Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneously Learning Robust Audio Embeddings and Balanced Hash Codes for Query-by-Example

Audio fingerprinting systems must efficiently and robustly identify query snippets in an extensive database. To this end, state-of-the-art systems use deep learning to generate compact audio fingerprints. These systems deploy indexing methods, which quantize fingerprints to hash codes in an unsupervised manner to expedite the search. However, these methods generate imbalanced hash codes, leading to their suboptimal performance. Therefore, we propose a self-supervised learning framework to compute fingerprints and balanced hash codes in an end-to-end manner to achieve both fast and accurate retrieval performance. We model hash codes as a balanced clustering process, which we regard as an instance of the optimal transport problem. Experimental results indicate that the proposed approach improves retrieval efficiency while preserving high accuracy, particularly at high distortion levels, compared to the competing methods. Moreover, our system is efficient and scalable in computational load and memory storage.
Class-Specific Attention (CSA) for Time-Series Classification

Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes; we call such features class-specific features. Existing models do not fully utilize the class-specific differences in features as they feed all extracted features from the hidden layers equally to the output layers. Recent attention mechanisms allow giving different emphasis (or attention) to different features, but these attention models are themselves class-agnostic. In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series. The CSA module is designed in a way such that it can be adopted in existing neural network (NN) based models to conduct time series classification. In the experiments, this module is plugged into five start-of-the-art neural network models for time series classification to test its effectiveness by using 40 different real datasets. Extensive experiments show that an NN model embedded with the CSA module can improve the base model in most cases and the accuracy improvement can be up to 42%. Our statistical analysis show that the performance of an NN model embedding the CSA module is better than the base NN model on 67% of MTS and 80% of UTS test cases and is significantly better on 11% of MTS and 13% of UTS test cases.
Instability in clinical risk stratification models using deep learning

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study the stability of different model architectures trained on electronic health records, using a set of outpatient prediction tasks as a case study. We show that repeated training runs of the same deep learning model on the same training data can result in significantly different outcomes at a patient level even though global performance metrics remain stable. We propose two stability metrics for measuring the effect of randomness of model training, as well as mitigation strategies for improving model stability.
Quantum correlations, local interactions and error correction

We consider the effects of local interactions upon quantum mechanically entangled systems. In particular we demonstrate that non-local correlations cannot increase through local operations on any of the subsystems, but that through the use of quantum error correction methods, correlations can be maintained. We provide two mathematical proofs that local general measurements cannot increase correlations, and also derive general conditions for quantum error correcting codes. Using these we show that local quantum error correction can preserve non-local features of entangled quantum systems. We also demonstrate these results by use of specific examples employing correlated optical cavities interacting locally with resonant atoms. By way of counter example, we also describe a mechanism by which correlations can be increased, which demonstrates the need for non-local interactions.
Disentangled Representation Learning

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controlability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, data mining etc. In this article, we comprehensively review DRL from various aspects including motivations, definitions, methodologies, evaluations, applications and model designs. We discuss works on DRL based on two well-recognized definitions, i.e., Intuitive Definition and Group Theory Definition. We further categorize the methodologies for DRL into four groups, i.e., Traditional Statistical Approaches, Variational Auto-encoder Based Approaches, Generative Adversarial Networks Based Approaches, Hierarchical Approaches and Other Approaches. We also analyze principles to design different DRL models that may benefit different tasks in practical applications. Finally, we point out challenges in DRL as well as potential research directions deserving future investigations. We believe this work may provide insights for promoting the DRL research in the community.
DualApp: Tight Over-Approximation for Neural Network Robustness Verification via Under-Approximation

—The robustness of neural networks is fundamental to the hosting system’s reliability and security. Formal veriﬁcation has been proven to be e ﬀ ective in providing provable robustness guarantees. To improve the veriﬁcation scalability, over-approximating the non-linear activation functions in neural networks by linear constraints is widely adopted, which trans- forms the veriﬁcation problem into an e ﬃ ciently solvable linear programming problem. As over-approximations inevitably intro- duce overestimation, many e ﬀ orts have been dedicated to deﬁning the tightest possible approximations. Recent studies have however showed that the existing so-called tightest approximations are superior to each other. In this paper we identify and report an crucial factor in deﬁn- ing tight approximations, namely the approximation domains of activation functions . We observe that existing approaches only rely on overestimated domains, while the corresponding tight approximation may not necessarily be tight on its actual do- main. We propose a novel under-approximation-guided approach, called dual-approximation , to deﬁne tight over-approximations and two complementary under-approximation algorithms based on sampling and gradient descent. The overestimated domain guarantees the soundness while the underestimated one guides the tightness. We implement our approach into a tool called DualApp and extensively evaluate it on a comprehensive benchmark of 84 collected and trained neural networks with di ﬀ erent architec-tures. The experimental results show that DualApp outperforms the state-of-the-art approximation-based approaches,
Discovering Locally Maximal Bipartite Subgraphs

