Miracle-dz/UniFAR-FTUs huggingface dataset:
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which it is played. Silver et al. developed a program called AlphaZero, which taught itself to play Go, chess, and shogi (a Japanese version of chess) (see the Editorial, and the Perspective by Campbell). AlphaZero managed to beat state-of-the-art programs specializing in these three games. The ability of AlphaZero to adapt to various game rules is a notable step toward achieving a general game-playing system. Science, this issue p. 1140; see also pp. 1087 and 1118 AlphaZero teaches itself to play three different board games and beats state-of-the-art programs in each. The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess), as well as Go.
VRHapticDrones: Providing Haptics in Virtual Reality through Quadcopters

We present VRHapticDrones, a system utilizing quadcopters as levitating haptic feedback proxy. A touchable surface is attached to the side of the quadcopters to provide unintrusive, flexible, and programmable haptic feedback in virtual reality. Since the users' sense of presence in virtual reality is a crucial factor for the overall user experience, our system simulates haptic feedback of virtual objects. Quadcopters are dynamically positioned to provide haptic feedback relative to the physical interaction space of the user. In a first user study, we demonstrate that haptic feedback provided by VRHapticDrones significantly increases users' sense of presence compared to vibrotactile controllers and interactions without additional haptic feedback. In a second user study, we explored the quality of induced feedback regarding the expected feeling of different objects. Results show that VRHapticDrones is best suited to simulate objects that are expected to feel either light-weight or have yielding surfaces. With VRHapticDrones we contribute a solution to provide unintrusive and flexible feedback as well as insights for future VR haptic feedback systems.
Communicating Shoulder Surfing Attacks to Users

Since mobile interaction takes place in almost every context, shoulder surfing attacks are becoming more and more a threat to user's privacy. While several approaches exist to prevent these attacks for the authentication process, protecting the actual interaction has not yet been in the main focus of research. In this work, we present the concept of communicating shoulder surfing attacks to the user. This should create awareness on the user side and help preventing this type of privacy invasion. We present out shoulder surfer detection mobile application, called DSSytem, and report on a focus group that helped to design this system. We also report on the results of a user study in which we compare four different notification methods, namely, vibro-tactile, front LED, on-screen icon, and video preview feedback. Vibro-tactile feedback results in the lowest reaction time of the participants and is also favoured throughout the follow-up semi-structured interviews.
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape

We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval. We propose a novel loss formulation by lifting 2D detection, orientation, and scale estimation into 3D space. Instead of optimizing these quantities separately, the 3D instantiation allows to properly measure the metric misalignment of boxes. We experimentally show that our 10D lifting of sparse 2D Regions of Interests (RoIs) achieves great results both for 6D pose and recovery of the textured metric geometry of instances. This further enables 3D synthetic data augmentation via inpainting recovered meshes directly onto the 2D scenes. We evaluate on KITTI3D against other strong monocular methods and demonstrate that our approach doubles the AP on the 3D pose metrics on the official test set, defining the new state of the art.
Epileptic Seizure Detection Based on EEG Signals and CNN

Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.
Comparison of fMRI Digit Representations of the Dominant and Non-dominant Hand in the Human Primary Somatosensory Cortex

The tactile digit representations in the primary somatosensory cortex have so far been mapped for either the left or the right hand. This study localized all ten digit representations in right-handed subjects and compared them within and across the left and right hands to assess potential differences in the functional organization of the digit map between hands and in the structural organization between hemispheres. Functional magnetic resonance imaging of tactile stimulation of each fingertip in BA 3b confirmed the expected lateral-anterior-inferior to medial-posterior-superior succession from thumb to little-finger representation, located in the post-central gyrus opposite to the motor hand knob. While the more functionally related measures, such as the extent and strength of activation as well as the Euclidean distance between neighboring digit representations, showed significant differences between the digits, no side difference was detected: the layout of the functional digit-representation map did not consistently differ between the left, non-dominant, and the right, dominant hand. Comparing the absolute spatial coordinates also revealed a significant difference for the digits, but not between the left and right hand digits. Estimating the individual subject's digit coordinates of one hand by within-subject mirroring of the other-hand digit coordinates across hemispheres yielded a larger estimation error distance than using averaged across-subjects coordinates from within the same hemisphere. However, both methods should only be used with care for single-subject clinical evaluation, as an average estimation error of around 9 mm was observed, being slightly higher than the average distance between neighboring digits.
Hapi: A Robust Pseudo-3D Calibration-Free WiFi-based Indoor Localization System

