Miracle-dz/UniFAR-FTUs huggingface dataset:
Dihedral Multi-Reference Alignment

We study the dihedral multi-reference alignment problem of estimating the orbit of a signal from multiple noisy observations of the signal, acted on by random elements of the dihedral group. We show that if the group elements are drawn from a generic distribution, the orbit of a generic signal is uniquely determined from the second moment of the observations. This implies that the optimal estimation rate in the high noise regime is proportional to the square of the variance of the noise. This is the first result of this type for multi-reference alignment over a non-abelian group with a non-uniform distribution of group elements. Based on tools from invariant theory and algebraic geometry, we also delineate conditions for unique orbit recovery for multi-reference alignment models over finite groups (namely, when the dihedral group is replaced by a general finite group) when the group elements are drawn from a generic distribution. Finally, we design and study numerically three computational frameworks for estimating the signal based on group synchronization, expectation-maximization, and the method of moments.
A Flexible Multi-Task Model for BERT Serving

We present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and incremental development of the tasks. The proposed framework is based on the idea of partial fine-tuning, i.e. only fine-tune some top layers of BERT while keep the other layers frozen. For each task, we train independently a single-task (ST) model using partial fine-tuning. Then we compress the task-specific layers in each ST model using knowledge distillation. Those compressed ST models are finally merged into one MT model so that the frozen layers of the former are shared across the tasks. We exemplify our approach on eight GLUE tasks, demonstrating that it is able to achieve 99.6% of the performance of the full fine-tuning method, while reducing up to two thirds of its overhead.
Microscopic interactions and emerging elasticity in model soft particulate gels

We discuss a class of models for particulate gels in which the particle contacts are described by an effective interaction combining a two-body attraction and a three-body angular repulsion. Using molecular dynamics, we show how varying the model parameters allows us to sample, for a given gelation protocol, a variety of gel morphologies. For a specific set of the model parameters, we identify the local elastic structures that get interlocked in the gel network. Using the analytical expression of their elastic energy from the microscopic interactions, we can estimate their contribution to the emergent elasticity of the gel and gain new insight into its origin.
Configuration mixing in strange tetraquarks 
Zcs

The BESIII Collaboration has observed a candidate for a cc̄sū tetraquark Zcs at (3982.5 −2.6 ± 2.1) MeV and width (12.8 +5.3 −4.4 ± 3.0) MeV, while the LHCb Collaboration has observed a Zcs candidate in the J/ψK − channel with mass of (4003± 6 −14) MeV and width (131± 15± 26) MeV. In this note we examine the possibility that these two states are distinct eigenstates of a mixing process similar to that which gives rise to two axial-vector mesons labeled by the Particle Data Group [1] K1(1270) and K1(1400). The main point is that on top of a c̄c pair, the Zcs states have the same light quark content as the K1-s. In the compact tetraquark picture this implies several additional states, analogous to members of theK1 nonet. These states have not yet been observed, nor are they required in the molecular approach. Thus experimental discovery or exclusion of these extra states will be a critical test for competing models of exotic mesons with hidden charm.
Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic

Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, these assumptions are often violated: testing done on networks (housemates, spouses, co-workers) captures correlated individuals, while infection risk varies substantially across time, place and individuals. Neglecting dependencies and heterogeneity may bias established optimality grids and induce a sub-optimal implementation of the procedure. As a lesson learned from this pandemic, this paper highlights the necessity of integrating field sampling information with statistical modeling to efficiently optimize pooled testing. Using real data, we show that (a) greater gains can be achieved at low logistical cost by exploiting natural correlations (non-independence) between samples -- allowing improvements in sensitivity and efficiency of up to 30% and 90% respectively;and (b) these gains are robust despite substantial heterogeneity across pools (non-identical). Our modeling results complement and extend the observations of Barak et al (2021) who report an empirical sensitivity well beyond expectations. Finally, we provide an interactive tool for selecting an optimal pool size using contextual information
Remote Blood Oxygen Estimation From Videos Using Neural Networks

