Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Hidden Populations through Attributed Search

Researchers often query online social platforms through their application programming interfaces (API) to find target populations such as people with mental illness~\cite{De-Choudhury2017} and jazz musicians~\cite{heckathorn2001finding}. Entities of such target population satisfy a property that is typically identified using an oracle (human or a pre-trained classifier). When the property of the target entities is not directly queryable via the API, we refer to the property as `hidden' and the population as a hidden population. Finding individuals who belong to these populations on social networks is hard because they are non-queryable, and the sampler has to explore from a combinatorial query space within a finite budget limit. By exploiting the correlation between queryable attributes and the population of interest and by hierarchically ordering the query space, we propose a Decision tree-based Thompson sampler (\texttt{DT-TMP}) that efficiently discovers the right combination of attributes to query. Our proposed sampler outperforms the state-of-the-art samplers in online experiments, for example by 54\% on Twitter. When the number of matching entities to a query is known in offline experiments, \texttt{DT-TMP} performs exceedingly well by a factor of 0.9-1.5$\times$ over the baseline samplers. In the future, we wish to explore the option of finding hidden populations by formulating more complex queries.
Non-Monotonic Synchronization Transitions and Quasi-Chimera States in Time-Delayed Oscillator Communities

We study the transitions between macroscopic states in coupled oscillator systems with community structure and time delays. We show that the combination of these two properties gives rise to non-monotonic transitions, whereby increasing the global coupling strength can both inhibit and promote synchronization, yielding both desynchronization and synchronization transitions. For asymmetric parameter choices we also observe quasi-chimera states where one community remains effectively incoherent while the other is synchronized. Using the ansatz of Ott and Antonsen we provide analytical descriptions for these transitions that confirm numerical simulations.
A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark

Face parsing, which is to assign a semantic label to each pixel in face images, has recently attracted increasing interest due to its huge application potentials. Although many face related fields (e.g., face recognition and face detection) have been well studied for many years, the existing datasets for face parsing are still severely limited in terms of the scale and quality, e.g., the widely used Helen dataset only contains 2,330 images. This is mainly because pixel-level annotation is a high cost and time-consuming work, especially for the facial parts without clear boundaries. The lack of accurate annotated datasets becomes a major obstacle in the progress of face parsing task. It is a feasible way to utilize dense facial landmarks to guide the parsing annotation. However, annotating dense landmarks on human face encounters the same issues as the parsing annotation. To overcome the above problems, in this paper, we develop a high-efficiency framework for face parsing annotation, which considerably simplifies and speeds up the parsing annotation by two consecutive modules. Benefit from the proposed framework, we construct a new Dense Landmark Guided Face Parsing (LaPa) benchmark. It consists of 22,000 face images with large variations in expression, pose, occlusion, etc. Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks. To the best of our knowledge, it is currently the largest public dataset for face parsing. To make full use of our LaPa dataset with abundant face shape and boundary priors, we propose a simple yet effective Boundary-Sensitive Parsing Network (BSPNet). Our network is taken as a baseline model on the proposed LaPa dataset, and meanwhile, it achieves the state-of-the-art performance on the Helen dataset without resorting to extra face alignment.
Integrating Objects into Monocular SLAM: Line Based Category Specific Models

We propose a novel Line based parameterization for category specific CAD models. The proposed parameterization associates 3D category-specific CAD model and object under consideration using a dictionary based RANSAC method that uses object Viewpoints as prior and edges detected in the respective intensity image of the scene. The association problem is posed as a classical Geometry problem rather than being dataset driven, thus saving the time and labour that one invests in annotating dataset to train Keypoint Network[25, 26] for different category objects. Besides eliminating the need of dataset preparation, the approach also speeds up the entire process as this method processes the image only once for all objects, thus eliminating the need of invoking the network for every object in an image across all images. A 3D-2D edge association module followed by a resection algorithm for lines is used to recover object poses. The formulation optimizes for shape and pose of the object, thus aiding in recovering object 3D structure more accurately. Finally, a Factor Graph formulation is used to combine object poses with camera odometry to formulate a SLAM problem.
Dynamical Masses of T Tauri Stars and Calibration of Pre-Main-Sequence Evolution

