Miracle-dz/UniFAR-FTUs huggingface dataset:
Engineering Compact Data Structures for Rank and Select Queries on Bit Vectors

Bit vectors are fundamental building blocks of many succinct data structures. They can be used to represent graphs, are an important part of many text indices in the form of the wavelet tree, and can be used to encode ordered sequences of integers as Elias-Fano codes. To do so, two queries have to be answered: namely rank and select queries. Given a position in the bit vector, a rank query returns the number of 1-bits before that position. A select query, given a parameter $j$, returns the position of the $j$-th 1-bit. On a length-n bit vector, both queries can be answered in $O(1)$ time and require $o(n)$ bits of additional space. In practice, the smallest (uncompressed) rank and select data structure cs-poppy has a space overhead of $\approx$ 3.51% [Zhou et al., SEA 13]. In this paper, we present an improved rank and select data structure that has the same space overhead but can answer queries up to 8% (rank) and 16.5% (select) faster compared with cs-poppy.
Dataset Distillation using Neural Feature Regression

Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset. Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the meta-dataset and the inner loop trains a model on the distilled data. Meta-gradient computation is one of the key challenges in this formulation, as differentiating through the inner loop learning procedure introduces significant computation and memory costs. In this paper, we address these challenges using neural Feature Regression with Pooling (FRePo), achieving the state-of-the-art performance with an order of magnitude less memory requirement and two orders of magnitude faster training than previous methods. The proposed algorithm is analogous to truncated backpropagation through time with a pool of models to alleviate various types of overfitting in dataset distillation. FRePo significantly outperforms the previous methods on CIFAR100, Tiny ImageNet, and ImageNet-1K. Furthermore, we show that high-quality distilled data can greatly improve various downstream applications, such as continual learning and membership inference defense. Please check out our webpage at https://sites.google. com/view/frepo.
The BRST Double Complex for the Coupling of Gravity to Gauge Theories

We consider (eﬀective) Quantum General Relativity coupled to the Standard Model (QGR-SM) and study the corresponding BRST double complex. This double complex is generated by inﬁnitesimal diﬀeomorphisms and inﬁnitesimal gauge transformations. To this end, we deﬁne the respective BRST diﬀerentials P and Q and their corresponding anti-BRST diﬀerentials P and Q . Then we show that all diﬀerentials mutually anticommute, which allows us to introduce the total BRST diﬀerential D := P + Q and the total anti-BRST diﬀerential D := P + Q . Furthermore, we characterize all non-constant Lagrange densities that are essentially closed with respect to the diﬀeomorphism diﬀerentials P and P as scalar tensor densities of weight w = 1. As a consequence thereof, we obtain that graviton-ghosts decouple from matter if the Yang–Mills theory gauge ﬁxing and ghost Lagrange densities are tensor densities of weight w = 1. In particular, we show that every such Lagrange density can be modiﬁed uniquely to satisfy said condition. Finally, we introduce a total gauge ﬁxing fermion Υ and show that we can generate the complete gauge ﬁxing and ghost Lagrange densities of QGR-SM via D Υ.
Strain-driven valley states and phase transitions in Janus VSiGeN4 monolayer

Manipulating the valley degree of freedom is an important target of valleytronics development, which provides remarkable opportunities for both fundamental research and practical applications. Here, based on first-principles calculations, we demonstrate the intrinsic valley-polarized quantum anomalous Hall effect in a monolayer ferrovalley material: Janus VSiGeN4, of which the edge states are chiral-spin-valley locking. Furthermore, a small tensile or compressive strain can drive phase transition in the material from the valley-polarized quantum anomalous Hall state to the half-valley-metal state. With the increase in the strain, the material turns into a ferrovalley semiconductor with the valley anomalous Hall effect. The origin of the phase transition is the sequent band inversion of the V d orbital at the K valleys. Moreover, we find that phase transition causes the sign reversal of the Berry curvature and induces different polarized light absorption in different valley states. Our work provides an ideal material platform for practical applications and experimental exploration of the interplay among topology, spintronics, and valleytronics.
Bayesian Inference of Stochastic Dynamical Networks

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, in this paper, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. For this reason, we consider CT models since discrete-time approximations often require fine-grained measurements and uniform sampling steps. The developed method applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank-Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The convergence property of the developed method is robust to the dimension of data sources. Monte Carlo simulations indicate that the developed method outperforms state-of-the-art methods including group sparse Bayesian learning (GSBL), BINGO, kernel-based methods, dynGENIE3, GENIE3, and ARNI. The simulations include random and ring networks, and a synthetic biological network. These are challenging networks, suggesting that the developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks, and communication systems.
Mask-Guided Divergence Loss Improves the Generalization and Robustness of Deep Neural Network

Deep neural network (DNN) with dropout can be regarded as an ensemble model consisting of lots of sub-DNNs (i.e., an ensemble sub-DNN where the sub-DNN is the remaining part of the DNN after dropout), and through increasing the diversity of the ensemble sub-DNN, the generalization and robustness of the DNN can be effectively improved. In this paper, a mask-guided divergence loss function (MDL), which consists of a cross-entropy loss term and an orthogonal term, is proposed to increase the diversity of the ensemble sub-DNN by the added orthogonal term. Particularly, the mask technique is introduced to assist in generating the orthogonal term for avoiding overfitting of the diversity learning. The theoretical analysis and extensive experiments on 4 datasets (i.e., MNIST, FashionMNIST, CIFAR10, and CIFAR100) manifest that MDL can improve the generalization and robustness of standard training and adversarial training. For CIFAR10 and CIFAR100, in standard training, the maximum improvement of accuracy is 1 . 38% on natural data, 30 . 97% on FGSM (i.e., Fast Gradient Sign Method) attack, 38 . 18% on PGD (i.e., Projected Gradient Descent) attack. While in adversarial training, the maximum improvement is 1 . 68% on natural data, 4 . 03% on FGSM attack and 2 . 65% on PGD attack.
Studying the Practices of Deploying Machine Learning Projects on Docker

Docker is a containerization service that allows for convenient deployment of websites, databases, applications’ APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based projects that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Toolkits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training). We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. We hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved.
Control hubs of complex networks and a polynomial-time identification algorithm

Unveiling the underlying control principles of complex networks is one of the ultimate goals of network science. We introduce a novel concept, control hub, to reveal a cornerstone of the control structure of a network. The control hubs of a network are the nodes that lie in the middle of a control path in every control scheme of the network. We present a theorem based on graph theory for identifying control hubs without computing all control schemes. We develop an algorithm to identify all control hubs in O ( N 0.5 L ) time complexity for a network of N nodes and L links.
The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia

Expression of sentiment in parliamentary debates is deemed to be significantly different from that on social media or in product reviews. This paper adds to an emerging body of research on parliamentary debates with a dataset of sentences annotated for detection sentiment polarity in political discourse. We sample the sentences for annotation from the proceedings of three Southeast European parliaments: Croatia, Bosnia-Herzegovina, and Serbia. A six-level schema is applied to the data with the aim of training a classification model for the detection of sentiment in parliamentary proceedings. Krippendorff's alpha measuring the inter-annotator agreement ranges from 0.6 for the six-level annotation schema to 0.75 for the three-level schema and 0.83 for the two-level schema. Our initial experiments on the dataset show that transformer models perform significantly better than those using a simpler architecture. Furthermore, regardless of the similarity of the three languages, we observe differences in performance across different languages. Performing parliament-specific training and evaluation shows that the main reason for the differing performance between parliaments seems to be the different complexity of the automatic classification task, which is not observable in annotator performance. Language distance does not seem to play any role neither in annotator nor in automatic classification performance. We release the dataset and the best-performing model under permissive licences.
A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation

One well established method of interactive image segmentation is the random walker algorithm. Considerable research on this family of segmentation methods has been continuously conducted in recent years with numerous applications. These methods are common in using a simple Gaussian weight function which depends on a parameter that strongly inﬂuences the segmentation performance. In this work we propose a general framework of deriving weight functions based on probabilistic modeling. This framework can be concretized to cope with virtually any well-deﬁned noise model. It eliminates the critical parameter and thus avoids time-consuming parameter search. We derive the speciﬁc weight functions for common noise types and show their superior performance on synthetic data as well as diﬀerent biomedical image data (MRI images from the NYU fastMRI dataset, larvae images acquired with the FIM technique). Our framework can also be used in multiple other applications, e.g., the graph cut algorithm and its extensions.
Generalized point-free spaces, pointwise

We survey foundational principles of Grothendieck’s generalized spaces, including a critical glossary of the various, and often conﬂicting, ter- minological usages. Known results using generalized points support a fully pointwise notation for these essentially point-free structures. This includes some from dependent type theory to deal with bundles as continuous space-valued maps, mapping base point to ﬁbre.
A Serverless Engine for High Energy Physics Distributed Analysis

The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a single machine. This issue has been tackled traditionally by running analyses in distributed environments using stateful, managed batch computing systems. While this approach has been effective so far, current estimates for future computing needs of the field present large scaling challenges. Such a managed approach may not be the only viable way to tackle them and an interesting alternative could be provided by serverless architectures, to enable an even larger scaling potential. This work describes a novel approach to running real HEP scientific applications through a distributed serverless computing engine. The engine is built upon ROOT, a well-established HEP data analysis software, and distributes its computations to a large pool of concurrent executions on Amazon Web Services Lambda Serverless Platform. Thanks to the developed tool, physicists are able to access datasets stored at CERN (also those that are under restricted access policies) and process it on remote infrastructures outside of their typical environment. The analysis of the serverless functions is monitored at runtime to gather performance metrics, both for data- and computation-intensive workloads.
Computer simulations of the glass transition and glassy materials

We provide an overview of the different types of computational techniques developed over the years to study supercooled liquids, glassy materials and the physics of the glass transition. We organise these numerical strategies into four broad families. For each of them, we describe the general ideas without discussing any technical details. We summarise the type of questions which can be addressed by any given approach and outline the main results which have been obtained. Finally we describe two important directions for future computational studies of glassy systems.
A weighted range‐free localization algorithm for irregular multihop networks

Range‐free localization is a widely concerned technique that supports many essential network applications like coverage and clustering. Most existing localization technologies assume that nodes are uniformly distributed in a regular network with no obstacles and that propagation paths between pairwise nodes are close to straight lines. However, in real‐world scenarios, network topology is usually irregular, resulting in estimated distances that differ significantly from the corresponding physical distances and poor anchor deployment. These will lead to erroneous distance measurement and inaccurate localization results. This paper proposes a new algorithm, called the weighted range‐free localization algorithm (WRL for short), to improve the localization performance in irregular networks. Specifically, we first present a feasible scheme that can fulfill the information exchange and relative distance estimation for each node simultaneously. We then employ an optimal weight function to suppress the effect of cumulative errors and introduce the weighted bounding‐box algorithm to mitigate outlier location estimation induced by probably collinear anchor nodes. Simulation results show that, compared to state‐of‐the‐art methods, WRL improves localization accuracy while incurring lower computation and communication overheads in various irregular network topologies.
Embodied Sense-Making of Diagrams as Conceptual Blending with Image Schemas

We present our approach to modeling the sense-making of diagrams as networks of conceptual blends that structure the geometric configuration of a diagram with embodied image schemas. We believe the types of inferences we confer to diagrammatic representations emerge as we cognitively construct these networks, and such inferences can be obtained in our model. We formalise image schemas and the geometric configurations of diagrams as FOL theories, drawing from Qualitative Spatial Reasoning formalisms; blends of image schemas with geometric configurations are then computed based on the theory of amalgams. We argue that this approach to sense-making of diagrams is more cognitively apt than the mainstream view of a diagram being a syntactic representation of some underlying logical semantics.
Towards Characterizing Coordinated Inauthentic Behaviors on YouTube

