Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Representation Learning: A Quantitative Review

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems. Finally, we discuss the remaining challenges and outlook the future directions for KRL. The codes and datasets used in the experiments can be found in this https URL.
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

Deep Neural Networks (DNNs) have been widely applied in autonomous systems such as self-driving vehicles. Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations. While existing testing techniques prove to be effective, particularly for autonomous driving, they mostly focus on generating digital adversarial perturbations, e.g., changing image pixels, which may never happen in the physical world. Thus, there is a critical missing piece in the literature on autonomous driving testing: understanding and exploiting both digital and physical adversarial perturbation generation for impacting steering decisions. In this paper, we propose a systematic physical-world testing approach, namely DeepBillboard, targeting at a quite common and practical driving scenario: drive-by billboards. DeepBillboard is capable of generating a robust and resilient printable adversarial billboard test, which works under dynamic changing driving conditions including viewing angle, distance, and lighting. The objective is to maximize the possibility, degree, and duration of the steering-angle errors of an autonomous vehicle driving by our generated adversarial billboard. We have extensively evaluated the efficacy and robustness of DeepBillboard by conducting both experiments with digital perturbations and physical-world case studies. The digital experimental results show that DeepBillboard is effective for various steering models and scenes. Furthermore, the physical case studies demonstrate that DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions, being able to mislead the average steering angle error up to 26.44 degrees. To the best of our knowledge, this is the first study demonstrating the possibility of generating realistic and continuous physical-world tests for practical autonomous driving systems; moreover, DeepBillboard can be directly generalized to a variety of other physical entities/surfaces along the curbside, e.g., a graffiti painted on a wall.
Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). It consists of an adaptive sampling mask generation network which is jointly trained with an image inpainting network. The sampling rate is controlled by the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. In addition to the image sampling and reconstruction process, we show how it can be extended and used to speed up raster scanning such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved into lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposure requirements (e.g., ${\text{100}}\; \mu{\text{s}}-{\text{1 ms}}$ per point). We propose an XRF image inpainting approach to address the long scanning times, thus speeding up the scanning process, while being able to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of existing methods.
SMPLR: Deep SMPL reverse for 3D human pose and shape recovery

Current state-of-the-art in 3D human pose and shape recovery relies on deep neural networks and statistical morphable body models, such as the Skinned Multi-Person Linear model (SMPL). However, regardless of the advantages of having both body pose and shape, SMPL-based solutions have shown difficulties to predict 3D bodies accurately. This is mainly due to the unconstrained nature of SMPL, which may generate unrealistic body meshes. Because of this, regression of SMPL parameters is a difficult task, often addressed with complex regularization terms. In this paper we propose to embed SMPL within a deep model to accurately estimate 3D pose and shape from a still RGB image. We use CNN-based 3D joint predictions as an intermediate representation to regress SMPL pose and shape parameters. Later, 3D joints are reconstructed again in the SMPL output. This module can be seen as an autoencoder where the encoder is a deep neural network and the decoder is SMPL model. We refer to this as SMPL reverse (SMPLR). By implementing SMPLR as an encoder-decoder we avoid the need of complex constraints on pose and shape. Furthermore, given that in-the-wild datasets usually lack accurate 3D annotations, it is desirable to lift 2D joints to 3D without pairing 3D annotations with RGB images. Therefore, we also propose a denoising autoencoder (DAE) module between CNN and SMPLR, able to lift 2D joints to 3D and partially recover from structured error. We evaluate our method on SURREAL and Human3.6M datasets, showing improvement over SMPL-based state-of-the-art alternatives by about 4 and 25 millimeters, respectively.
Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community. Code coverage is one of the most common metrics for evaluating effectiveness of these frameworks. Furthermore, code coverage is used as a fitness function for guiding evolutionary and fuzzy testing techniques. However, there are no reliable tools for measuring fine-grained code coverage in black-box Android app testing. We present the Android Code coVerage Tool, ACVTool for short, that instruments Android apps and measures code coverage in the black-box setting at class, method and instruction granularity. ACVTool has successfully instrumented 96.9% of apps in our experiments. It introduces a negligible instrumentation time overhead, and its runtime overhead is acceptable for automated testing tools. We demonstrate practical value of ACVTool in a large-scale experiment with Sapienz, a state-of-the-art automated testing tool. Using ACVTool on the same cohort of apps, we have compared different coverage granularities applied by Sapienz in terms of the found amount of crashes. Our results show that none of the applied coverage granularities clearly outperforms others in this aspect.
Topological Constraints on Homeomorphic Auto-Encoding

