Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistent Homology Transform Cosheaf

We employ the recent discovery of functoriality for persistent homology to recast the Persistent Homology Transform of a geometric complex as a cosheaf of combinatorial persistence diagrams. Detailed examples are provided. We hope the recasting of the PH transform as a cellular cosheaf will allow for the adoption of existing methods from category theory and (co)sheaf theory to the study of shapes.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias

Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the impact of bias to achieve unbiased recommendation. In this work, we focus on an essential bias in micro-video recommendation, duration bias. Specifically, existing micro-video recommender systems usually consider watch time as the most critical metric, which measures how long a user watches a video. Since videos with longer duration tend to have longer watch time, there exists a kind of duration bias, making longer videos tend to be recommended more against short videos. In this paper, we empirically show that commonly-used metrics are vulnerable to duration bias, making them NOT suitable for evaluating micro-video recommendation. To address it, we further propose an unbiased evaluation metric, called WTG (short for Watch Time Gain). Empirical results reveal that WTG can alleviate duration bias and better measure recommendation performance. Moreover, we design a simple yet effective model named DVR (short for Debiased Video Recommendation) that can provide unbiased recommendation of micro-videos with varying duration, and learn unbiased user preferences via adversarial learning. Extensive experiments based on two real-world datasets demonstrate that DVR successfully eliminates duration bias and significantly improves recommendation performance with over 30% relative progress. Codes and datasets are released at https://github.com/tsinghua-fib-lab/WTG-DVR.
A Reflection on the Organic Growth of the Internet Protocol Stack

In the last 15 years, the Internet architecture has continued evolving organically, introducing new headers and protocols to the classic TCP/IP stack. More specifically, we have identified two major trends. First, it is common that most communications are encrypted, either at L3 or L4. And second, due to protocol ossification, developers have resorted to upper layers to introduce new functionalities (L4 and above). For example, QUIC's connection migration feature provides mobility at L4. In this paper we present a reflection around these changes, and attempt to formalize them by adding two additional protocol headers to the TCP/IP stack: one for security, and another for new functionalities. We must note that we are not presenting a new architecture, but trying to draw up what it's already out there. In addition, we elaborate on the forces that have brought us here, and we enumerate current proposals that are shaping these new headers. We also analyze in detail three examples of such trends: the Zero Trust Networking paradigm, the QUIC transport protocol, and modern SD-WAN systems. Finally, we present a formalization of this architecture by adding these two additional layers to the TCP/IP protocol stack. Our goal is triggering a discussion on the changes of the current Internet architecture.
Identifying human mobility patterns using smart card data

ABSTRACT Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.
On the Kinematics of Cold, Metal-enriched Galactic Fountain Flows in Nearby Star-forming Galaxies

We use medium-resolution Keck/Echellette Spectrograph and Imager spectroscopy of bright quasars to study cool gas traced by Ca ii λλ3934, 3969 and Na i λλ5891, 5897 absorption in the interstellar/circumgalactic media of 21 foreground star-forming galaxies at redshifts 0.03 < z < 0.20 with stellar masses 7.4 ≤ log M */M ⊙ ≤ 10.6. The quasar–galaxy pairs were drawn from a unique sample of Sloan Digital Sky Survey quasar spectra with intervening nebular emission, and thus have exceptionally close impact parameters (R ⊥ < 13 kpc). The strength of this line emission implies that the galaxies’ star formation rates (SFRs) span a broad range, with several lying well above the star-forming sequence. We use Voigt profile modeling to derive column densities and component velocities for each absorber, finding that column densities N(Ca ii) > 1012.5 cm−2 (N(Na i) > 1012.0 cm−2) occur with an incidence f C(Ca ii) = 0.63+0.10 −0.11 (f C(Na i) = 0.57+0.10 −0.11). We find no evidence for a dependence of f C or the rest-frame equivalent widths W r (Ca ii K) or W r (Na i 5891) on R ⊥ or M *. Instead, W r (Ca ii K) is correlated with local SFR at >3σ significance, suggesting that Ca ii traces star formation-driven outflows. While most of the absorbers have velocities within ±50 km s−1 of the host redshift, their velocity widths (characterized by Δv 90) are universally 30–177 km s−1 larger than that implied by tilted-ring modeling of the velocities of interstellar material. These kinematics must trace galactic fountain flows and demonstrate that they persist at R ⊥ > 5 kpc. Finally, we assess the relationship between dust reddening and W r (Ca ii K) (W r (Na i 5891)), finding that 33% (24%) of the absorbers are inconsistent with the best-fit Milky Way E(B−V)-W r relations at >3σ significance.
A Fresh Perspective on DNN Accelerators by Performing Holistic Analysis Across Paradigms

