Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving search relevance of Azure Cognitive Search by Bayesian optimization

Azure Cognitive Search (ACS) has emerged as a major contender in"Search as a Service"cloud products in recent years. However, one of the major challenges for ACS users is to improve the relevance of the search results for their specific usecases. In this paper, we propose a novel method to find the optimal ACS configuration that maximizes search relevance for a specific usecase (product search, document search.) The proposed solution improves key online marketplace metrics such as click through rates (CTR) by formulating the search relevance problem as hyperparameter tuning. We have observed significant improvements in real-world search call to action (CTA) rate in multiple marketplaces by introducing optimized weights generated from the proposed approach.
Nash equilibria for dividend distribution with competition

We construct a Nash equilibrium in feedback form for a class of two-person stochastic games with absorption arising from corporate finance. More precisely, the paper focusses on a strategic dynamic game in which two financially-constrained firms operate in the same market. The firms distribute dividends and are faced with default risk. The strategic interaction arises from the fact that if one firm defaults, the other one becomes a monopolist and increases its profitability. To determine a Nash equilibrium in feedback form, we develop two different concepts depending on the initial endowment of each firm. If one firm is richer than the other one, then we use a notion of control vs.\ strategy equilibrium. If the two firms have the same initial endowment (hence they are symmetric in our setup) then we need mixed strategies in order to construct a symmetric equilibrium.
BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics

The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet, existing methods are largely limited to generating body motions only without considering the rich two-hand motions, let alone handling various conditions like body dynamics or texts. To break the data bottleneck, we propose BOTH57M, a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method, BOTH2Hands, for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research.
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness. Deep Learning based systems are gradually being introduced as automated support for clinical diagnosis. Since healthcare has always been an extremely important domain demanding error-free performance, any adversaries could pose a big threat to the applicability of such systems. In this work, we use Universal Adversarial Perturbations (UAPs) to quantify the vulnerability of Medical Deep Neural Networks (DNNs) for detecting DR. To the best of our knowledge, this is the very first attempt that works on attacking complete fine-grained classification of DR images using various UAPs. Also, as a part of this work, we use UAPs to fine-tune the trained models to defend against adversarial samples. We experiment on several models and observe that the performance of such models towards unseen adversarial attacks gets boosted on average by $3.41$ Cohen-kappa value and maximum by $31.92$ Cohen-kappa value. The performance degradation on normal data upon ensembling the fine-tuned models was found to be statistically insignificant using t-test, highlighting the benefits of UAP-based adversarial fine-tuning.
Causal Integration of Multiple Cancer Cohorts with High-Dimensional Confounders: Bayesian Propensity Score Estimation

Comparative meta-analyses of patient groups by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate confounder imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional confounders. Motivated by an integrative analysis of breast cancer patients across seven medical centers, this paper tackles the challenges associated with integrating multiple observational datasets and offering nationally interpretable results. The proposed inferential technique, called Bayesian Motif Submatrices for Confounders (B-MSMC), addresses the curse of dimensionality by a hybrid of Bayesian and frequentist approaches. B-MSMC uses nonparametric Bayesian ``Chinese restaurant"processes to eliminate redundancy in the high-dimensional confounders and discover latent motifs or lower-dimensional structure. With these motifs as potential predictors, standard regression techniques can be utilized to accurately infer the PSs and facilitate causal group comparisons. Simulations and meta-analysis of the motivating cancer investigation demonstrate the efficacy of our proposal in high-dimensional causal inference by integrating multiple observational studies; using different weighting methods, we apply the B-MSMC approach to efficiently address confounding when integrating observational health studies with high-dimensional confounders.
Consprompt: Exploiting Contrastive Samples for Few-Shot Prompt Learning

The prompt has become an effective linguistic tool for utilizing pre-trained language models. However, in few-shot scenarios, subtle changes in the prompt design always make the result widely different, and the prompt learning methods also make it easy to overfit the limited samples. To alleviate this, we explore utilizing suitable contrastive samples and multi-degree contrastive learning methods to improve the robustness of the prompt representation. Therefore, the proposed Consprompt combined with the prompt encoding network, contrastive sampling modules, and contrastive scoring modules, is introduced to realize differential contrastive learning. Our results exhibit state-of-the-art performance in different few-shot settings, and the ablation experiments also certify the effectiveness of utilizing multi-degree contrastive learning in the prompt-based fine-tuning process.
Operadic Dold-Kan correspondence and mapping spaces between enriched operads