Peripheral blood oxygen saturation (SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>) is an essential indicator of respiratory functionality and received increasing attention during the COVID-19 pandemic. Clinical findings show that COVID-19 patients can have significantly low SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> before any obvious symptoms. Measuring an individual's SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> without having to come into contact with the person can lower the risk of cross contamination and blood circulation problems. The prevalence of smartphones has motivated researchers to investigate methods for monitoring SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> using smartphone cameras. Most prior schemes involving smartphones are contact-based: They require using a fingertip to cover the phone's camera and the nearby light source to capture reemitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> estimation scheme using smartphone cameras. The scheme analyzes the videos of an individual's hand for physiological sensing, which is convenient and comfortable for users and can protect their privacy and allow for keeping face masks on. We design explainable neural network architectures inspired by the optophysiological models for SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> measurement, showing the potential of the proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula> estimation performance.
Consensus as a Nash Equilibrium of a Stochastic Differential Game

In this paper a consensus has been constructed in a social network which is modeled by a stochastic differential game played by agents of that network. Each agent independently minimizes a cost function which represents their motives. A conditionally expected integral cost function has been considered under an agent’s opinion filtration. The dynamic cost functional is minimized subject to a stochastic differential opinion dynamics. As opinion dynamics represents an agent’s differences of opinion from the others as well as from their previous opinions, random influences and stubbornness make it more volatile. An agent uses their rate of change of opinion at certain time point as a control input. This turns out to be a non-cooperative stochastic differential game which have a feedback Nash equilibrium. A Feynman-type path integral approach has been used to determine an optimal feedback opinion and control. This is a new approach in this literature. Later in this paper an explicit solution of a feedback Nash equilibrium opinion is determined.
Regression Analysis of Gluten‐Free Pasta from Brown Rice for Characterization and In vitro Digestibility

The paper explores the possibility of utilizing brown rice for preparation of gluten-free pasta using twin screw extruder. Response surface methodology was used to analyse the effect of feed moisture (28–34%), barrel temperature (70–110C) and screw speed(120–220 rpm) on quality responses (degree of gelatinization, minimum cooking time, water absorption, volume expansion, and gruel solid loss) and regression analysis was carried out to fit mathematical models to the experimental data. The selected pasta samples were examined further for pasting properties, color, texture, in vitro starch, and protein digestibility. Higher feed moisture exhibited dominant effect on cooking quality of pasta. The degree of gelatinization showed a positive correlation with feed moisture and barrel temperature. Luminosity enhanced with increasing screw speed and feed moisture. Firmness of cooked pasta elevated with an increase in the barrel temperature. Extrusion cooking significantly increased in vitro protein and starch digestibility, however, higher feed moisture favored protein and starch digestibility. Practical Applications Pasta products are normally produced using cold extrusion process 40–50C temperature. Generally single screw extruder is used for pasta manufacture. In the present scenario of convenience and newer products, we explored the possibility of using brown rice along with twin screw extruder for preparation of gluten-free pasta. Effect of feed moisture, barrel temperature, and screw speed on the quality parameters of pasta were studied. Pasta produced at higher feed moisture and screw speed showed higher in vitro starch and protein digestibility.
The nucleation fraction of local volume galaxies

Nuclear star clusters (NSCs) are a common phenomenon in galaxy centres and are found in a vast majority of galaxies of intermediate stellar mass $\approx 10^9\, \mathrm{M}_{\odot }$. Recent investigations suggest that they are rarely found in the least and most massive galaxies and that the nucleation fraction increases in dense environments. It is unclear whether this trend holds true for field galaxies due to the limited data currently available. Here we present our results on the nucleation fraction for 601 galaxies in the Local Volume ($\lesssim {12}{\, \mathrm{Mpc}}$). Covering more than eight orders of magnitude in stellar mass, this is the largest sample of galaxies analysed in a low-density environment. Within the Local Volume sample we find a strong dependence of the nucleation fraction on galaxy stellar mass, in agreement with previous work. We also find that for galaxies with $M_{\star } \lt 10^{9}\, \mathrm{M}_{\odot }$, early-type galaxies have a higher nucleation fraction than late-types. The nucleation fraction in the Local Volume correlates independently with stellar mass, Hubble type, and local environmental density. We compare our data to those in galaxy cluster environments (Coma, Fornax, and Virgo) by compiling previous results and calculating stellar masses in a homogeneous way. We find significantly lower nucleation fractions (up to 40 per cent) in galaxies with $M_{\star } \lesssim 10^{9.5}\, \mathrm{M}_{\odot }$, in agreement with previous work. Our results reinforce the connection between globular clusters and NSCs, but it remains unclear if it can explain the observed trends with Hubble type and local environment. We speculate that correlation between the nucleation fraction and cluster environment weakens for the densest clusters like Coma and Virgo.
NeuroDB: A Neural Network Framework for Answering Range Aggregate Queries and Beyond

