Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Nonnegative Matrix Factorization for Multiple-Local-Community Detection

Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such groups of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Existing local community detection methods focus on finding either one local community that all seeds are most likely to be in or finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. In this work, we present a novel method of detecting multiple local communities to which a single-seed member belongs. The proposed method consists of three key steps: 1) local sampling with Personalized PageRank (PPR); 2) using the sparseness generated by a sparse nonnegative matrix factorization (SNMF) to estimate the number of communities in the sampled subgraph; and 3) using SNMF soft community membership vectors to assign nodes to communities. The proposed method shows favorable accuracy performance and a good conductance compared with state-of-the-art community detection methods by experiments using a combination of artificial and real-world networks.
Stability of vacuum for the Landau equation with hard potentials

We consider the spatially inhomogeneous Landau equation with Maxwellian and hard potentials (i.e with $\gamma\in[0,1)$) on the whole space $\mathbb{R}^3$. We prove that if the initial data $f_{\text{in}}$ are close to the vacuum solution $f_{\text{vac}}=0$ in an appropriate weighted norm then the solution $f$ exists globally in time. This work builds up on the author's earlier work on local existence of solutions to Landau equation with hard potentials. Our proof uses $L^2$ estimates and exploits the null-structure established by Luk [Stability of vacuum for the Landau equation with moderately soft potentials, Annals of PDE (2019) 5:11]. To be able to close our estimates, we have to couple the weighted energy estimates, which were established by the author in a previous paper [Local existence for the Landau equation with hard potentials, arXiv:1910.11866], with the null-structure and devise new weighted norms that take this into account.
Efficient Radial Pattern Keyword Search on Knowledge Graphs in Parallel

Recently, keyword search on Knowledge Graphs (KGs) becomes popular. Typical keyword search approaches aim at finding a concise subgraph from a KG, which can reflect a close relationship among all input keywords. The connection paths between keywords are selected in a way that leads to a result subgraph with a better semantic score. However, such a result may not meet user information need because it relies on the scoring function to decide what keywords to link closer. Therefore, such a result may miss close connections among some keywords on which users intend to focus. In this paper, we propose a parallel keyword search engine, called RAKS. It allows users to specify a query as two sets of keywords, namely central keywords and marginal keywords. Specifically, central keywords are those keywords on which users focus more. Their relationships are desired in the results. Marginal keywords are those less focused keywords. Their connections to the central keywords are desired. In addition, they provide additional information that helps discover better results in terms of user intents. To improve the efficiency, we propose novel weighting and scoring schemes that boost the parallel execution during search while retrieving semantically relevant results. We conduct extensive experiments to validate that RAKS can work efficiently and effectively on open KGs with large size and variety.
Dynamical self-friction: how mass loss slows you down

We investigate dynamical self-friction, the process by which material that is stripped from a subhalo torques its remaining bound remnant, which causes it to lose orbital angular momentum. By running idealized simulations of a subhalo orbiting within an analytical host halo potential, we isolate the effect of self-friction from traditional dynamical friction due to the host halo. While at some points in a subhalo's orbit the torque of the stripped material can boost the orbital angular momentum of the remnant, the net effect over the long term is orbital decay regardless of the initial orbital parameters or subhalo mass. In order to quantify the strength of self-friction, we run a suite of simulations spanning typical host-to-subhalo mass ratios and orbital parameters. We find that the time-scale for self-friction, defined as the exponential decay time of the subhalo's orbital angular momentum, scales with mass ratio and orbital circularity similar to standard dynamical friction. The decay time due to self-friction is roughly an order of magnitude longer, suggesting that self-friction only contributes at the 10 percent level. However, along more radial orbits, self-friction can occasionally dominate over dynamical friction close to pericentric passage, where mass stripping is intense. This is also the epoch at which the self-friction torque undergoes large and rapid changes in both magnitude and direction, indicating that self-friction is an important process to consider when modeling pericentric passages of subhaloes and their associated satellite galaxies.
On the Joint Typicality of Permutations of Sequences of Random Variables

