Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency Regulation With Thermostatically Controlled Loads: Aggregation of Dynamics and Synchronization

Thermostatically controlled loads (TCLs) can provide ancillary services to the power network by aiding existing frequency-control mechanisms. TCLs are, however, characterized by an intrinsic limit cycle behavior, which raises the risk that these could synchronize when coupled with the frequency dynamics of the power grid, i.e., simultaneously switch, inducing persistent and possibly catastrophic power oscillations. To address this problem, schemes with a randomized response time in their control policy have been proposed in the literature. However, such schemes introduce delays in the response of TCLs to frequency feedback that may limit their ability to provide fast support at urgencies. In this article, we present a deterministic control mechanism for TCLs such that those switch when prescribed frequency thresholds are exceeded in order to provide ancillary services to the power network. For the considered scheme, we provide analytic conditions, which ensure that synchronization is avoided. In particular, we show that as the number of loads tends to infinity, there exist arbitrarily long time intervals where the frequency deviations are arbitrarily small. Our analytical results are verified with simulations on the Northeast Power Coordinating Council 140-bus system, which demonstrate that the proposed scheme offers improved frequency response compared with existing implementations.
Two-component Jets of GRB 160623A as Shocked Jet Cocoon Afterglow

Two components of jets associated with the afterglow of the gamma-ray burst (GRB) 160623A were observed with multifrequency observations including long-term monitoring in a submillimeter range (230 GHz) using the Submillimeter Array. The observed light curves with temporal breaks suggest on the basis of the standard forward-shock synchrotron-radiation model that the X-ray radiation is narrowly collimated with an opening angle , whereas the radio radiation originated from wider jets (∼27°). The temporal and spectral evolutions of the radio afterglow agree with those expected from a synchrotron-radiation modeling with typical physical parameters, except for the fact that the observed wide jet opening angle for the radio emission is significantly larger than the theoretical maximum opening angle. By contrast, the opening angle of the X-ray afterglow is consistent with the typical value of GRB jets. Since the theory of the relativistic cocoon afterglow emission is similar to that of a regular afterglow with an opening angle of ∼30°, the observed radio emission can be interpreted as the shocked jet cocoon emission. This result therefore indicates that the two components of the jets observed in the GRB 160623A afterglow are caused by the jet and the shocked jet cocoon afterglows.
HybridCite: A Hybrid Model for Context-Aware Citation Recommendation

Citation recommendation systems aim to recommend citations for either a complete paper or a small portion of text called a citation context. The process of recommending citations for citation contexts is called local citation recommendation and is the focus of this paper. Firstly, we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques. We combine, for the first time to the best of our knowledge, the best-performing algorithms into a semi-genetic hybrid recommender system for citation recommendation. We evaluate the single approaches and the hybrid approach offline based on several data sets, such as the Microsoft Academic Graph (MAG) and the MAG in combination with arXiv and ACL. We further conduct a user study for evaluating our approaches online. Our evaluation results show that a hybrid model containing embedding and information retrieval-based components outperforms its individual components and further algorithms by a large margin.
Hybrid Embedded Deep Stacked Sparse Autoencoder with w_LPPD SVM Ensemble

Deep learning is a kind of feature learning method with strong nonliear feature transformation and becomes more and more important in many fields of artificial intelligence. Deep autoencoder is one representative method of the deep learning methods, and can effectively extract abstract the information of datasets. However, it does not consider the complementarity between the deep features and original features during deep feature transformation. Besides, it suffers from small sample problem. In order to solve these problems, a novel deep autoencoder - hybrid feature embedded stacked sparse autoencoder(HESSAE) has been proposed in this paper. HFESAE is capable to learn discriminant deep features with the help of embedding original features to filter weak hidden-layer outputs during training. For the issue that class representation ability of abstract information is limited by small sample problem, a feature fusion strategy has been designed aiming to combining abstract information learned by HFESAE with original feature and obtain hybrid features for feature reduction. The strategy is hybrid feature selection strategy based on L1 regularization followed by an support vector machine(SVM) ensemble model, in which weighted local discriminant preservation projection (w_LPPD), is designed and employed on each base classifier. At the end of this paper, several representative public datasets are used to verify the effectiveness of the proposed algorithm. The experimental results demonstrated that, the proposed feature learning method yields superior performance compared to other existing and state of art feature learning algorithms including some representative deep autoencoder methods.
Tools and Techniques for Malware Detection and Analysis