We have used the high sensitivity and resolution of the IRAM interferometer to produce subarcsecond 12CO J = 2-1 images of nine protoplanetary disks surrounding T Tauri stars in the Taurus-Auriga cloud (seven singles and two binaries). The images demonstrate the disks are in Keplerian rotation around their central stars. Using the least-square fit method described in the 1998 work by Guilloteau & Dutrey, we derive the disk's properties, in particular its inclination angle and rotation velocity, hence the dynamical mass. Since the disk mass is usually small, this is a direct measurement of the stellar mass. Typically, we reach an internal precision of 10% in the determinations of stellar mass. The overall accuracy is limited by the uncertainty in the distance to a specific star. In a distance-independent way, we compare the derived masses with theoretical tracks of pre-main-sequence evolution. Combined with the mean distance to the Taurus region (140 pc), for stars with mass close to 1 M☉, our results tend to favor the tracks with cooler photospheres (higher masses for a given spectral type). We find that in UZ Tau E, the disk and the spectroscopic binary orbit appear to have different inclinations.
Secure Hybrid Digital and Analog Precoder for mmWave Systems With Low-Resolution DACs and Finite-Quantized Phase Shifters

Millimeter wave (mmWave) communication has been regarded as one of the most promising technologies for the future generation wireless networks because of its advantages of providing a ultra-wide new spectrum and ultra-high data transmission rate. To reduce the power consumption and circuit cost for mmWave systems, hybrid digital and analog (HDA) architecture is preferred in such a scenario. In this paper, an artificial noise (AN) aided secure HDA beamforming scheme is proposed for mmWave multiple input single output (MISO) system with low resolution digital-to-analog converters (DACs) and finite-quantized phase shifters on radio frequency. The additive quantization noise model for AN aided HDA system is established to make an analysis of the secrecy performance of such systems. With the partial channel knowledge of eavesdropper available, an approximate expression of secrecy rate (SR) is derived. Then using this approximation formula, we propose a two-layer alternatively iterative structure (TLAIS) for optimizing digital precoder (DP) of confidential message (CM), digital AN projection matrix (DANPM) and analog precoder (AP). The inner-layer iteration loop is to design the DP of CMs and DANPM alternatively given a fixed matrix of AP. The outer-layer iteration loop is between digital baseband part and analog part, where the former refers to DP and DANPM, and the latter is AP. Then for a given digital part, we propose a gradient ascent algorithm to find the AP vector. Given a matrix of AP, we make use of general power iteration (GPI) method to compute DP and DANPM. This process is repeated until the terminal condition is reached. Simulation results show that the proposed TLAIS can achieve a better SR performance than existing methods, especially in the high signal-to-noise ratio region.
IMPROVING THE PROTECTION OF FPGA BASED SEQUENTIAL IP CORE DESIGNS USING HIERARCHICAL WATERMARKING TECHNIQUE

In recent years, Intellectual Property (IP) cores in Very Large Scale Integration (VLSI) have become an active research area as it provides a new-fangled revolution in the Electronic Design Automation industry. An IP core is a previously designed and demonstrated component that can be integrated into design. Owing to the development of IP cores, time consumption becomes less and the product can be arrived in specified time. Designer of VLSI IP cores needs assurance that the design will not be illegally redistributed by consumers. IP core vendors are facing a major challenge to avoid revenue loss due to IP piracy. Watermarking is a well-known technique to protect an unauthorized use of IP core. Finite State Machine (FSM) is one of the representations of sequential digital designs. In this paper, a new dynamic hierarchical watermarking scheme is proposed. The watermark is embedded in the state transitions of FSM at the behavioural level. A watermark is embedded into FSM by hierarchically splitting original FSM into smaller FSMs. Experimental results on benchmark circuits shows that this hierarchical watermarking approach is an efficient method for protecting sequential IP cores.
Key Determinants of Performance Assessment for U.S. TPL: DEA and Cluster Analysis

Based on empirical data of twenty third-party logistic providers in America, this study adopts DEA and cluster analysis to evaluate the key factors in performance assessment. It was found that returns of scale of DMUs' status influenced efficiency ranking. The A&P model is shown useful in distinguishing efficient DMUs, while for inefficient DMUs the cross efficiency model is excellent. Empirical analysis in classification shows consistent results for factors directly correlated with logistics in both DEA and cluster analysis. While past research emphasized the importance of financial performance, this paper argues that it should take another perspective in performance assessment. This study indicates that important factors generally used in financial studies do not exhibit significant influence as expected, demonstrating a critical approach is to achieve operations efficiency rather than financial efficiency. Finally, this paper makes some recommendations to improve the operations' efficiency for logistics firms
Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization

Knowledge graphs capture interlinked information between entities and they represent an attractive source of structured information that can be harnessed for recommender systems. However, existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training, or provide poor scalability. Here we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end trainable framework that harnesses item relationships captured by the knowledge graph to provide better recommendations. Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph. Then, KGCN applies a graph convolutional neural network that computes an embedding of an item node by propagating and aggregating knowledge graph neighborhood information. Moreover, to provide better inductive bias KGCN uses label smoothness (LS), which provides regularization over edge weights and we prove that it is equivalent to label propagation scheme on a graph. Finally, We unify KGCN and LS regularization, and present a scalable minibatch implementation for KGCN-LS model. Experiments show that KGCN-LS outperforms strong baselines in four datasets. KGCN-LS also achieves great performance in sparse scenarios and is highly scalable with respect to the knowledge graph size.
Controlled Natural Languages and Default Reasoning

Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural languages. In this report, we focus on a class of CNLs, called machine-oriented CNLs, which have well-defined semantics that can be deterministically translated into formal languages, such as Prolog, to do logical reasoning. Over the past 20 years, a number of machine-oriented CNLs emerged and have been used in many application domains for problem solving and question answering. However, few of them support non-monotonic inference. In our work, we propose non-monotonic extensions of CNL to support defeasible reasoning. In the first part of this report, we survey CNLs and compare three influential systems: Attempto Controlled English (ACE), Processable English (PENG), and Computer-processable English (CPL). We compare their language design, semantic interpretations, and reasoning services. In the second part of this report, we first identify typical non-monotonicity in natural languages, such as defaults, exceptions and conversational implicatures. Then, we propose their representation in CNL and the corresponding formalizations in a form of defeasible reasoning known as Logic Programming with Defaults and Argumentation Theory (LPDA).
Cooperation of Cars and Formation of Cooperative Groups

Cooperation of cars capable to communicate bears a high potential with respect to safety in critical situations. Following a top-down design, cars form cooperative groups, exchange information available to them and establish a common relevant picture upon which critical situations are detected, optimal decisions for the groups are derived and are distributed in form of individual action sequences. This paper focuses on the formation of cooperative groups. To this end, a graph-based spatiotemporal distance measure is developed, using the concept of virtual meeting points within the road infrastructure. The distance measure is analyzed and it serves to define cooperative groups of cognitive automobiles.
A Survey of Controller Placement Problem in Software Defined Networks

Software Defined Network (SDN) is an emerging network paradigm which provides a centralized view of the network by decoupling the network control plane from the data plane. This strategy of maintaining a global view of the network optimizes resource management. However, the implementation of SDN using a single physical controller lead to issues of scalability and robustness. A physically distributed but logically centralized SDN controller architecture promises to resolve both these issues. Distributed SDN along with its benefits brings along the problem of the number of controllers required and their placement in the network. This problem is referred to as the controller placement problem (CPP) and this paper is mainly concerned with the CPP solution techniques. The paper formally defines CPP, gives a comprehensive review of the various performance metrics and characteristics of the available CPP solutions. Finally, we point out the existing literature gap and discuss the future research direction in this domain.
Comparing Alternate Modes of Teleoperation for Constrained Tasks

Teleoperation of heavy machinery in industry often requires operators to be in close proximity to the plant and issue commands on a per-actuator level using multiple joystick input devices. However, this is non-intuitive and makes achieving desired job properties a challenging task requiring operators to complete costly and extensive training. Despite this, operator fatigue is common with implications for personal safety, project timeliness, cost, and job quality. While full automation is not yet achievable due to unpredictability and the dynamic nature of the environment and task, shared control paradigms allow operators to issue high-level commands in an intuitive, task-informed control space while having the robot optimize for achieving desired job properties.In this paper, we compare a number of modes of teleoperation, exploring both the number of dimensions of the control input as well as the most intuitive control spaces. Our experimental evaluations of the performance metrics were based on quantifying the difficulty of tasks based on the well known Fitts’ law as well as a measure of how well constraints affecting the task performance were met. Our experiments show that higher performance is achieved when humans submit commands in low-dimensional task spaces as opposed to joint space manipulations.
Weyl connections and their role in holography