Permutations of correlated sequences of random variables appear naturally in a variety of applications such as graph matching and asynchronous communications. In this paper, the asymptotic statistical behavior of such permuted sequences is studied. It is assumed that a collection of random vectors is produced based on an arbitrary joint distribution, and the vectors undergo a permutation operation. The joint typicality of the resulting permuted vectors with respect to the original distribution is investigated. As an initial step, permutations of pairs of correlated random vectors are considered. It is shown that the probability of joint typicality of the permuted vectors depends only on the number and length of the disjoint cycles of the permutation. Consequently, it suffices to study typicality for a class of permutations called 'standard permutations', for which, upper-bounds on the probability of joint typicality are derived. The notion of standard permutations is extended to a class of permutation vectors called 'Bell permutation vectors'. By investigating Bell permutation vectors, upper-bounds on the probability of joint typicality of permutations of arbitrary collections of random sequences are derived.
CopyCAT: : Taking Control of Neural Policies with Constant Attacks

We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We show its effectiveness on Atari 2600 games in the novel read-only setting. In this setting, the adversary cannot directly modify the agent's state -- its representation of the environment -- but can only attack the agent's observation -- its perception of the environment. Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.
The Extreme CNO-enhanced Composition of the Primitive Iron-poor Dwarf Star J0815+4729

We present an analysis of high-resolution Keck/HIRES spectroscopic observations of J0815+4729, an extremely carbon-enhanced, iron-poor dwarf star. These high-quality data allow us to derive a metallicity of [Fe/H] = −5.49 ± 0.14 from the three strongest Fe i lines and to measure a high [Ca/Fe] = 0.75 ± 0.14. The large carbon abundance of A(C) = 7.43 ± 0.17 (or [C/Fe] ∼ 4.49 ± 0.11) places this star in the upper boundary of the low-carbon band in the A(C)–[Fe/H] diagram, suggesting no contamination from a binary AGB companion. We detect the oxygen triplet at 777 nm for the first time in an ultra-metal-poor star, indicating a large oxygen-to-iron abundance ratio of [O/Fe] = 4.03 ± 0.12 (A(O) = 7.23 ± 0.14), significantly higher than the previously most metal-poor dwarf J2209–0028 with an oxygen triplet detection with [O/Fe] ∼ 2.2 dex at [Fe/H] ∼ −3.9. Nitrogen is also dramatically enhanced with (A(N) = 6.75 ± 0.08) and an abundance ratio [N/Fe] ∼ 4.41 ± 0.08. We also detect Ca, Na, and Mg, while providing upper limits for eight other elements. The abundance pattern of J0815+4729 resembles that of HE 1327–2326, indicating that both are second-generation stars contaminated by a ∼21–27 M⊙ single, zero-metallicity, low-energy supernova with very little mixing and substantial fallback. The absence of lithium implies an upper limit abundance A(Li) < 1.3 dex, about 0.7 dex below the detected Li abundance in J0023+0307, which has a similar metallicity, exacerbating the cosmological lithium problem.
Common Conversational Community Prototype: Scholarly Conversational Assistant

This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic information needs and conversational interactions. Specifically, we propose to develop and operate a prototype conversational search system for scholarly activities. This Scholarly Conversational Assistant would serve as a useful tool, a means to create datasets, and a platform for running evaluation challenges by groups across the community. This article results from discussions of a working group at Dagstuhl Seminar 19461 on Conversational Search.
Solution Bounds, Stability, and Estimation of Trapping/Stability Regions of Some Nonlinear Time-Varying Systems

Estimation of solution norms and stability for time-dependent nonlinear systems is ubiquitous in numerous engineering, natural science and control problems. Yet, practically valuable results are rare in this area. This paper develops a novel approach, which bounds the solution norms, derives the corresponding stability criteria, and estimates the trapping/stability regions for some nonautonomous and nonlinear systems, which arise in various application domains. Our inferences rest on deriving a scalar differential inequality for the norms of solutions to the initial systems. Utility of the Lipschitz inequality linearizes the associated auxiliary differential equation and yields both the upper bounds for the norms of solutions and the relevant stability criteria. To refine these inferences, we introduce a nonlinear extension of the Lipschitz inequality, which improves the developed bounds and allows estimation of the stability basins and trapping regions for the corresponding systems. Finally, we confirm the theoretical results in representative simulations.
Experimental characterization of spin-
32
 silicon vacancy centers in 
6H
-SiC

Silicon carbide (SiC) hosts many interesting defects that can potentially serve as qubits for a range of advanced quantum technologies. Some of them have very interesting properties, making them potentially useful, e.g., as interfaces between stationary and flying qubits. Here we present a detailed overview of the relevant properties of the spins in silicon vacancies of the $6H$-SiC polytype. This includes the temperature-dependent photoluminescence, optically detected magnetic resonance, and the relaxation times of the longitudinal and transverse components of the spins during free precession as well as under the influence of different refocusing schemes.
AI Data Wrangling with Associative Arrays

The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data representations supporting the many query and analysis steps found in an AI pipeline. Rigorous mathematical representations of these data enables data translation and analysis optimization within and across steps. Associative array algebra provides a mathematical foundation that naturally describes the tabular structures and set mathematics that are the basis of databases. Likewise, the matrix operations and corresponding inference/training calculations used by neural networks are also well described by associative arrays. More surprisingly, a general denormalized form of hierarchical formats, such as XML and JSON, can be readily constructed. Finally, pivot tables, which are among the most widely used data analysis tools, naturally emerge from associative array constructors. A common foundation in associative arrays provides interoperability guarantees, proving that their operations are linear systems with rigorous mathematical properties, such as, associativity, commutativity, and distributivity that are critical to reordering optimizations.
PoAh: A Novel Consensus Algorithm for Fast Scalable Private Blockchain for Large-scale IoT Frameworks

In today's connected world, resource constrained devices are deployed for sensing and decision making applications, ranging from smart cities to environmental monitoring. Those recourse constrained devices are connected to create real-time distributed networks popularly known as the Internet of Things (IoT), fog computing and edge computing. The blockchain is gaining a lot of interest in these domains to secure the system by ignoring centralized dependencies, where proof-of-work (PoW) plays a vital role to make the whole security solution decentralized. Due to the resource limitations of the devices, PoW is not suitable for blockchain-based security solutions. This paper presents a novel consensus algorithm called Proof-of-Authentication (PoAh), which introduces a cryptographic authentication mechanism to replace PoW for resource constrained devices, and to make the blockchain application-specific. PoAh is thus suitable for private as well as permissioned blockchains. Further, PoAh not only secures the systems, but also maintains system sustainability and scalability. The proposed consensus algorithm is evaluated theoretically in simulation scenarios, and in real-time hardware testbeds to validate its performance. Finally, PoAh and its integration with the blockchain in the IoT and edge computing scenarios is discussed. The proposed PoAh, while running in limited computer resources (e.g. single-board computing devices like the Raspberry Pi) has a latency in the order of 3 secs.
Chemical abundances of Seyfert 2 AGNs – II. N2 metallicity calibration based on SDSS

This work is partially supported by the Brazilian agencies FAPESP, CAPES, and CNPq. EPM acknowledges support from the Spanish MINECO project Estallidos 6 AYA2016-79724-C4 and by the Spanish Science Ministry Centro de Excelencia Severo Ochoa Program under grant no. SEV-2017-0709.
Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the so-called angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-of-arrival (AoA)-based beamforming/combining schemes for FDD-based cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a max-min power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.
Pruning Neural Belief Propagation Decoders

We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al. While this method significantly outperforms conventional BP decoding, the underlying parity-check matrix may still limit the overall performance. In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We consider the weights in the Tanner graph as an indication of the importance of the connected check nodes (CNs) to decoding and use them to prune unimportant CNs. As the pruning is not tied over iterations, the final decoder uses a different parity-check matrix in each iteration. For ReedMuller and short low-density parity-check codes, we achieve performance within 0.27dB and 1.5dB of the ML performance while reducing the complexity of the decoder.
Dynamic Epistemic Logic Games with Epistemic Temporal Goals

Dynamic Epistemic Logic (DEL) is a logical framework in which one can describe in great detail how actions are perceived by the agents, and how they affect the world. DEL games were recently introduced as a way to define classes of games with imperfect information where the actions available to the players are described very precisely. This framework makes it possible to define easily, for instance, classes of games where players can only use public actions or public announcements. These games have been studied for reachability objectives, where the aim is to reach a situation satisfying some epistemic property expressed in epistemic logic; several (un)decidability results have been established. In this work we show that the decidability results obtained for reachability objectives extend to a much more general class of winning conditions, namely those expressible in the epistemic temporal logic LTLK. To do so we establish that the infinite game structures generated by DEL public actions are regular, and we describe how to obtain finite representations on which we rely to solve them.
Generate High-Resolution Adversarial Samples by Identifying Effective Features

As the prevalence of deep learning in computer vision, adversarial samples that weaken the neural networks emerge in large numbers, revealing their deep-rooted defects. Most adversarial attacks calculate an imperceptible perturbation in image space to fool the DNNs. In this strategy, the perturbation looks like noise and thus could be mitigated. Attacks in feature space produce semantic perturbation, but they could only deal with low resolution samples. The reason lies in the great number of coupled features to express a high-resolution image. In this paper, we propose Attack by Identifying Effective Features (AIEF), which learns different weights for features to attack. Effective features, those with great weights, influence the victim model much but distort the image little, and thus are more effective for attack. By attacking mostly on them, AIEF produces high resolution adversarial samples with acceptable distortions. We demonstrate the effectiveness of AIEF by attacking on different tasks with different generative models.
ACADEMIC QUALITY AND STUDENTS PERFORMANCE IN THE NIGERIAN TERTIARY EDUCATION: LESSONS FROM A PRIVATE FAITH-BASED UNIVERSITY IN SOUTH-WEST, NIGERIA (2010-2015)

The pivotal role of education as a driver of economic growth and devc:iopment i! " any country cannot be over-emphasised. To achieve this, academic quality and students' p:::rformanc<:) as intertwining variables in tertiary education must be deliberately contemplated. The absence of academic quality has obvious negative effects on students' performance, which ultirnateiy casts aspersions on the knowledge base of a society. The narrative on university education in Nigeria highl ights gaps in the delivery of triad objectives of teaching, research and community impact, and this is concomitant with allegation of graduates' unemployability resu lting from poor training and skills deficits. It is predicated on these, that this paper examines the impact of academic quality on students' performance with a focus on the University under reference between 2010 and 2015. With a reliance on secondary data, backed by the descriptive analytical approach, the paper observes that the University operates a departure philosophy based on its unique mission which radically altered the Nigerian tertiary educational landscape from inception. Continuous compliance with internal and external quality mechanisms and sustained infrastructural investments with excellent support services are seriously advocated
Regulation of M/G/1 queue with customers having nonincreasing stochastic marginal utilities

Consider a single-server first-come-first-served M/G/1 queue with the exception that service demand distribution is determined endogenously by the following mechanism: Each customer who enters into the service position faces a dynamic decision of when to terminate her service period and then to leave the system for good. In addition, all customers have random linear costs in their waiting times (not including service times) and each of them continuously observes the evolution of her marginal utility from service duration. It is assumed that the marginal utilities of the customers are general iid nonincreasing, right-continuous stochastic processes which are independent from the arrival process. Now, customers are acting according to their own self-interest and hence there is an overuse of the server. To overcome this inefficiency, this work includes an efficient method to compute a price function which implements a socially optimal resource allocation. Then, some examples of nonincreasing, right-continuous stochastic processes are considered. In addition, it is shown that this price function internalizes the externalities created by customers. Finally, similar results are derived for M/G/1 retrial queue. In particular, there is a conjecture regarding the expression for the expected externalities in such a retrial queueing system.