One of the major and serious threats that the Internet faces today is the vast amounts of data and files which need to be evaluated for potential malicious intent. Malicious software, often referred to as a malware that are designed by attackers are polymorphic and metamorphic in nature which have the capability to change their code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malware into their known families using machine learning techniques. This survey paper provides an overview of techniques and tools for detecting and analyzing the malware.
Lepton-flavor-violating decays of the SM-like Higgs boson $h\rightarrow e_ie_j$, and $e_i \rightarrow e_j\, \gamma $ in a flipped 3-3-1 model

In the framework of the flipped 3-3-1 model introduced recently [R. M. Fonseca and M. Hirsch, J. High Energy Phys. 1608, 003 (2016)], the lepton-flavor-violating (LFV) decay $\mu \rightarrow 3e$ was predicted to have a large branching ratio (Br) close to the recent experimental limit. We will show that the Br of LFV decays of the standard-model-like (SM-like) Higgs boson decays (LFVHD) Br$(h\rightarrow e_ae_b)$ may also be large. Namely, Br$(h\rightarrow \mu\tau,e\tau)$ can reach values of $\mathcal{O}(10^{-4}){-}\mathcal{O}(10^{-5})$, which will reach the upcoming experimental sensitivities. On the other hand, for LFV decays of charged leptons (cLFV) $(e_b\rightarrow e_a\gamma)$, the branching ratios are well below experimental bounds.
Gaussian prepivoting for finite population causal inference

In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, hypotheses which impute the missing potential outcomes. Oftentimes inference is instead desired for the weak null that the sample average of the treatment effects takes on a particular value while leaving the subject‐specific treatment effects unspecified. Tests valid for sharp null hypotheses can be anti‐conservative should only the weak null hold. We develop a general framework for unifying modes of inference for sharp and weak nulls, wherein a single procedure simultaneously delivers exact inference for sharp nulls and asymptotically valid inference for weak nulls. We employ randomization tests based upon prepivoted test statistics, wherein a test statistic is first transformed by a suitably constructed cumulative distribution function and its randomization distribution assuming the sharp null is then enumerated. For a large class of test statistics, we show that prepivoting may be accomplished by employing the push‐forward of a sample‐based Gaussian measure based upon a suitable covariance estimator. The approach enumerates the randomization distribution (assuming the sharp null) of a p‐value for a large‐sample test known to be valid under the weak null, and uses the resulting randomization distribution for inference. The versatility of the method is demonstrated through many examples, including rerandomized designs and regression‐adjusted estimators in completely randomized designs.
Performant TCP for Low-Power Wireless Networks

Low-power and lossy networks (LLNs) enable diverse applications integrating many resource-constrained embedded devices, often requiring interconnectivity with existing TCP/IP networks as part of the Internet of Things. But TCP has received little attention in LLNs due to concerns about its overhead and performance, leading to LLN-specific protocols that require specialized gateways for interoperability. We present a systematic study of a well-designed TCP stack in IEEE 802.15. 4-based LLNs, based on the TCP protocol logic in FreeBSD. Through careful implementation and extensive experiments, we show that modern low-power sensor platforms are capable of running full-scale TCP and that TCP, counter to common belief, performs well despite the lossy nature of LLNs. By carefully studying the interaction between the transport and link layers, we identify subtle but important modifications to both, achieving TCP goodput within 25% of an upper bound (5-40x higher than prior results) and low-power operation commensurate to CoAP in a practical LLN application scenario. This suggests that a TCP-based transport layer, seamlessly interoperable with existing TCP/IP networks, is viable and performant in LLNs.
Latent Variable Modelling with Hyperbolic Normalizing Flows