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning models, where a model of the data is learned to answer the queries. However, such modelling choices fail to utilize any query specific information. To capture such information, we observe that RAQs can be represented by query functions, which are functions that take a query instance (i.e., a specific RAQ) as an input and output its corresponding answer. Using this representation, we formulate the problem of learning to approximate the query function, and propose NeuroDB, a query specialized neural network framework, that answers RAQs efficiently. NeuroDB is query-type agnostic (i.e., it does not make any assumption about the underlying query type) and our observation that queries can be represented by functions is not specific to RAQs. Thus, we investigate whether NeuroDB can be used for other query types, by applying it to distance to nearest neighbour queries. We experimentally show that NeuroDB outperforms the state-of-the-art for this query type, often by orders of magnitude. Moreover, the same neural network architecture as for RAQs is used, bringing to light the possibility of using a generic framework to answer any query type efficiently.
Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis

Deep learning models have become a popular choice for medical image analysis. However, the poor generalization performance of deep learning models limits them from being deployed in the real world as robustness is critical for medical applications. For instance, the state-of-the-art Convolutional Neural Networks (CNNs) fail to detect adversarial samples or samples drawn statistically far away from the training distribution. In this work, we experimentally evaluate the robustness of a Mahalanobis distance-based confidence score, a simple yet effective method for detecting abnormal input samples, in classifying malaria parasitized cells and uninfected cells. Results indicated that the Mahalanobis confidence score detector exhibits improved performance and robustness of deep learning models, and achieves stateof-the-art performance on both out-of-distribution (OOD) and adversarial samples.
Non-abelian Mellin transformations and applications

Abstract We study non-abelian versions of the Mellin transformations, originally introduced by Gabber-Loeser on complex affine tori. Our main result is a generalisation to the non-abelian context and with arbitrary coefficients of the t-exactness of Gabber-Loeser’s Mellin transformation. As an intermediate step, we obtain vanishing results for the Sabbah specialisation functors. Our main application is to construct new examples of duality spaces in the sense of Bieri-Eckmann, generalising results of Denham-Suciu.
Few-shot Learning with Global Relatedness Decoupled-Distillation

Despite the success that metric learning based approaches have achieved in few-shot learning, recent works reveal the ineffectiveness of their episodic training mode. In this paper, we point out two potential reasons for this problem: 1) the random episodic labels can only provide limited supervision information, while the relatedness information between the query and support samples is not fully exploited; 2) the meta-learner is usually constrained by the limited contextual information of the local episode. To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy. Our GRDD learns new visual concepts quickly by imitating the habit of humans, i.e. learning from the deep knowledge distilled from the teacher. More specifically, we first train a global learner on the entire base subset using category labels as supervision to leverage the global context information of the categories. Then, the well-trained global learner is used to simulate the query-support relatedness in global dependencies. Finally, the distilled global query-support relatedness is explicitly used to train the meta-learner using the RDD strategy, with the goal of making the meta-learner more discriminative. The RDD strategy aims to decouple the dense query-support relatedness into the groups of sparse decoupled relatedness. Moreover, only the relatedness of a single support sample with other query samples is considered in each group. By distilling the sparse decoupled relatedness group by group, sharper relatedness can be effectively distilled to the meta-learner, thereby facilitating the learning of a discriminative meta-learner. We conduct extensive experiments on the miniImagenet and CIFAR-FS datasets, which show the state-of-the-art performance of our GRDD method.
SYNTHESIS, REACTIONS, AND SPECTRAL (IR, 1H, 13C AND 29Si) CHARACTERIZATION OF ORGANOSILICON ARYLOXIDES