When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic when restricted to the manifold, so that it is bijective and continuous with a continuous inverse. Using topological arguments, we show that when the manifold is non-trivial, the encoder must be globally discontinuous and propose a universal, albeit impractical, construction. In addition, we derive necessary constraints which need to be satisfied when designing manifold-specific practical encoders. These are used to analyse candidates for a homeomorphic encoder for the manifold of 3D rotations $SO(3)$.
An Achievable Rate Region for Two Groupcast Messages over the K-User Broadcast Channel and Capacity Results for the Combination Network

Using an order-theoretic framework, a novel achievable rate region is obtained for the general $K$-receiver discrete memoryless broadcast channel over which two groupcast messages are to be transmitted, with each message required by an arbitrary group of receivers. The associated achievability scheme is an amalgamation of random coding techniques with novel features including {\em up-set} message-splitting, message set expansion including the generation of possibly multiple auxiliary codebooks for certain compositions of split messages using superposition coding with subset inclusion order, partial interference decoding at all receivers in general, joint unique decoding at receivers that desire both messages, and non-unique or indirect decoding at receivers that desire only one of the two messages. While the generality of such a scheme implies that its rate region coincides with all previously found capacity regions for special classes of broadcast channels with two private or two {\em nested} groupcast messages, wherein the group of receivers desiring one message is contained in that desiring the other, we show that, when specialized to the so-called {\em combination network}, our inner bound coincides with the capacity region for three different scenarios, namely, (a) the two messages are intended for two distinct sets of $K{-}1$ receivers each and (b) two nested messages in which one message is intended for one or (c) two (common) receivers and both messages are intended for all other (private) receivers. Moreover, we show that there is a trade-off between the complexity of the coding scheme and that of the distribution of the auxiliary random variables and the encoding function that must be chosen to achieve the capacity region in these scenarios.
Comparison of the National Early Warning Score+Lactate score with the pre-endoscopic Rockall, Glasgow-Blatchford, and AIMS65 scores in patients with upper gastrointestinal bleeding

Objective We compared the predictive value of the National Early Warning Score+Lactate (NEWS+L) score with those of other parameters such as the pre-endoscopic Rockall score (PERS), Glasgow-Blatchford score (GBS), and albumin, international normalized ratio, altered mental status, systolic blood pressure, age older than 65 years score (AIMS65) among patients with upper gastrointestinal bleeding (UGIB). Methods We conducted a retrospective study of patients with UGIB during 2 consecutive years. The primary outcome was the composite of in-hospital death, intensive care unit admission, and the need for ≥5 packs of red blood cell transfusion within 24 hours. Results Among 530 included patients, the composite outcome occurred in 59 patients (19 in-hospital deaths, 13 intensive care unit admissions, and 40 transfusions of ≥5 packs of red blood cells within 24 hours). The area under the receiver operating characteristic curve of the NEWS+L score for the composite outcome was 0.76 (95% confidence interval, 0.70 to 0.82), which demonstrated a significant difference compared to PERS (0.66, 0.59–0.73, P=0.004), but not to GBS (0.70, 0.64–0.77, P=0.141) and AIMS65 (0.76, 0.70–0.83, P=0.999). The sensitivities of NEWS+L scores of 3 (n=34, 6.4%), 4 (n=92, 17.4%), and 5 (n=171, 32.3%) were 100%, 98.3%, and 96.6%, respectively, while the sensitivity of an AIMS65 score of 0 (n=159, 30.0%) was 91.5%. Conclusion The NEWS+L score showed better discriminative performance than the PERS and comparable discriminative performance to the GBS and AIMS65. The NEWS+L score may be used to identify low-risk patients among patients with UGIB.
Evidence Linking Brain Activity Modulation to Age and to Deductive Training