It is a well-known property of holographic theories that diffeomorphism invariance in the bulk space-time implies Weyl invariance of the dual holographic field theory in the sense that the field theory couples to a conformal class of background metrics. The usual Fefferman-Graham formalism, which provides us with a holographic dictionary between the two theories, breaks explicitly this symmetry by choosing a specific boundary metric and a corresponding specific metric ansatz in the bulk. In this paper, we show that a simple extension of the Fefferman-Graham formalism allows us to sidestep this explicit breaking; one finds that the geometry of the boundary includes an induced metric and an induced connection on the tangent bundle of the boundary that is a Weyl connection (rather than the more familiar Levi-Civita connection uniquely determined by the induced metric). Properly invoking this boundary geometry has far-reaching consequences: the holographic dictionary extends and naturally encodes Weyl-covariant geometrical data, and, most importantly, the Weyl anomaly gains a clearer geometrical interpretation, cohomologically relating two Weyl-transformed volumes. The boundary theory is enhanced due to the presence of the Weyl current, which participates with the stress tensor in the boundary Ward identity.
Variational inference for neural network matrix factorization and its application to stochastic blockmodeling

We consider the probabilistic analogue to neural network matrix factorization (Dziugaite & Roy, 2015), which we construct with Bayesian neural networks and fit with variational inference. We find that a linear model fit with variational inference can attain equivalent predictive performance to the regular neural network variants on the Movielens data sets. We discuss the implications of this result, which include some suggestions on the pros and cons of using the neural network construction, as well as the variational approach to inference. Such a probabilistic approach is required, however, when considering the important class of stochastic block models. We describe a variational inference algorithm for a neural network matrix factorization model with nonparametric block structure and evaluate its performance on the NIPS co-authorship data set.
Conceptual Abilities as the Basis of Individual Intellectual Resource

This article presents the results of the study of correlations and interactions between conceptual, intellectual and creative abilities and the manifestation of individual intellectual resource. The individual intellectual resource is treated as the individual identity of person’s mental experience that determines the effectiveness of his or her intellectual activity. Assuming that the expression of individual intellectual resource is the result of actualization of the conceptual abilities (abilities to generate a new mental experience), creativity, psychometric intelligence, we carried out two series of empirical research with school and student participants (n = 230). It is shown that the conceptual (generative) creative (non-verbal) abilities and psychometric intelligence make a system formation describing the state of individual intellectual resource. The results indicate that the level of conceptual abilities is the basis of individual intellectual resources, expressed in terms of real intellectual achievements.
A General Framework for Hedging and Speculating with Options

In contrast to their role in theory, options are in practice not only traded for hedging purposes. Many investors also use them for speculation purposes. For these investors the Black–Scholes price serves only as an orientation, their decisions to buy, hold or hedge an option are also based on subjective beliefs and on their personal utility functions (in the widest possible sense). The aim of this paper is to present a general framework to include different types of investors, especially hedgers, pure speculators and speculators following strategies with bounded risk. We derive their subjective values of an option endogenously from the solution of their decision problems.
Turbulent pressure support and hydrostatic mass bias in the intracluster medium

The degree of turbulent pressure support by residual gas motions in galaxy clusters is not well known. Mass modelling of combined X-ray and Sunyaev–Zel’dovich observations provides an estimate of turbulent pressure support in the outer regions of several galaxy clusters. Here, we test two different filtering techniques to disentangle bulk from turbulent motions in non-radiative high-resolution cosmological simulations of galaxy clusters using the cosmological hydrocode enzo. We find that the radial behaviour of the ratio of non-thermal pressure to total gas pressure as a function of cluster-centric distance can be described by a simple polynomial function. The typical non-thermal pressure support in the centre of clusters is ∼5 per cent, increasing to ∼15 per cent in the outskirts, in line with the pressure excess found in recent X-ray observations. While the complex dynamics of the intracluster medium makes it impossible to reconstruct a simple correlation between turbulent motions and hydrostatic bias, we find that a relation between them can be established using the median properties of a sample of objects. Moreover, we estimate the contribution of radial accelerations to the non-thermal pressure support and conclude that it decreases moving outwards from 40 per cent (in the core) to 15 per cent (in the cluster’s outskirts). Adding this contribution to one provided by turbulence, we show that it might account for the entire observed hydrostatic bias in the innermost regions of the clusters, and for less than 80 per cent of it at r > 0.8 r200,m.