Miracle-dz/UniFAR-FTUs huggingface dataset:
RARITYNet: Rarity Guided Affective Emotion Learning Framework

—Inspired from the assets of handcrafted and deep learning approaches, we proposed a RARITYNet: RARITY guided affective emotion learning framework to learn the appearance features and identify the emotion class of facial expressions. The RARITYNet framework is designed by combining the shallow (RARITY) and deep (AffEmoNet) features to recognize the facial expressions from challenging images as spontaneous expressions, pose variations, ethnicity changes, and illumination conditions. The RARITY is proposed to encode the inter-radial transitional patterns in the local neighbourhood. The AffEmoNet: affective emotion learning network is proposed by incorporating three feature streams: high boost edge ﬁltering (HBSEF) stream, to extract the edge information of highly affected facial expressive regions, multi-scale sophisticated edge cumulative (MSSEC) stream is to learns the sophisticated edge information from multi-receptive ﬁelds and RARITY uplift complementary context feature (RUCCF) stream reﬁnes the RARITY-encoded features and aid the MSSEC stream features to enrich the learning ability of RARITYNet.
Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial diﬀerential equations (PDEs), and have been widely used in a variety of PDE problems. However, there still remain some challenges in the application of PINNs: 1) the mechanism of PINNs is unsuitable (at least cannot be directly applied) to exploiting a small size of (usually very few) extra informative samples to reﬁne the networks; and 2) the eﬃciency of training PINNs often becomes low for some complicated PDEs. In this paper, we propose the generative adversarial physics-informed neural network (GA-PINN), which integrates the generative adversarial (GA) mechanism with the structure of PINNs, to improve the performance of PINNs by exploiting only a small size of exact solutions to the PDEs. Inspired from the weighting strategy of the Adaboost method, we then introduce a point-weighting (PW) method to improve the training eﬃciency of PINNs, where the weight of each sample point is adaptively updated at each training iteration. The numerical experiments show that GA-PINNs outperform PINNs in many well-known PDEs and the PW method also improves the eﬃciency of training PINNs and GA-PINNs.
i-MYO: A Hybrid Prosthetic Hand Control System based on Eye-tracking, Augmented Reality and Myoelectric signal

— Dexterous prosthetic hands have better grasp performance than traditional ones. However, patients still find it difficult to use these hands without a suitable control system. A new hybrid myoelectric control system, termed i-MYO , is presented and evaluated to solve this problem. The core component of the i-MYO is a novel grasp-type switching interface based on eye-tracking and augmented reality (AR), termed i-GSI. With the i-GSI, the user can easily switch a grasp type (six total) for a prosthetic hand by gazing at a GazeButton. The i-GSI is implemented in an AR helmet and is integrated, as an individual module, into the i-MYO system. In the i-MYO system, the myoelectric signal was used to control hand opening /closing proportionally. The operation of the i-MYO was tested on nine healthy subjects who wore HIT-V hand on the forearm and manipulated objects in a reach-and-grasp task. It was also tested on one patient who had an inferior myoelectric signal and was required to control the HIT-V hand to grasp objects. Results showed that in 91.6% of the trials, inexperienced healthy subjects accomplished the task within 5.9 s, and most failed trials were caused by a lack of experience in fine grasping. In addition, in about 1.5% of trials, the subjects also successfully transferred the objects but with a non-optimal grasp type. In 97.0% of the trials, the subjects spent ~1.3 s switching the optimal grasp types. A higher success rate in grasp type (99.1%) for the untrained patient has been observed thanks to more trials conducted. In 98.7 % of trials, the patient only needed another 2 s to control the hand to grasp the object after switching to the optimal grasp type. The tests demonstrate the control capability of the new system in multi-DOF prosthetics, and all inexperienced subjects were able to master the operation of the i-MYO quickly within a few pieces of training and apply it easily.
Understanding Factors that Shape Children’s Long Term Engagement with an In-Home Learning Companion Robot

Social robots are emerging as learning companions for children, and research shows that they facilitate the development of interest and learning even through brief interactions. However, little is known about how such technologies might support these goals in authentic environments over long-term periods of use and interaction. We designed a learning companion robot capable of supporting children reading popular-science books by expressing social and informational commentaries. We deployed the robot in homes of 14 families with children aged 10–12 for four weeks during the summer. Our analysis revealed critical factors that affected children’s long-term engagement and adoption of the robot, including external factors such as vacations, family visits, and extracurricular activities; family/parental involvement; and children’s individual interests. We present four in-depth cases that illustrate these factors and demonstrate their impact on children’s reading experiences and discuss the implications of our findings for robot design.
Majorana-Weyl cones in ferroelectric superconductors

Topological superconductors are predicted to exhibit outstanding phenomena, including non-abelian anyon excitations, heat-carrying edge states, and topological nodes in the Bogoliubov spectra. Nonetheless, and despite major experimental efforts, we are still lacking unambiguous signatures of such exotic phenomena. In this context, the recent discovery of coexisting superconductivity and ferroelectricity in lightly doped and ultra clean SrTiO$_3$ opens new opportunities. Indeed, a promising route to engineer topological superconductivity is the combination of strong spin-orbit coupling and inversion-symmetry breaking. Here we study a three-dimensional parabolic band minimum with Rashba spin-orbit coupling, whose axis is aligned by the direction of a ferroelectric moment. We show that all of the aforementioned phenomena naturally emerge in this model when a magnetic field is applied. Above a critical Zeeman field, Majorana-Weyl cones emerge regardless of the electronic density. These cones manifest themselves as Majorana arcs states appearing on surfaces and tetragonal domain walls. Rotating the magnetic field with respect to the direction of the ferroelectric moment tilts the Majorana-Weyl cones, eventually driving them into the type-II state with Bogoliubov Fermi surfaces. We then consider the consequences of the orbital magnetic field. First, the single vortex is found to be surrounded by a topological halo, and is characterized by two Majorana zero modes: One localized in the vortex core and the other on the boundary of the topological halo. Based on a semiclassical argument we show that upon increasing the field above a critical value the halos overlap and eventually percolate through the system, causing a bulk topological transition that always precedes the normal state. Finally, we propose concrete experiments to test our predictions.
Coherence-Based Frequency Subset Selection for Binaural RTF-vector-based Direction of Arrival Estimation for Multiple Speakers