Traditional computers with von Neumann architecture are unable to meet the latency and scalability challenges of Deep Neural Network (DNN) workloads. Various DNN accelerators based on Conventional compute Hardware Accelerator (CHA), Near-Data-Processing (NDP) and Processing-in-Memory (PIM) paradigms have been proposed to meet these challenges. Our goal in this work is to perform a rigorous comparison among the state-of-the-art accelerators from DNN accelerator paradigms, we have used unique layers from MobileNet, ResNet, BERT, and DLRM of MLPerf Inference benchmark for our analysis. The detailed models are based on hardware-realized state-of-the art designs. We observe that for memory-intensive Fully Connected Layer (FCL) DNNs, NDP based accelerator is 10.6 × faster than the state-of-the-art CHA and 39.9 × faster than PIM based accelerator for inferencing. For compute-intensive image classiﬁcation and object detection DNNs, the state-of-the-art CHA is ∼ 10 × faster than NDP and ∼ 2000 × faster than the PIM-based accelerator for inferencing. PIM-based accelerators are suitable for DNN applications where energy is a constraint ( ∼ 2.7 × and ∼ 21 × lower energy for CNN and FCL applications, respectively, than conventional ASIC systems). Further, we identify architectural changes (such as increasing memory bandwidth, buffer reorganization) that can increase throughput (up to linear increase) and lower energy (up to linear decrease) for ML applications with a detailed sensitivity analysis of relevant components in CHA, NDP and PIM based accelerators.
Commutators in Completely Simple Semigroups

We obtain a characterization of the binary commutator on completely simple semigroups, using their Rees matrix representation. Consequently, we prove that a regular semigroup is nilpotent (solvable) if and only if it is simple, and all its $\mathcal{H}$-classes are nilpotent (solvable) groups.
Aesthetic Bot: Interactively Evolving Game Maps on Twitter

This paper describes the implementation of the Aesthetic Bot, an automated Twitter account that posts images of small game maps that are either user-made or generated from an evolutionary system. The bot then prompts users to vote via a poll posted in the image's thread for the most aesthetically pleasing map. This creates a rating system that allows for direct interaction with the bot in a way that is integrated seamlessly into a user's regularly updated Twitter content feed. Upon conclusion of the each voting round, the bot learns from the distribution of votes for each map to emulate user preferences for design and visual aesthetic in order to generate maps that would win future vote pairings. We discuss the ongoing results and emerging behaviors that have occurred since the release of this system from both the bot's generation of game maps and the participating Twitter users.
Conditions for none to be whipped by `Rank and Yank' under the majority rule

‘Rank and Yank’ is practiced in many organizations. This paper is concerned with the condtions for none to be whipped by ‘Rank and Yank’ when the evaluation data under each criterion are assumed to be ordinal rankings and the majority rule is used. Two suﬃcient conditions are set forth of which the ﬁrst one formulates the alternatives indiﬀerence deﬁnition in terms of the election matrix, while the second one speciﬁes a certain balance in the probabilities of alternatives being ranked at positions. In a sense, ‘none to be whipped’ means that the organization is of stability. Thus the second suﬃcient condition indicates an intrinsic relation of balance and organization stability. In addition, directions for future research are put forward.
Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution

Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some studies have made progress in ZSC by exposing the agents to diverse partners during the training process. They usually involve self-play when training the partners, implicitly assuming that the tasks are homogeneous. However, many real-world tasks are heterogeneous, and hence previous methods may be inefficient. In this paper, we study the heterogeneous ZSC problem for the first time and propose a general method based on coevolution, which coevolves two populations of agents and partners through three sub-processes: pairing, updating and selection. Experimental results on various heterogeneous tasks highlight the necessity of considering the heterogeneous setting and demonstrate that our proposed method is a promising solution for heterogeneous ZSC tasks.
Reducing Exploitability with Population Based Training

