Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological Origins of Shared Perceptions of Troublesome Teen Groups: Implications for the Basic Systemic Model of Crime, the Incivilities Thesis, and Political Economy

This work investigates how community variation in perceptions of troublesome teen groups are shaped by delinquency, violent crime, and community socioeconomic status (SES). Experts consider this outcome the key indicator of impaired local supervisory control, and past work has confirmed its critical role in linking community structure to crime and victimization outcomes. The investigation responds to recent calls to learn more about impacts of crime on key community processes. Analyses of Philadelphia survey, census, violent crime, and delinquency data find strong impacts of SES. Impacts of crime and delinquency are significant but depend on how they are separated from SES. Influences of the spatially lagged outcome and partialled SES highlight connections between public and parochial control dynamics. These deserve closer theoretical scrutiny in both the basic systemic model of crime and the incivilities thesis.
A combined method for determining reaction paths, minima, and transition state geometries

Mapping out a reaction mechanism involves optimizing the reactants and products, finding the transition state and following the reaction path connecting them. Transition states can be difficult to locate and reaction paths can be expensive to follow. We describe an efficient algorithm for determining the transition state, minima and reaction path in a single procedure. Starting with an approximate path represented by N points, the path is iteratively relaxed until one of the N points reached the transition state, the end points optimize to minima and the remaining points converged to a second order approximation of the steepest descent path. The method appears to be more reliable than conventional transition state optimization algorithms, and requires only energies and gradients, but not second derivative calculations. The procedure is illustrated by application to a number of model reactions. In most cases, the reaction mechanism can be described well using 5 to 7 points to represent the transition state.
Cost vs. Information Tradeoffs for Treasure Hunt in the Plane

A mobile agent has to find an inert treasure hidden in the plane. Both the agent and the treasure are modeled as points. This is a variant of the task known as treasure hunt. The treasure is at a distance at most $D$ from the initial position of the agent, and the agent finds the treasure when it gets at distance $r$ from it, called the {\em vision radius}. However, the agent does not know the location of the treasure and does not know the parameters $D$ and $r$. The cost of finding the treasure is the length of the trajectory of the agent. We investigate the tradeoffs between the amount of information held {\em a priori} by the agent and the cost of treasure hunt. Following the well-established paradigm of {\em algorithms with advice}, this information is given to the agent in advance as a binary string, by an oracle cooperating with the agent and knowing the location of the treasure and the initial position of the agent. The size of advice given to the agent is the length of this binary string. For any size $z$ of advice and any $D$ and $r$, let $OPT(z,D,r)$ be the optimal cost of finding the treasure for parameters $z$, $D$ and $r$, if the agent has only an advice string of length $z$ as input. We design treasure hunt algorithms working with advice of size $z$ at cost $O(OPT(z,D,r))$ whenever $r\leq 1$ or $r\geq 0.9D$. For intermediate values of $r$, i.e., $1 0$, the treasure can be found at cost $O(OPT(z,D,r)^{1+\alpha})$.
On the Performance of NOMA-based Cooperative Relaying with Receive Diversity

Non-orthogonal multiple access (NOMA) is widely recognized as a potential multiple access (MA) technology for efficient spectrum utilization in the fifth-generation (5G) wireless standard. In this paper, we present the achievable sum rate analysis of a cooperative relaying system (CRS) using NOMA with two different receive diversity schemes - selection combining (SC), where the antenna with highest instantaneous signal-to-noise ratio (SNR) is selected, and maximal-ratio combining (MRC). We also present the outage probability and diversity analysis for the CRS-NOMA system. Analytical results confirm that the CRS-NOMA system outperforms the CRS with conventional orthogonal multiple access (OMA) by achieving higher spectral efficiency at high transmit SNR and achieves a full diversity order.
Stable Fractional Matchings