Organosilicon aryloxides of the types MenSi(OAr)^n(n = 1,2 and 3; OAr = OCfiH3Me2-2,6; O C ^ M e ^ ^ O C ^ M e ^ ^ ; OC^P^-2 ,6 ; OC6H2Me3-2,4,6; OC6H3Bu<2-2,6; O C ^ B u ^ 2,4,6),and two silicon (IV) aryloxide derivatives ClSi(OC6H3Me2-2,6)3andSi(OC6H3Me2-2,6)4 have been prepared by the reactions ofMenSiCL,. ,, or SiCl4 and an appropriate substituted phenol in desired molar ratios using pyridine as proton acceptor in benzene. The reactions of Me3Si(OAr)(OAr = OC6H3Me2-3,5 or OC6H,Me2-3,4) with TiCl4 (or NbCl5) in 1:1 and 1:2 molar ratios have produced corresponding mono-and bis-(aryloxo) derivatives of Ti(IV) and Nb(V) respectively. All these derivatives have been characterized by elemental analysis, molecular weight measurements and spectral (IR and multinuclear NMR) studies. Introduction Considerable amount of work have been carried out on silicon alkoxides" including their use in the preparation of glass ceramic materials by the Sol-Gel· technique, by contrast only a few simple· and sterically demanding silicon (IV) and organosilicon (IV) aryloxides have been described so far and for none of these Si NMR data are yet available. We therefore, report in this paper the synthesis, chemistry, and spectral characterization of substituted phenoxide derivatives containing organosilicon (IV) and silicon (IV) moieties. Results and Discussion Organosiliconand silicon (IV) aryloxides have been prepared by the reactions of the corresponding chlorosilanes with an appropriate substituted phenol in desired molar ratios in the presence of pyridine in benzene:
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation

Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years, convolutional encoder-decoder solutions have achieved substantial progress in the field of automatic medical image segmentation. Due to the inherent bias in the convolution operations, prior models mainly focus on local visual cues formed by the neighboring pixels, but fail to fully model the long-range contextual dependencies. In this article, we propose a novel Transformer-based Attention Guided Network called TransAttUnet, in which the multi-level guided attention and multi-scale skip connection are designed to jointly enhance the performance of the semantical segmentation architecture. Inspired by Transformer, the self-aware attention (SAA) module with Transformer Self Attention (TSA) and Global Spatial Attention (GSA) is incorporated into TransAttUnet to effectively learn the non-local interactions among encoder features. Moreover, we also use additional multi-scale skip connections between decoder blocks to aggregate the upsampled features with different semantic scales. In this way, the representation ability of multi-scale context information is strengthened to generate discriminative features. Benefitting from these complementary components, the proposed TransAttUnet can effectively alleviate the loss of fine details caused by the stacking of convolution layers and the consecutive sampling operations, finally improving the segmentation quality of medical images. Extensive experiments were conducted on multiple medical image segmentation datasets from various imaging modalities, which demonstrate that the proposed method consistently outperforms the existing state-of-the-art methods.
Non-Binary Snow Index for Multi-Component Surfaces

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs), such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, regions in Greenland and France–Italy where snow interacts with highly diversified geographical ecosystems were examined. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites were used. The NBSI-MS performance was also compared against the well-known Normalized Difference Snow Index (NDSI), NDSII-1, S3, and Snow Water Index (SWI) methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieved an overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as the OA. The precision assessment confirmed the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.
Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training

While self-training achieves state-of-the-art results in semi-supervised object detection (SSOD), it severely suffers from foreground-background and foreground-foreground imbalances in SSOD. In this paper, we propose an Adaptive Class-Rebalancing Self-Training (ACRST) with a novel memory module called CropBank to alleviate these imbalances and generate unbiased pseudo-labels. Besides, we observe that both self-training and data-rebalancing procedures suffer from noisy pseudo-labels in SSOD. Therefore, we contribute a simple yet effective two-stage pseudo-label filtering scheme to obtain accurate supervision. Our method achieves competitive performance on MS-COCO and VOC benchmarks. When using only 1% labeled data of MS-COCO, our method achieves 17.02 mAP improvement over the supervised method and 5.32 mAP gains compared with state-of-the-arts.