Miracle-dz/UniFAR-FTUs huggingface dataset:
AugShuffleNet: Communicate More, Compute Less

As a remarkable compact model, ShuffleNetV2 offers a good example to design efficient ConvNets but its limit is rarely noticed. In this paper, we rethink the design pattern of ShuffleNetV2 and find that the channel-wise redundancy problem still constrains the efficiency improvement of Shuffle block in the wider ShuffleNetV2. To resolve this issue, we propose another augmented variant of shuffle block in the form of bottleneck-like structure and more implicit short connections. To verify the effectiveness of this building block, we further build a more powerful and efficient model family, termed as AugShuffleNets. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShuffleNet consistently outperforms ShuffleNetV2 in terms of accuracy with less computational cost and fewer parameter count.
Predicting the Biological Classification of Cell-Cycle Regulated Genes of Saccharomyces cerevisiae using Community Detection Algorithms on Gene Co-expression Networks

The conventional approach for analyzing gene expression data involves clustering algorithms. Cluster analyses provide partitioning of the set of genes that can predict biological classification based on its similarity in n-dimensional space. In this study, we investigate whether network analysis will provide an advantage over the traditional approach. We identify the advantages and disadvantages of using the value-based and the rank-based construction in creating a graph representation of the original gene-expression data in a time-series format. We tested four community detection algorithms, namely, the Clauset-Newman-Moore (greedy), Louvain, Leiden, and Girvan-Newman algorithms in predicting the 5 functional groups of genes. We used the Adjusted Rand Index to assess the quality of the predicted communities with respect to the biological classifications. We showed that Girvan-Newman outperforms the 3 modularity-based algorithms in both value-based and ranked-based constructed graphs. Moreover, we also show that when compared to the conventional clustering algorithms such as K-means, Spectral, Birch, and Agglomerative algorithms, we obtained a higher ARI with Girvan-Newman. This study also provides a tool for graph construction, visualization, and community detection for further analysis of gene expression data.
Kupczynski’s contextual setting-dependent parameters offer no escape from Bell-CHSH

In a sequence of papers, Marian Kupczynski has argued that Bell’s theorem can be circumvented if one takes correct account of contextual setting-dependent parameters describing measuring instruments. We show that this is not true. Taking account of such parameters in the way he suggests, the Bell-CHSH inequality can still be derived. Violation thereof by quantum mechanics cannot be easily explained away: quantum mechanics and local realism (including Kupczynksi’s expanded concept of local realism) are not compatible with one another. Further inspection shows that Kupczynski is actually falling back on the detection loophole. Since 2015, numerous loophole-free experiments have been performed, in which the Bell-CHSH inequality is violated, so despite any other possible imperfections of such experiments, Kupczynski’s escape route for local realism is not available.
Rethinking Knowledge Distillation via Cross-Entropy

Knowledge Distillation (KD) has developed extensively and boosted various tasks. The classical KD method adds the KD loss to the original cross-entropy (CE) loss. We try to decompose the KD loss to explore its relation with the CE loss. Surprisingly, we find it can be regarded as a combination of the CE loss and an extra loss which has the identical form as the CE loss. However, we notice the extra loss forces the student's relative probability to learn the teacher's absolute probability. Moreover, the sum of the two probabilities is different, making it hard to optimize. To address this issue, we revise the formulation and propose a distributed loss. In addition, we utilize teachers' target output as the soft target, proposing the soft loss. Combining the soft loss and the distributed loss, we propose a new KD loss (NKD). Furthermore, we smooth students' target output to treat it as the soft target for training without teachers and propose a teacher-free new KD loss (tf-NKD). Our method achieves state-of-the-art performance on CIFAR-100 and ImageNet. For example, with ResNet-34 as the teacher, we boost the ImageNet Top-1 accuracy of ResNet18 from 69.90% to 71.96%. In training without teachers, MobileNet, ResNet-18 and SwinTransformer-Tiny achieve 70.04%, 70.76%, and 81.48%, which are 0.83%, 0.86%, and 0.30% higher than the baseline, respectively. The code is available at https://github.com/yzd-v/cls_KD.
Generalized Relative Neighborhood Graph (GRNG) for Similarity Search