According to the Dold-Kan correspondence, the normalization functor determines a categorical equivalence between simplicial abelian groups and non-negatively graded chain complexes. Nevertheless, the same statement is no longer true when extending to the context of algebras over an operad. In spite of this, the existence of the Dold-Kan correspondence for operadic algebras can still be realized from a homotopy-theoretic perspective. This was indeed observed for $A_\infty$-algebras by Schwede-Shipley, and for $E_\infty$-algebras by Mandell, and more generally, for algebras over any $\Sigma$-cofibrant operad due to White-Yau. In the first part of the paper, we shall prove further that the normalization functor induces an equivalence between the $\infty$-categories of operads with non-fixed sets of colors, in the Dwyer-Kan homotopy theory framework. Subsequently, we delve into investigating the mapping spaces between enriched operads by relating them to mapping spaces between operadic bimodules via nice fiber sequences. The validity of the main statements is examined first for simplicial operads, and then for connective dg operads due to the operadic Dold-Kan correspondence.
Probing Cosmological Principle using the spectral index of quasar flux distribution

We study the dipole signal in the spectral index (x) of the differential number counts using quasars in the CatWISE2020 catalog of infrared sources. The index is extracted by using the log-likelihood method. We obtain the value $x=1.579 \pm 0.001$ for a quasar sample of 1355352 sources. We extract the dipole signal in this parameter by employing $\chi^{2}$ minimization, assuming a sky model of x up to the quadrupole term. We find that the dipole amplitude |D| is 0.005 \pm 0.002 and dipole direction (l, b) in Galactic coordinate system equal to $(201.50^{\circ} \pm 27.87^{\circ}, -29.37^{\circ} \pm 19.86^{\circ})$. The direction of dipole anisotropy is found to be very close to the hemispherical power asymmetry $(l,b)=(221^\circ,-27^{\circ})$ in the Cosmic Microwave Background (CMB). We also obtain a signal of quadrupole anisotropy which is correlated with the ecliptic poles and can be attributed to ecliptic bias. }
C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation

To find the geolocation of a street-view image, cross-view geolocalization (CVGL) methods typically perform image retrieval on a database of georeferenced aerial images and determine the location from the visually most similar match. Recent approaches focus mainly on settings where street-view and aerial images are preselected to align w.r. t. translation or orientation, but struggle in challenging real-world scenarios where varying camera poses have to be matched to the same aerial image. We propose a novel trainable retrieval architecture that uses bird's eye view (BEV) maps rather than vectors as embedding representation, and explicitly addresses the many-to-one ambiguity that arises in real-world scenarios. The BEV-based retrieval is trained using the same contrastive setting and loss as classical retrieval. Our method C-BEV surpasses the state-of-the-art on the retrieval task on multiple datasets by a large margin. It is particularly effective in challenging many-to-one scenarios, e.g. increasing the top-1 recall on VIGOR's cross-area split with unknown orientation from 31.1% to 65.0%. Although the model is supervised only through a contrastive objective applied on image pairings, it additionally learns to infer the 3-DoF camera pose on the matching aerial image, and even yields a lower mean pose error than recent methods that are explicitly trained with metric groundtruth.
Multimodal Sentiment Analysis: Perceived vs Induced Sentiments

Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering a visually engaging way for users to communicate. In this research, we propose a multimodal framework that integrates visual and textual features to predict the GIF sentiment. It also incorporates attributes including face emotion detection and OCR generated captions to capture the semantic aspects of the GIF. The developed classifier achieves an accuracy of 82.7% on Twitter GIFs, which is an improvement over state-of-the-art models. Moreover, we have based our research on the ReactionGIF dataset, analysing the variance in sentiment perceived by the author and sentiment induced in the reader
Levenshtein Distance Embedding with Poisson Regression for DNA Storage

Efficient computation or approximation of Levenshtein distance, a widely-used metric for evaluating sequence similarity, has attracted significant attention with the emergence of DNA storage and other biological applications. Sequence embedding, which maps Levenshtein distance to a conventional distance between embedding vectors, has emerged as a promising solution. In this paper, a novel neural network-based sequence embedding technique using Poisson regression is proposed. We first provide a theoretical analysis of the impact of embedding dimension on model performance and present a criterion for selecting an appropriate embedding dimension. Under this embedding dimension, the Poisson regression is introduced by assuming the Levenshtein distance between sequences of fixed length following a Poisson distribution, which naturally aligns with the definition of Levenshtein distance. Moreover, from the perspective of the distribution of embedding distances, Poisson regression approximates the negative log likelihood of the chi-squared distribution and offers advancements in removing the skewness. Through comprehensive experiments on real DNA storage data, we demonstrate the superior performance of the proposed method compared to state-of-the-art approaches.
Polynomial-based Self-Attention for Table Representation learning

Structured data, which constitutes a significant portion of existing data types, has been a long-standing research topic in the field of machine learning. Various representation learning methods for tabular data have been proposed, ranging from encoder-decoder structures to Transformers. Among these, Transformer-based methods have achieved state-of-the-art performance not only in tabular data but also in various other fields, including computer vision and natural language processing. However, recent studies have revealed that self-attention, a key component of Transformers, can lead to an oversmoothing issue. We show that Transformers for tabular data also face this problem, and to address the problem, we propose a novel matrix polynomial-based self-attention layer as a substitute for the original self-attention layer, which enhances model scalability. In our experiments with three representative table learning models equipped with our proposed layer, we illustrate that the layer effectively mitigates the oversmoothing problem and enhances the representation performance of the existing methods, outperforming the state-of-the-art table representation methods.
Projective Parallel Single-Pixel Imaging: 3D Structured Light Scanning Under Global Illumination

We present projective parallel single-pixel imaging (pPSI), a 3D photography method that provides a robust and efficient way to analyze the light transport behavior and enables separation of light effect due to global illumination, thereby achieving 3D structured light scanning under global illumination. The light transport behavior is described by the light transport coefficients (LTC), which contain complete information for a projector camera pair, and is a 4D data set. However, the capture of LTC is generally time consuming. The 4D LTC in pPSI are reduced to projection functions, thereby enabling a highly efficient data capture process. We introduce the local maximum constraint, which provides constraint for the location of candidate correspondence matching points when projections are captured. Local slice extension (LSE) method is introduced to accelerate the capture of projection functions. Optimization is conducted for pPSI under several situations. The number of projection functions required for pPSI is optimized and the influence of capture ratio in LSE on the accuracy of the correspondence matching points is investigated. Discussions and experiments include two typical kinds of global illuminations: inter-reflections and subsurface scattering. The proposed method is validated with several challenging scenarios, and outperforms the state-of-the-art methods.
Machine learning–driven self-discovery of the robot body morphology

The morphology of a robot is typically assumed to be known, and data from external measuring devices are used mainly for its kinematic calibration. In contrast, we take an agent-centric perspective and ponder the vaguely explored question of whether a robot could learn elements of its morphology by itself, relying on minimal prior knowledge and depending only on unorganized proprioceptive signals. To answer this question, we propose a mutual information–based representation of the relationships between the proprioceptive signals of a robot, which we call proprioceptive information graphs (π-graphs). Leveraging the fact that the information structure of the sensorimotor apparatus is dependent on the embodiment of the robot, we use the π-graph to look for pairwise signal relationships that reflect the underlying kinematic first-order principles applicable to the robot’s structure. In our discussion, we show that analysis of the π-graph leads to the inference of two fundamental elements of the robot morphology: its mechanical topology and corresponding kinematic description, that is, the location and orientation of the robot’s joints. Results from a robot manipulator, a hexapod, and a humanoid robot show that the correct topology and kinematic description can be effectively inferred from their π-graph either offline or online, regardless of the number of links and body configuration. Robots learn their topology and kinematics from proprioception using a graph representation based on mutual information.
DNDI-6174, a preclinical candidate for visceral leishmaniasis that targets the cytochrome bc1 complex

New drugs for visceral leishmaniasis that are safe, low cost, and adapted to the field are urgently required. Despite concerted efforts over the last several years, the number of new chemical entities that are suitable for clinical development for the treatment of Leishmania remains low. Here, we describe the discovery and preclinical development of DNDI-6174, an inhibitor of Leishmania cytochrome bc1 complex activity that originated from a phenotypically identified pyrrolopyrimidine series. This compound fulfills all target candidate profile criteria required for progression into preclinical development. In addition to good metabolic stability and pharmacokinetic properties, DNDI-6174 demonstrates potent in vitro activity against a variety of Leishmania species and can reduce parasite burden in animal models of infection, with the potential to approach sterile cure. No major flags were identified in preliminary safety studies, including an exploratory 14-day toxicology study in the rat. DNDI-6174 is a cytochrome bc1 complex inhibitor with acceptable development properties to enter preclinical development for visceral leishmaniasis. We report the characterization of a Leishmania cytochrome bc1 complex inhibitor with potential for treating leishmaniasis. Editor’s summary Chagas disease (CD) and visceral leishmaniasis are considered neglected tropical diseases and are responsible for ~30,000 fatalities annually. Current treatments for these vector-borne parasitic infections are inadequate, and there is a pressing need for better therapeutics. In companion articles, González et al. and Braillard et al. describe the development of closely related pyrrolopyrimidine compounds that target the cytochrome bc1 complex of the parasite electron transport chain and show promise for the treatment of both diseases. DNDI-6174 demonstrates potent activity in Leishmania models of infection and has a suitable profile for clinical development. For CD, the combination of pyrrolopyrimidine compounds with a subefficacious dose of benznidazole delivers rapid clearance of parasites in an animal model of chronic CD, offering the prospect of safer and shorter treatment regimens. Collectively, these studies represent advances in the development of potential treatments for some of the world’s most neglected patient populations. —Christiana Fogg
Unraveling the Impact of Sperm DNA Fragmentation on Reproductive Outcomes