Induced bipartite subgraphs of maximal vertex cardinality are an essential concept for the analysis of graphs. Yet, discovering them in large graphs is known to be computationally hard. Therefore, we consider in this work a weaker notion of this problem, where we discard the maximality constraint in favor of inclusion maximality. Thus, we aim to discover locally maximal bipartite subgraphs. For this, we present three heuristic approaches to extract such subgraphs and compare their results to the solutions of the global problem. For the latter, we employ the algorithmic strength of fast SAT-solvers. Our three proposed heuristics are based on a greedy strategy, a simulated annealing approach, and a genetic algorithm, respectively. We evaluate all four algorithms with respect to their time requirement and the vertex cardinality of the discovered bipartite subgraphs on several benchmark datasets.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10.
Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

Road scene understanding tasks have recently become crucial for self-driving vehicles. In particular, real-time semantic segmentation is indispensable for intelligent self-driving agents to recognize roadside objects in the driving area. As prior research works have primarily sought to improve the segmentation performance with computationally heavy operations, they require far significant hardware resources for both training and deployment, and thus are not suitable for real-time applications. As such, we propose a doubly contrastive approach to improve the performance of a more practical lightweight model for self-driving, specifically under adverse weather conditions such as fog, nighttime, rain and snow. Our proposed approach exploits both image- and pixel-level contrasts in an end-to-end supervised learning scheme without requiring a memory bank for global consistency or the pretraining step used in conventional contrastive methods. We validate the effectiveness of our method using SwiftNet on the ACDC dataset, where it achieves up to 1.34%p improvement in mIoU (ResNet-18 backbone) at 66.7 FPS (2048x1024 resolution) on a single RTX 3080 Mobile GPU at inference. Furthermore, we demonstrate that replacing image-level supervision with self-supervision achieves comparable performance when pre-trained with clear weather images.
Magnetic-monopole-induced polarons in atomic superlattices

Magnetic monopoles have been realized as emergent quasiparticles in both condensed matter and ultracold atomic platforms, with growing interests in the coupling effects between the monopole and different magnetic quasiparticles. In this work, interaction effects between monopoles and magnons are investigated for an atomic pseudospin chain. We reveal that the monopole can excite a virtual magnon cloud in the paramagnetic chain, thereby giving rise to a new type of polaron, the monopole-cored polaron (McP). The McP is composed of the monopole as the impurity core and the virtual magnon excitation as the dressing cloud. The magnon dressing facilitates the Dirac string excitation and impacts the monopole hopping. This induces an anti-trapping effect of the McP, which refers to the fact that the dressing enhances the mobility of the McP, in contrast to the self-trapping of the common polarons. Moreover, heterogeneous bipolarons are shown to exist under the simultaneous doping of a north and a south monopole. The heterogeneous bipolaron possesses an inner degree of freedom composed of two identical impurities. Our investigation sheds light on the understanding of how the coupling between the impurity core and the dressing cloud can engineer the property of the polaron
An improved threshold for the number of distinct intersections of intersecting families

A family F of subsets of { 1 , 2 , . , n } is called a t -intersecting family if | F ∩ G | ≥ t for any two members F, G ∈ F and for some positive integer t . If t = 1, then we call the family F to be intersecting. Deﬁne the set I ( F ) = { F ∩ G : F, G ∈ F and F 6 = G } to be the collection of all distinct intersections of F . Frankl et al. proved an upper bound for the size of I ( F ) of intersecting families F of k -subsets of { 1 , 2 , . , n } . Their theorem holds for integers n ≥ 50 k 2 . In this article, we prove an upper bound for the size of I ( F ) of t -intersecting families F , provided that n exceeds a certain number f ( k, t ). Along the way we also improve the threshold k 2 to k 3 / 2+ o (1) for the intersecting families.
Compositional Scene Modeling with Global Object-Centric Representations

The appearance of the same object may vary in different scene images due to perspectives and occlusions between objects. Humans can easily identify the same object, even if occlusions exist, by completing the occluded parts based on its canonical image in the memory. Achieving this ability is still a challenge for machine learning, especially under the unsupervised learning setting. Inspired by such an ability of humans, this paper proposes a compositional scene modeling method to infer global representations of canonical images of objects without any supervision. The representation of each object is divided into an intrinsic part, which characterizes globally invariant information (i.e. canonical representation of an object), and an extrinsic part, which characterizes scene-dependent information (e.g., position and size). To infer the intrinsic representation of each object, we employ a patch-matching strategy to align the representation of a potentially occluded object with the canonical representations of objects, and sample the most probable canonical representation based on the category of object determined by amortized variational inference. Extensive experiments are conducted on four object-centric learning benchmarks, and experimental results demonstrate that the proposed method not only outperforms state-of-the-arts in terms of segmentation and reconstruction, but also achieves good global object identification performance.
Experimental studies of attenuation in sediments