The choice of approximate posterior distributions plays a central role in stochastic variational inference (SVI). One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions. However, one key limitation of existing normalizing flows is that they are restricted to the Euclidean space and are ill-equipped to model data with an underlying hierarchical structure. To address this fundamental limitation, we present the first extension of normalizing flows to hyperbolic spaces. We first elevate normalizing flows to hyperbolic spaces using coupling transforms defined on the tangent bundle, termed Tangent Coupling ($\mathcal{TC}$). We further introduce Wrapped Hyperboloid Coupling ($\mathcal{W}\mathbb{H}C$), a fully invertible and learnable transformation that explicitly utilizes the geometric structure of hyperbolic spaces, allowing for expressive posteriors while being efficient to sample from. We demonstrate the efficacy of our novel normalizing flow over hyperbolic VAEs and Euclidean normalizing flows. Our approach achieves improved performance on density estimation, as well as reconstruction of real-world graph data, which exhibit a hierarchical structure. Finally, we show that our approach can be used to power a generative model over hierarchical data using hyperbolic latent variables.
$A_4$ realization of left-right symmetric linear seesaw

We explore an $A_4$-symmetric flavor based left-right symmetric model with linear seesaw mechanism and study the associated neutrino phenomenology. The framework offers the advantage of studying neutrino mass, non-unitarity effects in lepton sector, lepton flavour violation and CP violation. The fermion content of the model includes usual quarks, leptons along with additional sterile fermion per generation while the scalar content includes Higgs doublets and scalar bidoublet. We study analytically as well as numerically the correlation between different model parameters and their dependence on experimentally determined neutrino observables.
Learning Pregrasp Manipulation of Objects from Ungraspable Poses

In robotic grasping, objects are often occluded in ungraspable configurations such that no feasible grasp pose can be found, e.g. large flat boxes on the table that can only be grasped once lifted. Inspired by human bimanual manipulation, e.g. one hand to lift up things and the other to grasp, we address this type of problems by introducing pregrasp manipulation – push and lift actions. We propose a model-free Deep Reinforcement Learning framework to train feedback control policies that utilize visual information and proprioceptive states of the robot to autonomously discover robust pregrasp manipulation. The robot arm learns to push the object first towards a support surface and then lift up one side of the object, creating an object-table clearance for possible grasping solutions. Furthermore, we show the robustness of the proposed learning framework in training pregrasp policies that can be directly transferred to a real robot. Lastly, we evaluate the effectiveness and generalization ability of the learned policy in real-world experiments, and demonstrate pregrasp manipulation of objects with various sizes, shapes, weights, and surface friction.
Distributed Sketching Methods for Privacy Preserving Regression

In this work, we study distributed sketching methods for large scale regression problems. We leverage multiple randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distributed systems. We derive novel approximation guarantees for classical sketching methods and analyze the accuracy of parameter averaging for distributed sketches. We consider random matrices including Gaussian, randomized Hadamard, uniform sampling and leverage score sampling in the distributed setting. Moreover, we propose a hybrid approach combining sampling and fast random projections for better computational efficiency. We illustrate the performance of distributed sketches in a serverless computing platform with large scale experiments.
Structural modeling of 2019-novel coronavirus (nCoV) spike protein reveals a proteolytically-sensitive activation loop as a distinguishing feature compared to SARS-CoV and related SARS-like coronaviruses

The 2019 novel coronavirus (2019-nCoV) is currently causing a widespread outbreak centered on Hubei province, China and is a major public health concern. Taxonomically 2019-nCoV is closely related to SARS-CoV and SARS-related bat coronaviruses, and it appears to share a common receptor with SARS-CoV (ACE-2). Here, we perform structural modeling of the 2019-nCoV spike glycoprotein. Our data provide support for the similar receptor utilization between 2019-nCoV and SARS-CoV, despite a relatively low amino acid similarity in the receptor binding module. Compared to SARS-CoV, we identify an extended structural loop containing basic amino acids at the interface of the receptor binding (S1) and fusion (S2) domains, which we predict to be proteolytically-sensitive. We suggest this loop confers fusion activation and entry properties more in line with MERS-CoV and other coronaviruses, and that the presence of this structural loop in 2019-nCoV may affect virus stability and transmission.
Low-PMEPR Preamble Sequence Design for Dynamic Spectrum Allocation in OFDMA Systems

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks.
Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined

Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. Identity, gender, and viewpoint codes were studied at the "neural unit" and ensemble levels of a face-identification network. At the unit level, identification, gender classification, and viewpoint estimation were measured by deleting units to create variably-sized, randomly-sampled subspaces at the top network layer. Identification of 3,531 identities remained high (area under the ROC approximately 1.0) as dimensionality decreased from 512 units to 16 (0.95), 4 (0.80), and 2 (0.72) units. Individual identities separated statistically on every top-layer unit. Cross-unit responses were minimally correlated, indicating that units code non-redundant identity cues. This "distributed" code requires only a sparse, random sample of units to identify faces accurately. Gender classification declined gradually and viewpoint estimation fell steeply as dimensionality decreased. Individual units were weakly predictive of gender and viewpoint, but ensembles proved effective predictors. Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes. At the ensemble level, principal component analysis of face representations showed that identity, gender, and viewpoint information separated into high-dimensional subspaces, ordered by explained variance. Identity, gender, and viewpoint information contributed to all individual unit responses, undercutting a neural tuning analogy for face attributes. Interpretation of neural-like codes from DCNNs, and by analogy, high-level visual codes, cannot be inferred from single unit responses. Instead, "meaning" is encoded by directions in the high-dimensional space.
The Minimum Hybrid Contract (MHC): Combining Legal and Blockchain Smart Contracts

Corruption is a major global financial problem with billions of dollars rendered lost or unaccountable annually. Corruption through contract fraud is often conducted by withholding and/or altering financial information. When such scandals are investigated by authorities, financial and legal documents are usually altered to conceal the paper trail. Smart contracts have emerged in recent years and appear promising for applications such as legal contracts where transparency is critical and of public interest. Transparency and auditability are inherent because smart contracts execute operations on the blockchain, a distributed public ledger. In this paper, we propose the Minimum Hybrid Contract (MHC), with the aim of introducing 1) auditability, 2) transparency, and 3) immutability to the contract's financial transactions. The MHC comprises an online smart contract and an offline traditional legal contract. where the two are immutably linked. Secure peer-to-peer financial transactions, transparency, and cost accounting are automated by the smart contract, and legal issues or disputes are carried out by civil courts. The reliance on established legal processes facilitates an appropriate adoption of smart contracts in traditional contracts.
Accurate Stress Assessment based on functional Near Infrared Spectroscopy using Deep Learning Approach

Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. In this study, signals produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded from 10 healthy volunteers are employed to assess the stress induced by the Montreal Imaging Stress Task by means of a deep learning system. The proposed deep learning system consists of two main parts: First, the one-dimensional convolutional neural network is employed to build informative feature maps. Then, a stack of deep fully connected layers is used to predict the stress existence probability. Experiment results showed that the trained fNIRS model performs stress classification by achieving 88.52 ± 0.77% accuracy. Employment of the proposed deep learning system trained on the fNIRS measurements leads to higher stress classification accuracy than the existing methods proposed in fNIRS studies in which the same experimental procedure has been employed. The proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time stress assessment.
Serial Speakers: a Dataset of TV Series

For over a decade, TV series have been drawing increasing interest, both from the audience and from various academic fields. But while most viewers are hooked on the continuous plots of TV serials, the few annotated datasets available to researchers focus on standalone episodes of classical TV series. We aim at filling this gap by providing the multimedia/speech processing communities with “Serial Speakers”, an annotated dataset of 155 episodes from three popular American TV serials: “Breaking Bad”, “Game of Thrones” and “House of Cards”. “Serial Speakers” is suitable both for investigating multimedia retrieval in realistic use case scenarios, and for addressing lower level speech related tasks in especially challenging conditions. We publicly release annotations for every speech turn (boundaries, speaker) and scene boundary, along with annotations for shot boundaries, recurring shots, and interacting speakers in a subset of episodes. Because of copyright restrictions, the textual content of the speech turns is encrypted in the public version of the dataset, but we provide the users with a simple online tool to recover the plain text from their own subtitle files.
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning to mitigate the interference among concurrent transmissions in wireless networks. We equip each transmitter in the network with a deep RL agent, which receives partial delayed observations from its own associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power level to use at each scheduling interval. We propose a scalable agent design, where the dimensions of its observation and action spaces do not vary with changes in the environment configuration, e.g., in terms of number of transmitter and user nodes. Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5th percentile user rates, while achieving performance close to, and even in certain cases outperforming, that of a centralized baseline.