Similarity search is a fundamental building block for information retrieval on a variety of datasets. The notion of a neighbor is often based on binary considerations, such as the k nearest neighbors. However, considering that data is often organized as a manifold with low intrinsic dimension, the notion of a neighbor must recognize higher-order relationship, to capture neighbors in all directions. Proximity graphs, such as the Relative Neighbor Graphs (RNG), use trinary relationships which capture the notion of direction and have been successfully used in a number of applications. However, the current algorithms for computing the RNG, despite widespread use, are approximate and not scalable. This paper proposes a novel type of graph, the Generalized Relative Neighborhood Graph (GRNG) for use in a pivot layer that then guides the efficient and exact construction of the RNG of a set of exemplars. It also shows how to extend this to a multi-layer hierarchy which significantly improves over the state-of-the-art methods which can only construct an approximate RNG.
Markovian queues with Poisson control

We investigate Markovian queues that are examined by a controller at random times determined by a Poisson process. Upon examination, the controller sets the service speed to be equal to the minimum of the current number of customers in the queue and a certain maximum service speed; this service speed prevails until the next examination time. We study the resulting two-dimensional Markov process of queue length and server speed, in particular two regimes with time scale separation, specifically for infinitely frequent and infinitely long examination times. In the intermediate regime the analysis proves to be extremely challenging. To gain further insight into the model dynamics we then analyse two variants of the model in which the controller is just an observer and does not change the speed of the server.
MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a personal device (laptop, smart phone, etc.) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios–-users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.
A biologically-inspired evaluation of molecular generative machine learning

Elizaveta Vinogradova1,2†, Abay Artykbayev3†, Alisher Amanatay3†, Mukhamejan Karatayev3, Maxim Mametkulov3, Albina Li3, Anuar Suleimenov3, Abylay Salimzhanov3, Karina Pats1,2, Rustam Zhumagambetov3, Ferdinand Molnár,1*, Vsevolod Peshkov,4* and Siamac Fazli,3* 1*Department of Biology, Nazarbayev University, Nur-Sultan, Kazakhstan. 2*Computer Technologies Laboratory, ITMO University, St. Petersburg, Russia. 3*Department of Computer Science, Nazarbayev University, Nur-Sultan, Kazakhstan. 4*Department of Chemistry, Nazarbayev University, Nur-Sultan, Kazakhstan.
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network

Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. As a special kind of graph data, the tree has a simpler data structure and can provide rich hierarchical information for text classification. Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification. Specifically, we first establish a dependency parsing graph for each text. Then we designed a structural entropy minimization algorithm to decode the key information in the graph and convert each graph to its corresponding coding tree. Based on the hierarchical structure of the coding tree, the representation of the entire graph is obtained by updating the representation of non-leaf nodes in the coding tree layer by layer. Finally, we present the effectiveness of hierarchical information in text classification. Experimental results show that HINT outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters.
An Unusual Reservoir of Water Emission in the VV CrA A Protoplanetary Disk

We present an analysis of an unusual pattern of water vapor emission from the ∼2 Myr old low-mass binary system VV CrA, as observed in IR spectra obtained with VLT-CRIRES, VLT-VISIR, and Spitzer-IRS. Each component of the binary shows emission from water vapor in both the L (∼3 μm) and N (∼12 μm) bands. The N-band and Spitzer spectra are similar to those previously observed from young stars with disks and are consistent with emission from an extended protoplanetary disk. Conversely, the CRIRES L-band data of VV CrA A show an unusual spectrum, which requires the presence of a water reservoir with high temperature (T ≳ 1500 K), column density (N H2O ∼ 3 × 1020 cm−2), and turbulent broadening (v ∼ 10 km s−1) but very small emitting area (A ≲ 0.005 au2). Similarity to previously observed water emission from V1331 Cyg and SVS 13 suggests that the presence of such a reservoir may be linked to evolutionary state, perhaps related to the presence of high accretion rates or winds. While the inner disk may harbor such a reservoir, simple Keplerian models do not match well with emitting line shapes, and alternative velocity fields must be considered. We also present a new idea, that the unusual emission could arise in a circumplanetary disk, embedded within the larger VV CrA A protoplanetary disk. Additional data are likely required to determine the true physical origin of this unusual spectral pattern.
Gaseous Flow in Microconduits with Viscous Dissipation