Recent theoretical work suggests that attenuation of acoustic waves in ocean sediments depends on two distinct types of energy loss; these losses result from inelasticity of the skeletal frame and motion of the pore water relative to the frame. In this paper, experimental evidence is presented which verifies the type of response predicted by the theoretical model. Torsional resonance and logarithmic decrement are measured over a wide frequency range in both water‐saturated and dry sediments. A changeover from dominance by one type of loss to the other is clearly demonstrated by a change of response that is observed as the frequency is varied. Specially designed equipment is used which allows the intergranular stress to be controlled to simulate various depths of embedment in the sea floor. Both the theory and the experiments suggest that for some sediments the attenuation will vary in a manner quite different from the usual dependency on the first power of frequency that is often assumed.
Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called “RNASeq” and “VariantSeq” to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, “RNASeq” and “VariantSeq” are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user’s PC under any operating system or on remote servers, as a cloud solution. “RNASeq” and “VariantSeq” are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. “RNASeq” and “VariantSeq” also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.
Spectroscopy of candidate young globular clusters in NGC 1275

We present spectra of the brightest member of the population of compact blue objects discovered in the peculiar galaxy NGC 1275 by Holtzman et al. (1992) using Hubble Space Telescope images. These spectra show strong Balmer absorption lines like those observed in A-type stars, as expected if the object is a young globular cluster. The age estimated from the strength of the Balmer lines is about 0.5 Gyr, although ages ranging from 0.1 Gyr to 0.9 Gyr cannot be confidently excluded given current models of stellar populations. If these estimated ages are adopted for the young cluster population of NGC 1275 as a whole, the fading predicted by stellar populations models gives a luminosity function which is consistent with that of the Galactic globular cluster system convolved with the observational selection function for the NGC 1275 system. We also use the equivalent widths of the Mg b and Fe 5270 features to constrain the metallicity of the young cluster. Combining these absorption-line widths with the age estimates from the Balmer lines and stellar population models, we find a metallicity of roughly solar, based on the Mg b index, and somewhat higher for the Fe 5270 index. The radial velocity of the absorption lines of the cluster spectrum is offset from the emission lines of the galaxy spectrum at the same position by - 130 km/s, providing further evidence for the identification of the object as a global cluster and opening up the future possibility of studying the kinematics of young cluster systems. The discovery of objects with the characteristics of young globular clusters in NGC 1275, which shows evidence of a recent interaction or merger, supports the hypothesis that galaxy interactions and mergers are favorable sites for the formation of globular clusters.
Skotomorphogenesis exploits threonine to promote hypocotyl elongation

Abstract Abstract Mobilisation of seed storage reserves is important for seedling establishment in Arabidopsis. In this process, sucrose is synthesised from triacylglycerol via core metabolic processes. Mutants with defects in triacylglycerol-to-sucrose conversion display short etiolated seedlings. We found that whereas sucrose content in the indole-3-butyric acid response 10 (ibr10) mutant was significantly reduced, hypocotyl elongation in the dark was unaffected, questioning the role of IBR10 in this process. To dissect the metabolic complexity behind cell elongation, a quantitative-based phenotypic analysis combined with a multi-platform metabolomics approach was applied. We revealed that triacylglycerol and diacylglycerol breakdown were disrupted in ibr10, resulting in low sugar content and poor photosynthetic ability. Importantly, batch-learning self-organised map clustering revealed that threonine level was correlated with hypocotyl length. Consistently, exogenous threonine supply stimulated hypocotyl elongation, indicating that sucrose levels are not always correlated with etiolated seedling length, suggesting the contribution of amino acids in this process.
Author Response: Oral and Topical Treatment of Painful Diabetic Polyneuropathy: Practice Guideline Update Summary: Report of the AAN Guideline Subcommittee

We are grateful for the response to this guideline summary.1 Dr. Argoff makes the important point that many patients with painful diabetic neuropathy continue to experience pain despite using the recommended medications. This highlights the need for new therapies for this common and highly morbid condition. He also points to 2 recent articles, both examining the same trial at different time points, that evaluated the efficacy of 10 kHz spinal cord stimulation (SCS) for patients with painful diabetic neuropathy.2,3 This AAN guideline was focused on oral and topical medications; therefore, devices such as SCS were not included.4 However, it should be pointed out that the recent studies of 10 kHz SCS were not blinded, which is extremely important when rating the evidence for clinical trials with pain outcomes. As a result, future studies with appropriate sham controls are needed before including these interventions in future guideline recommendations.
Study of the Improved Projective Approach and the Variable Separation Solutions in a (2+1)-Dimensional Soliton System

The projective approach is a kind of classic, efficient and well-developed method to solve nonlinear evolution equations, the remarkable characteristic of which is that we can have many different ansatzs and therefore, a large number of solutions. In this paper, with the help of the improved projective approach and a linear variable separation approach, some new variable separation solutions of the (2+1)-dimensional Generalized Calogero-Bogoyavlenskii-Schiff system (GCBS) is derived. … j i i y x x t u u u u P