes. However, any robot that navigates autonomously necessarily requires stable, accurate, and continuous localization in such a mesh map where it plans its paths and missions. We present MICP-L, a novel and very fast Mesh ICP Localization method that can register one or more range sensors directly on a triangle mesh map to continuously localize a robot, determining its 6D pose in the map. Correspondences between a range sensor and the mesh are found through simulations accelerated with the latest RTX hardware. With MICP-L, a correction can be performed quickly and in parallel even with combined data from different range sensor models. With this work, we aim to signiﬁcantly advance the development in the ﬁeld of mesh-based environment representation for autonomous robotic applications. MICP-L is open source and fully integrated with ROS and tf.
ASD: Towards Attribute Spatial Decomposition for Prior-Free Facial Attribute Recognition

—Representing the spatial properties of facial at- tributes is a vital challenge for facial attribute recognition (FAR). Recent advances have achieved the reliable performances for FAR, beneﬁting from the description of spatial properties via extra prior information. However, the extra prior information might not be always available, resulting in the restricted application scenario of the prior-based methods. Meanwhile, the spatial ambiguity of facial attributes caused by inherent spatial diversities of facial parts is ignored. To address these issues, we propose a prior-free method for attribute spatial decomposition (ASD), mitigating the spatial ambiguity of facial attributes without any extra prior information. Speciﬁcally, assignment-embedding module (AEM) is proposed to enable the procedure of ASD, which consists of two operations: attribute-to-location assignment and location-to-attribute embedding. The attribute- to-location assignment ﬁrst decomposes the feature map based on latent factors, assigning the magnitude of attribute components on each spatial location. Then, the assigned attribute components from all locations to represent the global-level attribute embed- dings. Furthermore, correlation matrix minimization (CMM) is introduced to enlarge the discriminability of attribute embed- dings. Experimental results demonstrate the superiority of ASD compared with state-of-the-art prior-based methods, while the reliable performance of ASD for the case of limited training data is further validated. the blank of prior-free method in FAR task.