Heat transfer analysis of two-dimensional, incompressible, constant property, hydrodynamically developed, thermally developing, single phase laminar flow in microtubes and microchannels between parallel plates with negligible axial conduction is performed for constant wall temperature and constant wall heat flux thermal boundary conditions for slip flow regime. Fully developed velocity profile is determined analytically, and energy equation is solved by transforming the steady-state problem into a transient problem then by the use of the finite difference scheme for both of the geometries. The rarefaction effect which is important for flow in low pressures or flow in microchannels is imposed to the boundary conditions of the momentum and energy equations. The viscous dissipation term which is important for high speed flows or flows in long pipelines is included in the energy equation. The effects of rarefaction and viscous heating on local Nusselt number are discussed. The results of the numerical method are verified with the exact solutions of the flow in macrochannels (i.e. Kn = 0, Br = 0) and with the available analytical results of flow in micro channels for simplified cases. The results show significant deviations from the flow in macrochannels.
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Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings

Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help model polysemy. However, context sensitivity also leads to the variance of representations, which may break the semantic consistency for synonyms. Previous works that investigate contextualized sensitivity focus on the token level representations, while we are taking a deeper dive into exploring representations at the fine-grained sense level. In particular, we quantify how much the contextualized embeddings of each word sense vary across contexts in typical pre-trained models, the results show that contextualized embeddings can be highly consistent across contexts, even for two different words with the same sense. In addition, part-of-speech, number of word senses, and sentence length have an influence on the variance of sense representations. Interestingly, we find that word representations are position-biased, where the first words in different contexts tend to be more similar. We analyze such a phenomenon and also propose a prompt-augmentation method to alleviate such bias in distance-based word sense disambiguation settings. Finally, we investigate the influence of sense-level pre-training on the performance of different downstream tasks, results show that such external tasks can improve the sense- and syntactic-related tasks, while not necessarily benefiting general language understanding tasks.
On the non-efficient PAC learnability of acyclic conjunctive queries

This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not eﬃciently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size ﬁtting property , a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of “acyclicity”; (iii) we show that CQs are eﬃciently PAC learnable with membership queries.
Effectiveness of Function Matching in Driving Scene Recognition

Knowledge distillation is an effective approach for training compact recognizers required in autonomous driving. Recent studies on image classification have shown that matching student and teacher on a wide range of data points is critical for improving performance in distillation. This concept (called function matching) is suitable for driving scene recognition, where generally an almost infinite amount of unlabeled data are available. In this study, we experimentally investigate the impact of using such a large amount of unlabeled data for distillation on the performance of student models in structured prediction tasks for autonomous driving. Through extensive experiments, we demonstrate that the performance of the compact student model can be improved dramatically and even match the performance of the large-scale teacher by knowledge distillation with massive unlabeled data.
Turing Machines with Two-level Memory: A Deep Look into the Input/Output Complexity

The input/output complexity, which is the complexity of data exchange between the main memory and the external memory, has been elaborately studied by a lot of former researchers. However, the existing works failed to consider the input/output complexity in a computation model point of view. In this paper we remedy this by proposing three variants of Turing machine that include external memory and the mechanism of exchanging data between main memory and external memory. Based on these new models, the input/output complexity is deeply studied. We discussed the relationship between input/output complexity and the other complexity measures such as time complexity and parameterized complexity, which is not considered by former researchers. We also define the external access trace complexity, which reflects the physical behavior of magnetic disks and gives a theoretical evidence of IO-efficient algorithms.
Last-Iterate Convergence with Full- and Noisy-Information Feedback in Two-Player Zero-Sum Games