Online social networks and information consumed by their users have been increasingly targeted and altered by manipulative campaigns in recent years. YouTube is one of the most popular websites in the world. However, most of the existing systems and studies to detect and characterize manipulative influence campaigns focus on other platforms, while very little is known about the potential ways to detect and mitigate such attacks on YouTube. Furthermore, although recent literature is significantly developed in terms of assessing and detecting individual suspicious accounts across online social networks, more research is needed to detect, predict, and characterize large-scale coordinated campaigns in different contexts. This makes the analysis and detection of coordinated suspicious and inorganic activities on the given platform vital for researchers, policymakers, journalists, and more. In this paper, we report our study in progress to assess and characterize such suspicious activity on the level of YouTube channels, combining multiple layers of rolling window correlation analysis, anomaly detection, peak detection, rule-based supervised classification, network feature engineering, and unsupervised clustering approaches. Overall, the experimental dataset amounted to 39 channels, 936,247 videos, 99,415,476 comments, 115,825,225 subscribers and over 51 billion views. The results show that channels exhibiting inauthentic activities are characterized by a relatively lesser number of peaks in their anomaly patterns. However, the magnitude of these peaks is usually higher compared to that of less suspicious channels. Also, coordination assessment based on network structures and features produces promising results for identifying clusters of suspicious behaviors across channels.
Identification of regulatory proteins of acid-sensing ion channels.

Acid-sensing ion channels (ASICs) are voltage-independent proton-gated ion channels belonging to the amiloride-sensitive degenerin/epithelial Na+ channel (DEG/ENaC) family of receptor channels. Six subunits (ASICla, lb, 2a, 2b, 3 and 4) encoded for by four genes, have been identified so far. All ASIC subunits are expressed in dorsal root ganglia (DRG) and have been implicated in physiological sensory processes such as nociception associated with tissue acidosis, cutaneous and visceral mechanosensation, sour taste and cochlear function. However, some ASIC subunits also show a wide distribution throughout the brain, where they are thought to modulate synaptic communication. Supporting this hypothesis several studies demonstrated in mice a role for ASICs in learning, memory and fear behaviour. Recently ASIC la channels were also shown to make a major contribution to hippocampal neuronal damage in stroke through Ca2+ overload. ASIC4 is broadly expressed in the nervous system but is not gated by protons and has no known function. This thesis describes a genetic analysis carried out to identify interacting partners of ASICs in sensory neurons, in order to shed light on the possible role of ASIC4, and to better understand the functional roles of ASIC 1-3 and their regulatory mechanisms. A rat dorsal root ganglion cDNA library was screened in a yeast two-hybrid assay and a number of proteins interacting with the N-terminal domain of rat ASIC 1-4 were trapped. Many of these proteins were involved in trafficking of ion channels, G protein pathways, endocytosis, protein ubiquitination, or cell adhesion, suggesting potentially novel roles and regulatory mechanisms for ASIC channels. The annexin II light chain pll was found to specifically interact with ASIC la, and to promote its functional expression at the plasma membrane, as shown by immunocytochemistry, cell surface protein biotinylation and electrophysiology. ASIC4 was shown to decrease the protein level of other ASIC subunits, and to downregulate ASIC la-mediated currents. Preliminary data suggest that this effect may be due to an involvement of ASIC4 in ubiquitination pathways. Overall, this work has led to the identification of interacting proteins that regulate ASICs and suggested novel functions for ASIC4.
Magnetic gears – an essential enabler for the next generation’s electromechanical drives

New magnetic gears show promise as replacements for mechanical gears, not only because of high reliability and low maintenance, but also because of superior torque-to-weight ratios. A superior torque-to-weight ratio is most unexpected but is surfacing as a real possibility when the full perimeter of the gear is used to generate torque. Mechanical gears are to be distinguished in this one aspect since only two to three teeth are engaged in a single stage. For this same reason, high gear ratio magnetic gears can be constructed without the use of multiple stages.