Self-play reinforcement learning has achieved state-of-the-art, and often superhuman, performance in a variety of zero-sum games. Yet prior work has found that policies that are highly capable against regular opponents can fail catastrophically against adversarial policies: an opponent trained explicitly against the victim. Prior defenses using adversarial training were able to make the victim robust to a specific adversary, but the victim remained vulnerable to new ones. We conjecture this limitation was due to insufficient diversity of adversaries seen during training. We analyze a defense using population based training to pit the victim against a diverse set of opponents. We evaluate this defense's robustness against new adversaries in two low-dimensional environments. This defense increases robustness against adversaries, as measured by the number of attacker training timesteps to exploit the victim. Furthermore, we show that robustness is correlated with the size of the opponent population.
Poincar\'e inequalities for Markov chains: a meeting with Cheeger, Lyapunov and Metropolis

We develop a theory of weak Poincaré inequalities to characterize convergence rates of ergodic Markov chains. Motivated by the application of Markov chains in the context of algorithms, we develop a relevant set of tools which enable the practical study of convergence rates in the setting of Markov chain Monte Carlo methods, but also well beyond.
The Analysis about Building Cross-lingual Sememe Knowledge Base Based on Deep Clustering Network

A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain words annotated with sememes, have been successfully applied to many NLP tasks, and we believe that by learning the smallest unit of meaning, computers can more easily understand human language. However, Existing sememe KBs are built on only manual annotation, human annotations have personal understanding biases, and the meaning of vocabulary will be constantly updated and changed with the times, and artificial methods are not always practical. To address the issue, we propose an unsupervised method based on a deep clustering network (DCN) to build a sememe KB, and you can use any language to build a KB through this method. We first learn the distributed representation of multilingual words, use MUSE to align them in a single vector space, learn the multi-layer meaning of each word through the self-attention mechanism, and use a DNC to cluster sememe features. Finally, we completed the prediction using only the 10-dimensional sememe space in English. We found that the low-dimensional space can still retain the main feature of the sememes.
On pseudoinverse-free randomized methods for linear systems: Unified framework and acceleration

We present a new framework for the analysis and design of randomized algorithms for solving various types of linear systems, including consistent or inconsistent, full rank or rank-deficient. Our method is formulated with four randomized sampling parameters, which allows the method to cover many existing randomization algorithms within a unified framework, including the doubly stochastic Gauss-Seidel, randomized Kaczmarz method, randomized coordinate descent method, and Gaussian Kaczmarz method. Compared with the projection-based block algorithms where a pseudoinverse for solving a least-squares problem is utilized at each iteration, our design is pseudoinverse-free. Furthermore, the flexibility of the new approach also enables the design of a number of new methods as special cases. Polyak's heavy ball momentum technique is also introduced in our framework for improving the convergence behavior of the method. We prove the global linear convergence rates of our method as well as an accelerated linear rate for the case of the norm of expected iterates. Finally, numerical experiments are provided to confirm our results.
The Completeness of Reasoning Algorithms for Clause Sets in Description Logic ALC

On the Semantic Web, metadata and ontologies are used to enable computers to read data. The Web Ontology Language (OWL) has been proposed as a standard ontological language, and various inference systems for this language have been studied. Description logics are regarded as the theoretical foundations of OWL; they provide the syntax and semantics of a formal language for describing ontologies and knowledge bases. In addition, tableau algorithms for description logics have been developed as the standard reasoning algorithms for decidable problems. However, tableau algorithms generate inefficient reasoning steps owing to their nondeterministic branching for disjunction as well as the increase in the size of models occasioned by existential quantification. In this study, we propose conjunctive normal form (CNF) concepts, which utilize a flat concept form for description logic ALC in order to develop algorithms for reasoning about sets of clauses. We present an efficient reasoning algorithm for clause sets where any ALC concept is transformed into an equivalent CNF concept. Theoretically, we prove the soundness, completeness, and termination of the reasoning algorithms for the satisfiability of CNF concepts.
Erasure qubits: Overcoming the $T_1$ limit in superconducting circuits