We study a generalization of the classical stable matching problem that allows for cardinal preferences (as opposed to ordinal) and fractional matchings (as opposed to integral). After observing that, in this cardinal setting, stable fractional matchings can have much higher social welfare than stable integral ones, our goal is to understand the computational complexity of finding an optimal (i.e., welfare-maximizing) or nearly-optimal stable fractional matching. We present simple approximation algorithms for this problem with weak welfare guarantees and, rather unexpectedly, we furthermore show that achieving better approximations is hard. This computational hardness persists even for approximate stability. To the best of our knowledge, these are the first computational complexity results for stable fractional matchings. En route to these results, we provide a number of structural observations.
Interdecadal variability of the Arabian Sea and the Indian Ocean

On the base of reanalysis data and station measurements during the last century, the spatial-temporal distribution of ∼ 60 yr and other interdecadal oscillations for the part of hydrometeorological, hydrophysical and hydrobiological parameters of the Arabian Sea and the Indian Ocean was studied. Their dominant climate trends at present time are shown for the different parts of the region. We were also investigated periodicities in the time series of various marine and terrestrial paleo-reconstructions having duration up to 5000 yrs based of the study of marine sediments, coral reefs, dendrochronology, stalagmites, ice cores which are both from this region and the adjacent areas, as well as the Wolf number. The climatic variability of an Indian Ocean Dipole (IOD) index was analyzed. It has been shown that the ∼ 60-yr oscillations play an important role in the variability of the monsoon over the Arabian Sea and the Indian Ocean.
Metabolic basis of brain-like electrical signalling in bacterial communities

Information processing in the mammalian brain relies on a careful regulation of the mem-brane potential dynamics of its constituent neurons, which propagates across the neuronal tissue via electrical signalling. We recently reported the existence of electrical signalling in a much simpler organism, the bacterium Bacillus subtilis. In dense bacterial communi-ties known as biofilms, nutrient-deprived B. subtilis cells in the interior of the colony use electrical communication to transmit stress signals to the periphery, which interfere with the growth of peripheral cells and reduce nutrient consumption, thereby relieving stress from the interior. Here we explicitly address the interplay between metabolism and elec-trophysiology in bacterial biofilms, by introducing a spatially-extended mathematical model that combines the metabolic and electrical components of the phenomenon in a discretised reaction-diffusion scheme. The model is experimentally validated by environmental and ge-netic perturbations, and confirms that metabolic stress is transmitted through the bacterial population via a potassium wave. Interestingly, this behaviour is reminiscent of cortical spreading depression in the brain, characterised by a wave of electrical activity mediated by potassium diffusion that has been linked to various neurological disorders, calling for future studies on the evolutionary link between the two phenomena.
SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement

Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). However, most conventional KG embedding models only focus on KG completion with an unreasonable assumption that all facts in KG hold without noises, ignoring error detection which also should be significant and essential for KG refinement.In this paper, we propose a novel support-confidence-aware KG embedding framework (SCEF), which implements KG completion and correction simultaneously by learning knowledge representations with both triple support and triple confidence. Specifically, we build model energy function by incorporating conventional translation-based model with support and confidence. To make our triple support-confidence more sufficient and robust, we not only consider the internal structural information in KG, studying the approximate relation entailment as triple confidence constraints, but also the external textual evidence, proposing two kinds of triple supports with entity types and descriptions respectively.Through extensive experiments on real-world datasets, we demonstrate SCEF's effectiveness.
$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network (<inline-formula> <tex-math notation="LaTeX">$\mathcal {R}^{2}$ </tex-math></inline-formula>-CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector. Tiny-Net is a lightweight residual structure, which enables fast and powerful features extraction from inputs. Global attention block is built upon Tiny-Net to inhibit false positives. Classifier is then used to predict the existence of target in each patch, and detector is followed to locate them accurately if available. The classifier and detector are mutually reinforced with end-to-end training, which further speed up the process and avoid false alarms. Effectiveness of <inline-formula> <tex-math notation="LaTeX">$\mathcal {R}^{2}$ </tex-math></inline-formula>-CNN is validated on hundreds of GF-1 images and GF-2 images that are <inline-formula> <tex-math notation="LaTeX">$18\,000 \times 18\,192$ </tex-math></inline-formula> pixels, 2.0-m resolution, and <inline-formula> <tex-math notation="LaTeX">$27\,620 \times 29\,200$ </tex-math></inline-formula> pixels, 0.8-m resolution, respectively. Specifically, we can process a GF-1 image in 29.4 s on Titian X just with single thread. According to our knowledge, no previous solution can detect the tiny object on such huge remote sensing images gracefully. We believe that it is a significant step toward practical real-time remote sensing systems.
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system. In this paper, we jointly learn the model of recommendation and knowledge graph completion. Distinct from previous KG-based recommendation methods, we transfer the relation information in KG, so as to understand the reasons that a user likes an item. As an example, if a user has watched several movies directed by (relation) the same person (entity), we can infer that the director relation plays a critical role when the user makes the decision, thus help to understand the user's preference at a finer granularity. Technically, we contribute a new translation-based recommendation model, which specially accounts for various preferences in translating a user to an item, and then jointly train it with a KG completion model by combining several transfer schemes. Extensive experiments on two benchmark datasets show that our method outperforms state-of-the-art KG-based recommendation methods. Further analysis verifies the positive effect of joint training on both tasks of recommendation and KG completion, and the advantage of our model in understanding user preference. We publish our project at https://github.com/TaoMiner/joint-kg-recommender.
A new Potential-Based Reward Shaping for Reinforcement Learning Agent