In this paper, we present Hapi, a novel system that uses off-the-shelf standard WiFi to provide pseudo-3D indoor localization---it estimates the user's floor and her 2D location on that floor. Hapi is calibration-free, only requiring the building's floorplans and its WiFi APs' installation location for deployment. Our analysis shows that while a user can hear APs from nearby floors as well as her floor, she will typically only receive signals from spatially closer APs in distant floors, as compared to APs in her floor. This is due to signal attenuation by floors/ceilings along with the 3D distance between the APs and the user. Hapi leverages this observation to achieve accurate and robust location estimates. A deep-learning based method is proposed to identify the user's floor. Then, the identified floor along with the user's visible APs from all floors are used to estimate her 2D location through a novel RSS-Rank Gaussian-based method. Additionally, we present a regression based method to predict Hapi's location estimates' quality and employ it within a Kalman Filter to further refine the accuracy. Our evaluation results, from deployment on various android devices over 6 months with 13 subjects in 5 different up to 9 floors multistory buildings, show that Hapi can identify the user's exact floor up to 95.2% of the time and her 2D location with a median accuracy of 3.5m, achieving 52.1% and 76.0% improvement over related calibration-free state-of-the-art systems respectively.
D-ATR for SAR Images Based on Deep Neural Networks

Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2.
Sensitivity of Multiphase Pseudocontinuous Arterial Spin Labelling (MP pCASL) Magnetic Resonance Imaging for Measuring Brain and Tumour Blood Flow in Mice

Brain and tumour blood flow can be measured noninvasively using arterial spin labelling (ASL) magnetic resonance imaging (MRI), but reliable quantification in mouse models remains difficult. Pseudocontinuous ASL (pCASL) is recommended as the clinical standard for ASL and can be improved using multiphase labelling (MP pCASL). The aim of this study was to optimise and validate MP pCASL MRI for cerebral blood flow (CBF) measurement in mice and to assess its sensitivity to tumour perfusion. Following optimization of the MP pCASL sequence, CBF data were compared with gold-standard autoradiography, showing close agreement. Subsequently, MP pCASL data were acquired at weekly intervals in models of primary and secondary brain tumours, and tumour microvessel density was determined histologically. MP pCASL measurements in a secondary brain tumour model revealed a significant reduction in blood flow at day 35 after induction, despite a higher density of blood vessels. Tumour core regions also showed reduced blood flow compared with the tumour rim. Similarly, significant reductions in CBF were found in a model of glioma 28 days after tumour induction, together with an increased density of blood vessels. These findings indicate that MP pCASL MRI provides accurate and robust measurements of cerebral blood flow in naïve mice and is sensitive to changes in tumour perfusion.
Metal-Free Graphitic Carbon Nitride Photocatalyst Goes Into Two-Dimensional Time

Graphitic carbon nitride (g-C3N4) is always a research hotspot as a metal-free visible-light-responsive photocatalyst, in the field of solar energy conversion (hydrogen-production by water splitting). This critical review summarizes the recent progress in the design and syntheses of two-dimensional (2D) g-C3N4 and g-C3N4-based nanocomposites, covering (1) the modifications of organic carbon nitrogen precursors, such as by heat treatment, metal or metal-free atoms doping, and modifications with organic functional groups, (2) the influencing factors for the formation of 2D g-C3N4 process, including the calcination temperature and protective atmosphere, etc. (3) newly 2D g-C3N4 nanosheets prepared from pristine raw materials and bulk g-C3N4, and the combination of 2D g-C3N4 with other 2D semiconductors or metal atoms as a cocatalyst, and (4) the structures and characteristics of each type of 2D g-C3N4 systems, together with their optical absorption band structures and interfacial charge transfers. In addition, the first-principles density functional theory (DFT) calculation of the g-C3N4 system has been summarized, and this review provides an insightful outlook on the development of 2D g-C3N4 photocatalysts. The comprehensive review is concluded with a summary and future perspective. Moreover, some exciting viewpoints on the challenges, and future directions of 2D g-C3N4 photocatalysts are discussed and highlighted in this review. This review can open a new research avenue for the preparation of 2D g-C3N4 photocatalysts with good performances.
High-Quality Face Capture Using Anatomical Muscles

Muscle-based systems have the potential to provide both anatomical accuracy and semantic interpretability as compared to blendshape models; however, a lack of expressivity and differentiability has limited their impact. Thus, we propose modifying a recently developed rather expressive muscle-based system in order to make it fully-differentiable; in fact, our proposed modifications allow this physically robust and anatomically accurate muscle model to conveniently be driven by an underlying blendshape basis. Our formulation is intuitive, natural, as well as monolithically and fully coupled such that one can differentiate the model from end to end, which makes it viable for both optimization and learning-based approaches for a variety of applications. We illustrate this with a number of examples including both shape matching of three-dimensional geometry as as well as the automatic determination of a three-dimensional facial pose from a single two-dimensional RGB image without using markers or depth information.
Power Allocation in Multi-User Cellular Networks with Deep Q Learning Approach

The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developing in this field, and among them the deep reinforcement learning (DRL) is proved to be of great potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present standard DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified.
Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics

The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect.
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs

Programs on a visual cognitive computer learned underlying concepts and generalized well to new settings without explicit demonstrations. Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, then it would notably improve their ability to understand our intent and to transfer tasks between different environments. To that end, we introduce a computational framework that replicates aspects of human concept learning. Concepts are represented as programs on a computer architecture consisting of a visual perception system, working memory, and action controller. The instruction set of this cognitive computer has commands for parsing a visual scene, directing gaze and attention, imagining new objects, manipulating the contents of a visual working memory, and controlling arm movement. Inferring a concept corresponds to inducing a program that can transform the input to the output. Some concepts require the use of imagination and recursion. Previously learned concepts simplify the learning of subsequent, more elaborate concepts and create a hierarchy of abstractions. We demonstrate how a robot can use these abstractions to interpret novel concepts presented to it as schematic images and then apply those concepts in very different situations. By bringing cognitive science ideas on mental imagery, perceptual symbols, embodied cognition, and deictic mechanisms into the realm of machine learning, our work brings us closer to the goal of building robots that have interpretable representations and common sense.
Jogos computacionais fonoarticulatorios para crianças com deficiencia auditiva

O desenvolvimento de jogos computacionais, para exercicios de aprimoramento da coordenacao fonoarticulatoria, em especial para criancas e adolescentes com deficiencia auditiva, constituiu o objetivo principal deste trabalho. Os jogos desenvolvidos visam proporcionar um ambiente ludico no qual o deficiente auditivo possa sentir prazer ao explorar as suas potencialidades, ao longo da pratica que permitira a coordenacao articulatoria. Esta pratica e necessaria para a obtencao do aprimoramento do controle sensorio-motor e da destreza exigida para a realizacao dos sons, com minimo esforco articulatorio. As aplicacoes exigem modelos matematicos rapidos e precisos, tal que possam proporcionar uma retroalimentacao consequente para a crianca. A enorme variabilidade da voz, em especial de deficientes auditivos, requer algoritmos com grande adaptacao. Conciliar rapidez, precisao e adaptabilidade foi o grande desafio deste projeto. Para superar este desafio foi realizado um minucioso estudo sobre as caracteristicas fisicas dos sons da lingua. Partindo das propriedades fisicas dos sons, foi possivel desenvolver modelos matematicos relativamente simples, precisos e com grande adaptabilidade. Abstract
RIG-I Signaling via MAVS Is Dispensable for Survival in Lethal Influenza Infection In Vivo

Retinoic acid-inducible gene I (RIG-I) is an important regulator of virus-induced antiviral interferons (IFNs) and proinflammatory cytokines. It requires interaction with an adaptor molecule, mitochondrial antiviral-signaling protein (MAVS), to activate downstream signaling pathways. To elucidate the mechanism(s) by which RIG-I-dependent recognition of IAV infection in vivo triggers innate immune responses, we infected mutant mice lacking RIG-I or MAVS with influenza A virus (IAV) and measured their innate immune responses. As has previously been demonstrated with isolated deletion of the virus recognition receptors TLR3, TLR7, and NOD2, RIG-I or MAVS knockout (KO) did not result in higher mortality and did not reduce IAV-induced cytokine responses in mice. Infected RIG-I KO animals displayed similar lung inflammation profiles as did WT mice, in terms of the protein concentration, total cell count, and inflammatory cell composition in the bronchoalveolar lavage fluid. RNA-Seq results demonstrated that all types of mice exhibited equivalent antiviral and inflammatory gene responses following IAV infection. Together, the results indicated that although RIG-I is important in innate cytokine responses in vitro, individual deletion of the genes encoding RIG-I or MAVS did not change survival or innate responses in vivo after IAV infection in mice.