Electrical brain activity modulation in terms of changes in its intensity and spatial distribution is a function of age and task demand. However, the dynamics of brain modulation is unknown when it depends on external factors such as training. The aim of this research is to verify the effect of deductive reasoning training on the modulation in the brain activity of healthy younger and older adults (N = 47 (mean age of 21 ± 3.39) and N = 38 (mean age of 68.92 ± 5.72)). The analysis reveals the benefits of training, showing that it lowers cerebral activation while increasing the number of correct responses in the trained reasoning task (p < 0.001). The brain source generators were identified by time-averaging low-resolution brain electromagnetic tomography (sLORETA) current density images. In both groups, a bilateral overactivation associated with the task and not with age was identified. However, while the profile of bilateral activation in younger adults was symmetrical in anterior areas, in the older ones, the profile was located asymmetrically in anterior and posterior areas. Consequently, bilaterality may be a marker of how the brain adapts to maintain cognitive function in demanding tasks in both age groups. However, the differential bilateral locations across age groups indicate that the tendency to brain modulation is determined by age.
End-stage Renal Disease and Risk of Active Tuberculosis: a Nationwide Population-Based Cohort Study

Background The converging epidemics of tuberculosis (TB) and end-stage renal disease (ESRD) have generated a significant public health burden, however, previous studies have been limited to a small number of patients. This nationwide cohort study aimed to assess the rate of developing active TB among patients receiving dialysis for ESRD. Methods The Korean national health insurance database was used to identify patients receiving dialysis for new-onset ESRD during 2004–2013, who were propensity score matched to an equivalent number of non-dialysis subjects from the general population. The incidences of active TB in the ESRD and control cohorts were calculated for 2004–2013, and multivariable Cox proportional hazards model was used to evaluate the ESRD-related risk of active TB. Results During 2004–2013, 59,584 patients received dialysis for newly diagnosed ESRD. In the dialysis and control cohorts, 457 (0.8%) and 125 (0.2%) cases of active TB were detected, respectively. Patients with ESRD were associated with a significantly higher risk of active TB compared to the controls (incidence rate ratio, 4.80). The ESRD cohort had an independently elevated risk of active TB (adjusted hazard ratio, 4.39; 95% confidence interval, 3.60–5.37). Conclusion We found that patients receiving dialysis for ESRD had an elevated risk of active TB. These results highlight the need for detailed and well-organised guidelines for active TB screening among patients with ESRD.
Semantics, Logic, and Verification of "Exact Real Computation"

This work formalizes "Exact Real Computation" (ERC): a paradigm combining (i) ALGEBRAIC imperative programming of/over abstract data types (ADTs) for CONTINUOUS structures with (ii) a selection and sound semantics of primitives computable in the sense of Recursive ANALYSIS, that is, by means of approximations -- yet presented to the user as exact. We specify a small imperative programming language for the ADT of real (i.e., including transcendental) numbers with rigorous semantics: arguments are provided, passed to and received from calls to functions (like $e^x$), and operated on EXACTLY -- with partial inequality predicate and multivalued binary SELECT and continuous conditional (aka PARALLEL-IF) operations -- yet REALIZING a function (again like $e^x$) requires only to APPROXIMATE its return value up to guaranteed absolute error $2^p$ for any given integer $p$: closure under composition is implicit. We prove this language Turing-COMPLETE: it can express precisely those partial real functions computable in the sense of Recursive Analysis; similarly for functionALs. Three basic numerical problems demonstrate both the convenience and novel control-flow considerations of this approach to Reliable Numerics: multivalued integer rounding, solving systems of linear equations, and simple root finding. For rigorously specifying and arguing about such (non-extensional) computations, we propose a first-order theory over two sorts: integers and reals; and prove it both decidable and 'model complete': thus reflecting the elegance inherent to real (as opposed to rational/floating point) numbers. Rules of Hoare Logic are extended to support formal correctness proofs in ERC.
Calibration of Heterogeneous Sensor Systems