The theory of learning in games is prominent in the AI community, motivated by several rising applications such as multi-agent reinforcement learning and Generative Adversarial Networks. We propose Mutation-driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and prove that it exhibits the last-iterate convergence property in both full-and noisy-information feedback settings. In the full-information feedback setting, the players observe their exact gradient vectors of the utility functions. On the other hand, in the noisy-information feedback setting, they can only observe the noisy gradient vectors. Existing algorithms, including the well-known Multiplicative Weights Update (MWU) and Optimistic MWU (OMWU) algorithms, fail to converge to a Nash equilibrium with noisy-information feedback. In contrast, M2WU exhibits the last-iterate convergence to a stationary point near a Nash equilibrium in both of the feedback settings. We then prove that it converges to an exact Nash equilibrium by adapting the mutation term iteratively. We empirically conﬁrm that M2WU outperforms MWU and OMWU in exploitability and convergence rates.
The Double White Dwarf Merger Progenitors of SDSS J2211+1136 and ZTF J1901+1458

Double white dwarf (DWD) mergers are possibly the leading formation channel of massive, rapidly rotating, high-field magnetic white dwarfs (HFMWDs). However, a direct link connecting a DWD merger to any observed HFMWD is still missing. We here show that the HFMWDs SDSS J221141.80+113604.4 (hereafter J2211+1136) and ZTF J190132.9+145808.7 (hereafter J1901+1458) might be DWD merger products. J2211+1136 is a 1.27 M ⊙ white dwarf (WD) with a rotation period of 70.32 s and a surface magnetic field of 15 MG. J1901+1458 is a 1.327–1.365 M ⊙ WD with a rotation period of 416.20 s, and a surface magnetic field in the range 600–900 MG. With the assumption of single-star evolution and the currently measured WD masses and surface temperatures, the cooling ages of J2211+1136 and J1901+1458 are, respectively, 2.61–2.85 Gyr and 10–100 Myr. We hypothesize that these WDs are DWD merger products and compute the evolution of the postmerged configuration formed by a central WD surrounded by a disk. We show that the postmerger system evolves through three phases depending on whether accretion, mass ejection (propeller), or magnetic braking dominates the torque onto the central WD. We calculate the time the WD spends in each of these phases and obtain the accretion rate and disk mass for which the WD rotational age, i.e., the total time elapsed since the merger to the instant where the WD central remnant reaches the current measured rotation period, agrees with the estimated WD cooling age. We infer the mass values of the primary and secondary WD components of the DWD merger that lead to a postmerger evolution consistent with the observations.
gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus

We design a user-friendly and scalable knowledge graph construction (KGC) system for extracting structured knowledge from the unstructured corpus. Different from existing KGC systems, gBuilder provides a flexible and user-defined pipeline to embrace the rapid development of IE models. More built-in template-based or heuristic operators and programmable operators are available for adapting to data from different domains. Furthermore, we also design a cloud-based self-adaptive task scheduling for gBuilder to ensure its scalability on large-scale knowledge graph construction. Experimental evaluation demonstrates the ability of gBuilder to organize multiple information extraction models for knowledge graph construction in a uniform platform, and confirms its high scalability on large-scale KGC tasks.
Are Dipolarization Fronts a Typical Feature of Magnetotail Plasma Jets Fronts?

Plasma jets are ubiquitous in the Earth's magnetotail. Plasma jet fronts (JFs) are a seat of particle acceleration and energy conversion. JFs are commonly associated with dipolarization fronts (DFs), which are often characterized by solitary sharp large‐amplitude increases in the northward component of the magnetic field Bz. However, MHD and kinetic instabilities can develop at JFs and disturb the front structure which questions on the occurrence of solitary DFs at the JFs. We investigate the structure of JFs using 5 years (2017–2021) of the Magnetospheric Multiscale observations in the central plasma sheet (CPS) in the Earth's magnetotail. We compiled a database of 2394 CPS jets. We find that 42% of the JFs are associated with large‐amplitude changes in Bz. However, solitary DFs constitute a quarter of these large‐amplitude events, while the rest are associated with more complex structures.
Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias

Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project, different machine learning techniques are compared from various sources to find one that provides not only high accuracy but also low latency and memory overhead to be used in real-world health care systems.
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation

Unsupervised domain adaptation (UDA) is an approach to minimizing the domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images, which improves the performance of the downstream tasks, for example, cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this letter, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL) and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks. Source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG.