Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty relations for the Hohenberg-Kohn theorem

How does charge density constrain many-body wavefunctions in nature? The Hohenberg-Kohn theorem for non-relativistic, interacting many-body Schr¨odinger systems is well-known and was proved using reductio-ad-absurdum ; however, the physical mechanism or principle which enables this theorem in nature has not been understood. Here, we obtain eﬀective canonical operators in the interacting many-body problem – (i) the local electric ﬁeld, which mediates interaction between particles, and contributes to the potential energy; and (ii) the particle momenta, which contribute to the kinetic energy. The commutation of these operators results in the charge density distribution. Thus, quantum ﬂuctuations of interacting many-particle systems are constrained by charge density, providing a mechanism by which an external potential, by coupling to the charge density, tunes the quantum-mechanical many-body wavefunction. As an initial test, we obtain the functional form for total energy of interacting many-particle systems, and in the uniform density limit, ﬁnd promising agreement with Quantum Monte Carlo simulations.
CREER: A Large-Scale Corpus for Relation Extraction and Entity Recognition

—We describe the design and use of the CREER dataset, a large corpus annotated with rich English grammar and semantic attributes. The CREER dataset uses the Stanford CoreNLP Annotator to capture rich language structures from Wikipedia plain text. This dataset follows widely used linguistic and semantic annotations so that it can be used for not only most natural language processing tasks but also scaling the dataset. This large supervised dataset can serve as the basis for improving the performance of NLP tasks in the future.
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus

The UMLS Metathesaurus integrates more than 200 biomedical source vocabularies. During the Metathesaurus construction process, synonymous terms are clustered into concepts by human editors, assisted by lexical similarity algorithms. This process is error-prone and time-consuming. Recently, a deep learning model (LexLM) has been developed for the UMLS Vocabulary Alignment (UVA) task. This work introduces UBERT, a BERT-based language model, pretrained on UMLS terms via a supervised Synonymy Prediction (SP) task replacing the original Next Sentence Prediction (NSP) task. The effectiveness of UBERT for UMLS Metathesaurus construction process is evaluated using the UMLS Vocabulary Alignment (UVA) task. We show that UBERT outperforms the LexLM, as well as biomedical BERT-based models. Key to the performance of UBERT are the synonymy prediction task specifically developed for UBERT, the tight alignment of training data to the UVA task, and the similarity of the models used for pretrained UBERT.
Systematic Literature Review: Anti-Phishing Defences and Their Application to Before-the-click Phishing Email Detection

Most research into anti-phishing defence assumes that the mal-actor is attempting to harvest end-users' personally identifiable information or login credentials and, hence, focuses on detecting phishing websites. The defences for this type of attack are usually activated after the end-user clicks on a link, at which point the link is checked. This is known as after-the-click detection. However, more sophisticated phishing attacks (such as spear-phishing and whaling) are rarely designed to get the end-user to visit a website. Instead, they attempt to get the end-user to perform some other action, for example, transferring money from their bank account to the mal-actors account. These attacks are rarer, and before-the-click defence has been investigated less than after-the-click defence. To better integrate and contextualize these studies in the overall anti-phishing research, this paper presents a systematic literature review of proposed anti-phishing defences. From a total of 6330 papers, 21 primary studies and 335 secondary studies were identified and examined. The current research was grouped into six primary categories, blocklist/allowlist, heuristics, content, visual, artificial intelligence/machine learning and proactive, with an additional category of"other"for detection techniques that do not fit into any of the primary categories. It then discusses the performance and suitability of using these techniques for detecting phishing emails before the end-user even reads the email. Finally, it suggests some promising areas for further research.
Using optimization costs modeling to maintain performance constraints for the failure mode identification

Failure identification procedures are currently being used for quality control and for the detection of potential failure modes (includes failure location) during the design stage or product producing, Although many methods have given, such as failure modes and effects analysis (FMEA), fault tree analysis (FTA), failure modes, effects and criticality analysis (FMECA), they do not mention the optimization cost and maintain performance constraints for the production. This paper aims give the optimization algorithmic modeling for typical censoring structures to identification failure and analysis the reason. Application to typical example of failure censored data of the motor will be presented and failure identification procedures will be made based on some preliminary research.
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning

Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting the top-ranked nodes and removing the rest nodes to construct a coarsened graph representation. However, local structural information of the removed nodes would be inevitably dropped in these methods, due to the inherent coupling of nodes (location) and their features (signals). In this paper, we propose an enhanced three-stage method via lifting, named LiftPool, to improve hierarchical graph representation by maximally preserving the local structural information in graph pooling. LiftPool introduces an additional stage of graph lifting before graph coarsening to preserve the local information of the removed nodes and decouple the processes of node removing and feature reduction. Specifically, for each node to be removed, its local information is obtained by subtracting the global information aggregated from its neighboring preserved nodes. Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening. Furthermore, we demonstrate that the proposed LiftPool is localized and permutation-invariant. The proposed graph lifting structure is general to be integrated with existing downsampling-based graph pooling methods. Evaluations on benchmark graph datasets show that LiftPool substantially outperforms the state-of-the-art graph pooling methods in the task of graph classification.
We’re Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce consistency-preserving configuration operators (CPCOs)—genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.
Learning to Transfer Role Assignment Across Team Sizes

Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerful approach is to decompose team works into roles, which are ideally assigned to agents with the relevant skills. Training agents to adaptively choose and play emerging roles in a team thus allows the team to scale to complex tasks and quickly adapt to changing environments. These promises, however, have not been fully realised by current role-based multi-agent reinforcement learning methods as they assume either a pre-defined role structure or a fixed team size. We propose a framework to learn role assignment and transfer across team sizes. In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment. We demonstrate that re-using the role-based credit assignment structure can foster the learning process of larger reinforcement learning teams to achieve tasks requiring different roles. Our proposal outperforms competing techniques in enriched role-enforcing Prey-Predator games and in new scenarios in the StarCraft II Micro-Management benchmark.
Probing Simile Knowledge from Pre-trained Language Models

Simile interpretation (SI) and simile generation (SG) are challenging tasks for NLP because models require adequate world knowledge to produce predictions. Previous works have employed many hand-crafted resources to bring knowledge-related into models, which is time-consuming and labor-intensive. In recent years, pre-trained language models (PLMs) based approaches have become the de-facto standard in NLP since they learn generic knowledge from a large corpus. The knowledge embedded in PLMs may be useful for SI and SG tasks. Nevertheless, there are few works to explore it. In this paper, we probe simile knowledge from PLMs to solve the SI and SG tasks in the unified framework of simile triple completion for the first time. The backbone of our framework is to construct masked sentences with manual patterns and then predict the candidate words in the masked position. In this framework, we adopt a secondary training process (Adjective-Noun mask Training) with the masked language model (MLM) loss to enhance the prediction diversity of candidate words in the masked position. Moreover, pattern ensemble (PE) and pattern search (PS) are applied to improve the quality of predicted words. Finally, automatic and human evaluations demonstrate the effectiveness of our framework in both SI and SG tasks.
Some results on Quillen's Conjecture via equivalent-poset techniques

We extend the Main Theorem of Aschbacher and Smith on Quillen's Conjecture from $p>5$ to the remaining odd primes $p = 3,5$. In the process, we develop further combinatorial and homotopical methods for studying the poset of nontrivial elementary abelian $p$-subgroups of a finite group. The techniques lead to a number of further results on the Conjecture, often reducing dependence on the CFSG; in particular, we also provide some partial results toward the case of $p=2$.
Dynamic Registration: Joint Ego Motion Estimation and 3D Moving Object Detection in Dynamic Environment

Localization in a dynamic environment suffers from moving objects. Removing dynamic object is crucial in this situation but become tricky when ego-motion is coupled. In this paper, instead of proposing a new slam framework, we aim at a more general strategy for a localization scenario. In that case, Dynamic Registration is available for integrating with any lidar slam system. We utilize 3D object detection to obtain potential moving objects and remove them temporarily. Then we proposed Dynamic Registration, to iteratively estimate ego-motion and segment moving objects until no static object generates. Static objects are merged with the environment. Finally, we successfully segment dynamic objects, static environments with static objects, and ego-motion estimation in a dynamic environment. We evaluate the performance of our proposed method on KITTI Tracking datasets. Results show stable and consistent improvements based on other classical registration algorithms.
Evidence for Modification of b Quark Hadronization in High-Multiplicity pp Collisions at sqrt[s]=13  TeV.

The production rate of B_{s}^{0} mesons relative to B^{0} mesons is measured by the LHCb experiment in pp collisions at a center-of-mass energy sqrt[s]=13 TeV over the forward rapidity interval 2<y<4.5 as a function of the charged particle multiplicity measured in the event. Evidence at the 3. 4σ level is found for an increase of the ratio of B_{s}^{0} to B^{0} cross sections with multiplicity at transverse momenta below 6 GeV/c, with no significant multiplicity dependence at higher transverse momentum. Comparison with data from e^{+}e^{-} collisions implies that the density of the hadronic medium may affect the production rates of B mesons. This is qualitatively consistent with the emergence of quark coalescence as an additional hadronization mechanism in high-multiplicity collisions.
The symmetry for two class of steady stratified periodic water waves

In this paper, we mainly consider two class of travelling stratiﬁed periodic water waves, one with negative (or without) surface tension and the other with constant Bernoulli’s function and stagnation points. We ﬁrst establish the symmetry result for stratiﬁed water waves with negative (or without) surface tension, but without stagnation by using the modiﬁed maximum principle. Furthermore, the symmetry property of stratiﬁed water waves with constant Bernoulli’s function and stagnation points is also obtained provided the monotonic property is known.
Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization

Established approaches to obtain generalization bounds in data-driven optimization and machine learning mostly build on solutions from empirical risk minimization (ERM), which depend crucially on the functional complexity of the hypothesis class. In this paper, we present an alternate route to obtain these bounds on the solution from distributionally robust optimization (DRO), a recent data-driven optimization framework based on worst-case analysis and the notion of ambiguity set to capture statistical uncertainty. In contrast to the hypothesis class complexity in ERM, our DRO bounds depend on the ambiguity set geometry and its compatibility with the true loss function. Notably, when using statistical distances such as maximum mean discrepancy, Wasserstein distance, or $\phi$-divergence in the DRO, our analysis implies generalization bounds whose dependence on the hypothesis class appears the minimal possible: The bound depends solely on the true loss function, independent of any other candidates in the hypothesis class. To our best knowledge, it is the first generalization bound of this type in the literature, and we hope our findings can open the door for a better understanding of DRO, especially its benefits on loss minimization and other machine learning applications.
Protecting android devices following BYOD policy against data security and privacy attacks

Bring Your Own Device (BYOD) is an IT policy being adopted by corporate organizations worldwide. It permits the employees to bring their own devices like smartphones, tablets, etc to their place of work and use them to access the privileged corporate information while being both inside and outside their place of work. Therefore, employees use the same device for their personal and office work. Such a corporate policy brings in a number of advantages like increased employee productivity, improved employee satisfaction, and reduction in corporate expenses. However, one of the major concerns in implementing such a policy is data security and privacy. Permitting employees to access the privileged corporate information on their personal device can lead to pertinent corporate data being compromised. On the other hand, employees are apprehensive that the corporate organizations may spy or track their personal cyber activities. Existing solutions for BYOD can be categorized into Mobile Device Management (MDM)-based and Network Access Control (NAC)-based. MDM-based solutions are comprised of a client software, which runs on the users’ mobile devices 24/7 monitoring, securing, and managing the mobile device from a corporate-based server. Such a solution could lead to breach of employees’ privacy and extensive battery drainage. NAC-based solutions assumes the use of corporate network. Such a solution is not full proof because the corporate data is at a security risk, when the device is not connected to the corporate network. In this study, we formulate a generic BYOD policy from a corporate data security perspective, study the possible security breaches on that policy from Android devices’ perspective, and propose ways to defend against them. We propose a solution architecture for Android-based mobile devices. Our approach, unlike the existing BYOD solutions, provides data security, preserves privacy, and consumes less energy. Our approach successfully detects (a) root status of the device and (b) malicious apps, which steal information or subvert information. Our detection is 70 % accurate when tested on real malicious applications. Finally, we present limitations of our approach.
Assessment of Listeria monocytogenes Surface Proteins Identified from Proteomics Analysis for Use as Diagnostic Biomarkers

Strains of Listeria monocytogenes are differentiated serologically into at least 13 serotypes and grouped phylogenetically into 4 distinct lineages (I, II, III, and IV). No single monoclonal antibody (MAb) reported to date is capable of binding to the surface of L. monocytogenes strains representing all the serotypes. ABSTRACT The Gram-positive bacterium Listeria monocytogenes is an important pathogen that causes a foodborne illness with a high percentage of fatalities. Surface proteins, specifically expressed from a wide range of L. monocytogenes serotypes under selective enrichment culture conditions, can serve as targets for the detection and isolation of this pathogen using antibody-based methods. Among a number of surface proteins identified by mass spectrometry in a previous proteomic study, six candidates (annotated as LMOf2365_0148, LMOf2365_0312, LMOf2365_0546, LMOf2365_1883, LMOf2365_2111, and LMOf2365_2742) were selected here for investigating their expression in the bacterial cells cultured in vitro by raising rabbit polyclonal antibodies (PAbs) against the recombinant form of each candidate. These protein candidates contained regions conserved among various L. monocytogenes isolates but variable in other Listeria species. LMOf2365_0148, an uncharacterized protein with a LPXTG motif accountable for covalent linkage to the cell wall peptidoglycan, exhibited a strong reaction signal from anti-LMOf2365_0148 PAb binding to the cell surface, as detected by immunofluorescence microscopy. Further study, through the generation of a panel of mouse monoclonal antibodies (MAbs) to the recombinant LMOf2365_0148, showed that one of the MAbs, M3686, reacted to bacterial isolates belonging to all three lineages of L. monocytogenes under Health Canada’s standard enrichment culture conditions (MFHPB-07 and MFHPB-30). These results demonstrated the potential of using LMOf2365_0148 as a surface biomarker, in conjunction with specific MAbs developed here, for the isolation and detection of L. monocytogenes from foods and food processing environments. IMPORTANCE Strains of Listeria monocytogenes are differentiated serologically into at least 13 serotypes and grouped phylogenetically into 4 distinct lineages (I, II, III, and IV). No single monoclonal antibody (MAb) reported to date is capable of binding to the surface of L. monocytogenes strains representing all the serotypes. This study assessed the expression of six surface proteins selected from a previous proteomic study and demonstrated that surface protein LMOf2365_0148 has the greatest potential as a surface biomarker. A panel of 24 MAbs to LMOf2365_0148 were assessed extensively, revealing that one of the MAbs, M3686, reacted to a wide range of L. monocytogenes isolates (lineage I, II, and III isolates) grown under standard enrichment culture conditions and thus led to the conclusion that LMOf2365_0148 is a useful novel surface biomarker for identifying, detecting, and isolating the pathogen from food and environmental samples.
Rescuing ReRAM-based Neural Computing Systems from Device Variation

Resistive random-access memory (ReRAM)-based crossbar array (RCA) is a promising platform to accelerate vector-matrix multiplication in deep neural networks (DNNs). There are, however, some practical issues, especially device variation, that hinder the versatile development of ReRAM in neural computing systems. The device variations include device-to-device variation (DDV) and cycle-to-cycle variation (CCV) that deviate the devise resistance in the RCA from their target state. Such resistance deviation seriously degrades the inference accuracy of DNNs. To address this issue, we propose a software-hardware compensation solution that includes compensation training based on scale factors (CTSF) and variation-aware compensation training based on scale factors (VACTSF) to protect the ReRAM-based DNN accelerator against device variation. The scale factors in CTSF can be flexibly set for reducing accuracy loss due to device variation when the weights programmed into RCA are determined. For effectively handling CCV, the scale factors are introduced into the training process for obtaining variation-tolerant weights by leveraging the inherent self-healing ability of DNNs. Simulation results based on our method confirm that the accuracy losses due to device variation on LeNet-5, ResNet, and VGG16 with different datasets are less than 5% under a large device variation by CTSF. More robust weights for conquering CCV are also obtained by VACTSF. The simulation results present that our method is competitive in comparison to other variation-tolerant methods.
A Survey on Empirical Security Analysis of Access-control Systems: A Real-world Perspective

There any many different access-control systems, yet a commonality is that they provide flexible mechanisms to enforce different access levels. Their importance in organisations to adequately restrict resources, coupled with their use in a dynamic environment, mandates the need to routinely perform policy analysis. The aim of performing analysis is often to identify potential problematic permissions, which have the potential to be exploited and could result in data theft and unintended modification. There is a vast body of published literature on analysing access-control systems, yet as performing analysis has a strong end-user motivation and is grounded in security challenges faced in real-world systems, it is important to understand how research is developing, what are the common themes of interest, and to identify key challenges that should be addressed in future work. To the best of the authors’ knowledge, no survey has been performed to gain an understanding of empirical access-control analysis, focussing on how techniques are evaluated and how they align to the needs of real-world analysis tasks. This article provides a systematic literature review, identifying and summarising key works. Key findings are identified and discussed as areas of future work.