Potential-based reward shaping (PBRS) is a particular category of machine learning methods that aims to improve the learning speed of a reinforcement learning agent by extracting and utilizing extra knowledge while performing a task. There are two steps in the transfer learning process: extracting knowledge from previously learned tasks and transferring that knowledge to use it in a target task. The latter step is well discussed in the literature, with various methods being proposed for it, while the former has been explored less. With this in mind, the type of knowledge that is transmitted is very important and can lead to considerable improvement. Among the literature of both transfer learning and potential-based reward shaping, a subject that has never been addressed is the knowledge gathered during the learning process itself. In this paper, we presented a novel potential-based reward shaping method that attempted to extract knowledge from the learning process. The proposed method extracts knowledge from episodes' cumulative rewards. The proposed method has been evaluated in the Arcade learning environment, and the results indicate an improvement in the learning process in both the single-task and the multi-task reinforcement learner agents.
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality

Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information. Stereo matching is a computer vision based approach for 3-D scene reconstruction. In this paper, we explore an improved stereo matching network, SLED-Net, in which a Single Long Encoder-Decoder is proposed to replace the stacked hourglass network in PSM-Net for better contextual information learning. We compare SLED-Net to state-of-the-art methods recently published, and demonstrate its superior performance on Scene Flow and KITTI2015 test sets.
Binary Debloating for Security via Demand Driven Loading

Modern software systems heavily use C/C++ based libraries. Because of the weak memory model of C/C++, libraries may suffer from vulnerabilities which can expose the applications to potential attacks. For example, a very large number of return oriented programming gadgets exist in glibc that allow stitching together semantically valid but malicious Turing-complete programs. In spite of significant advances in attack detection and mitigation, full defense is unrealistic against an ever-growing set of possibilities for generating such malicious programs. In this work, we create a defense mechanism by debloating libraries to reduce the dynamic functions linked so that the possibilities of constructing malicious programs diminishes significantly. The key idea is to locate each library call site within an application, and in each case to load only the set of library functions that will be used at that call site. This approach of demand-driven loading relies on an input-aware oracle that predicts a near-exact set of library functions needed at a given call site during the execution. The predicted functions are loaded just in time, and the complete call chain (of function bodies) inside the library is purged after returning from the library call back into the application. We present a decision-tree based predictor, which acts as an oracle, and an optimized runtime system, which works directly with library binaries like GNU libc and libstdc++. We show that on average, the proposed scheme cuts the exposed code surface of libraries by 97.2%, reduces ROP gadgets present in linked libraries by 97.9%, achieves a prediction accuracy in most cases of at least 97%, and adds a small runtime overhead of 18% on all libraries (16% for glibc, 2% for others) across all benchmarks of SPEC 2006, suggesting this scheme is practical.
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction

Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of structure information of MRI images that is crucial for clinical diagnosis. To tackle this problem, we propose the Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at both local and global scale. SEGAN defines a new structure regularization called Patch Correlation Regularization (PCR) which allows for efficient extraction of structure information. In addition, to further enhance the ability to uncover structure information, we propose a novel generator SU-Net by incorporating multiple-scale convolution filters into each layer. Besides, we theoretically analyze the convergence of stochastic factors contained in training process. Experimental results show that SEGAN is able to learn target structure information and achieves state-of-theart performance for CS-MRI reconstruction.
Do Financial Incentives Affect the Quality of Expert Performance? Evidence from the Racetrack

Does the quality of performance by experts respond to financial incentives? I provide some new evidence on this question by examining the propensity of racehorse trainers to undertake effort-diverting actions. In a sample of 30426 horse races, I find that lower race stakes are strongly associated with more unexpected outcomes, consistent with more trainers exerting less-than-full effort in such races. These results continue to hold when low-information races are excluded from the sample, thereby ruling out the possibility that stake is simply a proxy for the level of information that is available to bettors. Moreover, in a sub-sample of 4416 races for which final odds data are available, the dispersion in odds is positively related to race stake, consistent with rational bettors recognising the incentives faced by trainers and incorporating this insight in their investments. As a group, horse trainers apparently tailor the quality of their services to the potential size of their remuneration from clients.
Transient Typechecks Are (Almost) Free

Transient gradual typing imposes run-time type tests that typically cause a linear slowdown in programs' performance. This performance impact discourages the use of type annotations because adding types to a program makes the program slower. A virtual machine can employ standard just-in-time optimizations to reduce the overhead of transient checks to near zero. These optimizations can give gradually-typed languages performance comparable to state-of-the-art dynamic languages, so programmers can add types to their code without affecting their programs' performance.
Data Management in Networked Industrial Environments: State of the Art and Open Challenges

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article are based on an exhaustive research of recent scientific publications and thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.
Intra- and Inter-epoch Temporal Context Network (IITNet) for Automatic Sleep Stage Scoring

This study proposes a novel deep learning model, called IITNet, to learn intra- and inter-epoch temporal contexts from a raw single channel electroencephalogram (EEG) for automatic sleep stage scoring. When sleep experts identify the sleep stage of a 30-second PSG data called an epoch, they investigate the sleep-related events such as sleep spindles, K-complex, and frequency components from local segments of an epoch (sub-epoch) and consider the relations between sleep-related events of successive epochs to follow the transition rules. Inspired by this, IITNet learns how to encode sub-epoch into representative feature via a deep residual network, then captures contextual information in the sequence of representative features via BiLSTM. Thus, IITNet can extract features in sub-epoch level and consider temporal context not only between epochs but also in an epoch. IITNet is an end-to-end architecture and does not need any preprocessing, handcrafted feature design, balanced sampling, pre-training, or fine-tuning. Our model was trained and evaluated in Sleep-EDF and MASS datasets and outperformed other state-of-the-art results on both the datasets with the overall accuracy (ACC) of 84.0% and 86.6%, macro F1-score (MF1) of 77.7 and 80.8, and Cohen's kappa of 0.78 and 0.80 in Sleep-EDF and MASS, respectively.
A Survey on Android Malware Detection Techniques

Now-a-days widely applicable smart phone application adoption and rapid growth of contextually sensitive nature of smartphone devices has led to a renaissance in mobile application services and increased concerns over smartphone malware. Most of the smart phone users are connected to internet for various purposes. Even after the advanced technology used in the smartphones still the users are remaining unprotected from malware attacks. To overcome these issues and to keep the smartphones safe a number of malicious applications, malware threat and adwares for mobile phones is expected to increase with the functionality enhancement of smart mobile phone. Android Operating Systems are most commonly used systems in the smartphones. Many applications are available in android play store and it is very difficult to distinguish and to discriminate between clean and malicious applications. We have briefly discussed different methods for detecting android malwares in this review and present a clear picture about these detection tools in the smartphone applications.