The amplitude damping time, $T_1$, has long stood as the major factor limiting quantum fidelity in superconducting circuits, prompting concerted efforts in the material science and design of qubits aimed at increasing $T_1$. In contrast, the dephasing time, $T_{\phi}$, can usually be extended above $T_1$ (via, e.g., dynamical decoupling), to the point where it does not limit fidelity. In this article we propose a scheme for overcoming the conventional $T_1$ limit on fidelity by designing qubits in a way that amplitude damping errors can be detected and converted into erasure errors. Compared to standard qubit implementations our scheme improves the performance of fault-tolerant protocols, as numerically demonstrated by the circuit-noise simulations of the surface code. We describe two simple qubit implementations with superconducting circuits and discuss procedures for detecting amplitude damping errors, performing entangling gates, and extending $T_\phi$. Our results suggest that engineering efforts should focus on improving $T_\phi$ and the quality of quantum coherent control, as they effectively become the limiting factor on the performance of fault-tolerant protocols.
Quantization of color images based on uniform color spaces

Digital devices for display or printing of color images may be characterized by their ability to generate colored spots centered at points on a fixed spatial lattice. Each spot may be assigned one of a finite number of color values. For reasons of economy in hardware design and data representation, it is desirable to use as small a set of different color values as possible. We investigate algorithms for selecting this set of output colors in alternate color spaces chosen on a perceptual basis. These algorithms utilize quantizers that range in complexity from being separable, uniform, and image independent, to being nonseparable, nonuniform, and image dependent
Transport coefficients of quasiparticle models within a new relaxation time approximation of the Boltzmann equation

We investigate the transport properties of a kinetic theory model that is tuned to describe the thermodynamic properties of QCD at zero chemical potential using a new formulation of the relaxation time approximation. In contrast to previous approaches, the latter is constructed to preserve the fundamental properties of the collision term of the Boltzmann equation for any energy-dependence of the relaxation time. A novel choice of matching conditions is implemented to ensure that the background mean-field depends only on the temperature even when the system is out of equilibrium. We provide a consistent analysis of how the transport coefficients of relativistic Navier-Stokes theory vary with the energy dependence of the relaxation time. We also show that the entropy production of this theory is consistent with the second law of thermodynamics and verify that it is independent of the matching conditions employed. We used this fact to calculate the matching independent combination of transport coefficients.
Explaining Machine Learning DGA Detectors from DNS Traffic Data

One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker. This attack is made by leveraging the Domain Name System (DNS) technology through Domain Generation Algorithms (DGAs), a stealthy connection strategy that yet leaves suspicious data patterns. To detect such threats, advances in their analysis have been made. For the majority, they found Machine Learning (ML) as a solution, which can be highly effective in analyzing and classifying massive amounts of data. Although strongly performing, ML models have a certain degree of obscurity in their decision-making process. To cope with this problem, a branch of ML known as Explainable ML tries to break down the black-box nature of classifiers and make them interpretable and human-readable. This work addresses the problem of Explainable ML in the context of botnet and DGA detection, which at the best of our knowledge, is the first to concretely break down the decisions of ML classifiers when devised for botnet/DGA detection, therefore providing global and local explanations.
Influence of Low-Temperature Charge on the Mechanical Integrity Behavior of 18650 Lithium-Ion Battery Cells Subject to Lateral Compression

The study on the damage tolerance and failure mechanism of lithium-ion batteries (LIBs) subject to mechanical attack has attracted considerable attention. The electrochemical performance and thermal behavior of LIB were significantly affected by operation temperature and charging rate, but the dependence of these two factors on mechanical response remains unclear. Hence, we investigated how the environmental temperatures and rates in charging process affected the mechanical response characteristics of 18650 LIB cells. The onset of the short circuit in the cells which charged at temperatures above −25 °C occurred around their modulus peak under compression. At −25 °C, there was a strong possibility that a premature short circuit occurred locally in the cells during charging, thus they might show complex and variable mechanical response under compression. The failure moduli and crushing stresses of cells subject to compression tended to decrease as their ambient charging temperatures went down. Besides, 0.5 C-charged cells exhibited higher failure moduli and crushing stresses than the 1 C-charged cells above −20 °C. Morphology analyses of the cell electrode surfaces revealed that mossy lithium deposits became evident at temperatures below −10 °C. Furthermore, their distribution was uniform. Mechanical results also indicated that the short-term cycling at −20 °C and 0.5 C would soften the cell.