Environment perception is a key component of any autonomous system and is often based on a heterogeneous set of sensors and fusion thereof for which sensor sensor calibration plays fundamental role. It can be divided to intrinsic and extrinsic sensor calibration. Former seeks for internal parameters of each individual sensor, while latter provides coordinate frame transformation between sensors. Calibration techniques require correspondence registration in the measurements which is one of the main challenges in the extrinsic calibration of heterogeneous sensors, since generally, each sensor can operate on a different physical principle. Measurement correspondences can originate from a designated calibration target or from features in the environment. Additionally, environment features can be used to estimate motion of individual sensors and the calibration is found by aligning these estimates. Motion-based calibration is the most common approach in the online calibration since it is more practical than the target-based methods, although it can lack in accuracy. Furthermore, online calibration is beneficial for system robustness as it can detect and adjust recalibration of the system in runtime, which can be seen as a prerequisite for long-term autonomy.
Two "correlation games" for a nonlinear network with Hebbian excitatory neurons and anti-Hebbian inhibitory neurons

A companion paper introduces a nonlinear network with Hebbian excitatory (E) neurons that are reciprocally coupled with anti-Hebbian inhibitory (I) neurons and also receive Hebbian feedforward excitation from sensory (S) afferents. The present paper derives the network from two normative principles that are mathematically equivalent but conceptually different. The first principle formulates unsupervised learning as a constrained optimization problem: maximization of S-E correlations subject to a copositivity constraint on E-E correlations. A combination of Legendre and Lagrangian duality yields a zero-sum continuous game between excitatory and inhibitory connections that is solved by the neural network. The second principle defines a zero-sum game between E and I cells. E cells want to maximize S-E correlations and minimize E-I correlations, while I cells want to maximize I-E correlations and minimize power. The conflict between I and E objectives effectively forces the E cells to decorrelate from each other, although only incompletely. Legendre duality yields the neural network.
Rate-Distortion-Perception Tradeoff of Variable-Length Source Coding for General Information Sources

Blau and Michaeli recently introduced a novel concept for inverse problems of signal processing, that is, the perception-distortion tradeoff. We introduce their tradeoff into the rate distortion theory of variable-length lossy source coding in information theory, and clarify the tradeoff among information rate, distortion and perception for general information sources. We also discuss the fixed-length coding with average distortion criterion that was missing in the previous letter.
CoSpace: Common Subspace Learning From Hyperspectral-Multispectral Correspondences

With a large amount of open satellite multispectral (MS) imagery (e.g., Sentinel-2 and Landsat-8), considerable attention has been paid to global MS land cover classification. However, its limited spectral information hinders further improving the classification performance. Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to MS ones. To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification. By locally aligning the manifold structure of the two modalities, CoSpace linearly learns a shared latent subspace from hyperspectral-MS (HS-MS) correspondences. The MS out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and thus leads to a better classification. Extensive experiments on two simulated HS-MS data sets (University of Houston and Chikusei), where HS-MS data sets have tradeoffs between coverage and spectral resolution, are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games

Weighted voting games are a family of cooperative games, typically used to model voting situations where a number of agents (players) vote against or for a proposal. In such games, a proposal is accepted if an appropriately weighted sum of the votes exceeds a prespecified threshold. As the influence of a player over the voting outcome is not in general proportional to her assigned weight, various power indices have been proposed to measure each player’s influence. The inverse power index problem is the problem of designing a weighted voting game that achieves a set of target influences according to a predefined power index. In this work, we study the computational complexity of the inverse problem when the power index belongs to the class of semivalues. We prove that the inverse problem is computationally intractable for a broad family of semivalues, including all regular semivalues. As a special case of our general result, we establish computational hardness of the inverse problem for the Banzhaf indices and the Shapley values, arguably the most popular power indices.
EMMA: An Emotionally Intelligent Personal Assistant for Improving Wellbeing

The delivery of mental health interventions via ubiquitous devices has shown much promise. A natural conversational interface that allows longitudinal symptom tracking and appropriate just-in-time interventions would be extremely valuable. However, the task of designing emotionally-aware agents is still poorly understood. Furthermore, the feasibility of automating the delivery of just-in-time mHealth interventions via such an agent has not been fully studied. In this paper, we present the design and evaluation of EMMA (EMotion-Aware mHealth Agent) through two human-subject experiments with N=39 participants (one-week, and two-week long respectively). EMMA conducts experience sampling in an empathetic manner and provides emotionally appropriate micro-activities. We show the system can be extended to detect a user's mood purely from smartphone sensor data. Our results show that extraverts preferred EMMA significantly more than introverts and that our personalized machine learning model worked as well as relying on gold-standard self-reports of emotion from users. Finally, we provide a set of guidelines for the design of bots for mHealth.