Abstract In recent years, there has been a growing interest in identifying subcellular causes of male infertility, and sperm DNA fragmentation (SDF) research has been at the forefront of this focus. DNA damage can occur during spermatogenesis due to faulty chromatin compaction or excessive abortive apoptosis. It can also happen as sperm transit through the genital tract, often induced by oxidative stress. There are several methods for SDF testing, with the sperm chromatin structure assay, terminal deoxynucleotidyl transferase d-UTI nick end labeling (TUNEL) assay, comet assay, and sperm chromatin dispersion test being the most commonly used. Numerous studies strongly support the negative impact of SDF on male fertility potential. DNA damage has been linked to various morphological and functional sperm abnormalities, ultimately affecting natural conception and assisted reproductive technology outcomes. This evidence-based review aims to explore how SDF influences male reproduction and provide insights into available therapeutic options to minimize its detrimental impact.
Draft genome sequences of two thermophilic, spore-forming Aeribacillus pallidus strains isolated from dairy products

ABSTRACT The presence of thermophilic spore-forming bacteria is challenging in industrial food processing. The presented genome sequences of Aeribacillus pallidus, isolated from raw milk and cocoa powder, provide insights into how to prevent damage to minimally processed foods and products with extended shelf life, such as milk products.
Effectiveness, safety and costs of the FreeStyle Libre glucose monitoring system for children and adolescents with type 1 diabetes in Spain: a prospective, uncontrolled, pre-post study

Objectives This study aimed to evaluate the effectiveness, safety and costs of FreeStyle Libre (FSL) glucose monitoring system for children and adolescents with type 1 diabetes mellitus (T1DM) in Spain. Design Prospective, multicentre pre-post study. Setting Thirteen Spanish public hospitals recruited patients from January 2019 to March 2020, with a 12-month follow-up. Participants 156 patients were included. Primary and secondary outcome measures Primary: glycated haemoglobin (HbA1c) change. Secondary: severe hypoglycaemic events (self-reported and clinical records), quality of life, diabetes treatment knowledge, treatment satisfaction, adverse events, adherence, sensor usage time and scans. Healthcare resource utilisation was assessed for cost analysis from the National Health System perspective, incorporating direct healthcare costs. Data analysis used mixed regression models with repeated measures. The intervention’s total cost was estimated by multiplying health resource usage with unit costs. Results In the whole sample, HbA1c increased significantly (0.32%; 95% CI 0.10% to 0.55%). In the subgroup with baseline HbA1c≥7.5% (n=88), there was a significant reduction at 3 months (−0.46%; 95% CI −0.69% to −0.23%), 6 months (−0.49%; 95% CI −0.73% to −0.25%) and 12 months (−0.43%; 95% CI −0.68% to –0.19%). Well-controlled patients had a significant 12-month worsening (0.32%; 95% CI 0.18% to 0.47%). Self-reported severe hypoglycaemia significantly decreased compared with the previous year for the whole sample (−0.37; 95% CI −0.62 to –0.11). Quality of life and diabetes treatment knowledge showed no significant differences, but satisfaction increased. Adolescents had lower sensor usage time and scans than children. Reduction in HbA1c was significantly associated with device adherence. No serious adverse effects were observed. Data suggest that use of FSL could reduce healthcare resource use (strips and lancets) and costs related to productivity loss. Conclusions The use of FSL in young patients with T1DM was associated with a significant reduction in severe hypoglycaemia, and improved HbA1c levels were seen in patients with poor baseline control. Findings suggest cost savings and productivity gains for caregivers. Causal evidence is limited due to the study design. Further research is needed to confirm results and assess risks, especially for patients with lower baseline HbA1c.