Recently, a method has been proposed to estimate the direction of arrival (DOA) of a single speaker by minimizing the frequency-averaged Hermitian angle between an estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors. In this paper, we extend this method to multi-speaker localization by introducing the frequency-averaged Hermitian angle spectrum and selecting peaks of this spatial spectrum. To construct the Hermitian angle spectrum, we consider only a subset of frequencies, where it is likely that one speaker is dominant. We compare the effectiveness of the generalized magnitude squared coherence and two coherent-to-diffuse ratio (CDR) estimators as frequency selection criteria. Simulation results for estimating the DOAs of two speakers in a reverberant environment with diffuse-like babble noise using binaural hearing devices show that using the binaural effective-coherence-based CDR estimate as a frequency selection criterion yields the best performance.
Constraining the Attack Space of Machine Learning Models with Distribution Clamping Preprocessing

Preprocessing and outlier detection techniques have both been applied to neural networks to increase robustness with varying degrees of success. In this paper, we formalize the ideal preprocessor function as one that would take any input and set it to the nearest in-distribution input. In other words, we detect any anomalous pixels and set them such that the new input is in-distribution. We then illustrate a relaxed solution to this problem in the context of patch attacks. Speciﬁcally, we demonstrate that we can model constraints on the patch attack that specify regions as out of distribution. With these constraints, we are able to preprocess inputs successfully, increasing robustness on CARLA object detection.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders

Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers’ decision-making. While some prior work has explored impacted stakeholders’ concerns with current uses of data-driven predictive risk models (PRMs), less work has asked stakeholders whether such tools ought to be used in the first place. In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system. We found that participants worried current PRMs perpetuate or exacerbate existing problems in child welfare. Participants suggested new ways to use data and data-driven tools to better support impacted communities and suggested paths to mitigate possible harms of these tools. Participants also suggested low-tech or no-tech alternatives to PRMs to address problems in child welfare. Our study sheds light on how researchers and designers can work in solidarity with impacted communities, possibly to circumvent or oppose child welfare agencies.
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation

Recent work on curvilinear structure segmentation has mostly focused on backbone network design and loss engineering. The challenge of collecting labelled data, an expensive and labor intensive process, has been overlooked. While labelled data is expensive to obtain, unlabelled data is often readily available. In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden. Our framework addresses two key challenges in formulating curvilinear segmentation in a semi-supervised manner. First, to fully exploit the power of consistency based SSL, we introduce a geometric transformation as strong data augmentation and then align segmentation predictions via a differentiable inverse transformation to enable the computation of pixel-wise consistency. Second, the traditional mean square error (MSE) on unlabelled data is prone to collapsed predictions and this issue exacerbates with severe class imbalance (significantly more background pixels). We propose a N-pair consistency loss to avoid trivial predictions on unlabelled data. We evaluate SemiCurv on six curvilinear segmentation datasets, and find that with no more than 5% of the labelled data, it achieves close to 95% of the performance relative to its fully supervised counterpart.
SoK: The Impact of Unlabelled Data in Cyberthreat Detection

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. This paper is aimed at systematization of existing work on SsL for CTD and, in particular, on understanding the utility of unlabelled data in such systems. To this end, we analyze the cost of labelling in various CTD tasks and develop a formal cost model for SsL in this context. Building on this foundation, we formalize a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. We review the state-of-the-art and observe that no previous work meets such requirements. To address this problem, we propose a framework for assessing the benefits of unlabelled data in SsL. We showcase an application of this framework by performing the first benchmark evaluation that highlights the tradeoffs of 9 existing SsL methods on 9 public datasets. Our findings verify that, in some cases, unlabelled data provides a small, but statistically significant, performance gain. This paper highlights that SsL in CTD has a lot of room for improvement, which should stimulate future research in this field.
Power Divider/Combiner using Half-Mode Substrate Integrated Waveguide (HMSIW) Technology with High Power and High Isolation

ABSTRACT A power divider/combiner using half-mode substrate integrated waveguide (HMSIW) and microstrip transmission lines with high isolation for high-power applications is presented. The design consists of a planar cavity, two 90° microstrip transmission lines, two matched loads, and one 180° microstrip transmission line. The cavity is fabricated using HMSIW structure and excited the TE101 mode at the centre frequency of 4.875 GHz. The external microstrip transmission lines and matched loads are added to improve the isolation between output ports. Moreover, the matched loads prevent the appeared heat loss in the substrate. The circuit behaviour is analysed based on the even and odd mode techniques, and the structure is simulated using a 3D EM simulator. The design is fabricated on a single-layer Rogers RO4003C, and a good agreement between the results of the simulation and measurement is observed. The measured results show that the proposed structure has the insertion loss of dB, and the isolation and output return loss better than 21.6 and 13.7 dB, respectively, when the return loss of the input port is better than 15 dB (4.8–4.95 GHz). This design has many advantages, such as low cost, low insertion loss, good return loss, good channels isolation, low profile, and compatibility with planar circuits.
Representations of free products of semisimple algebras via quivers

Let K denote an algebraically closed ﬁeld and A a free product of ﬁnitely many semisimple associative K -algebras. We associate to A a ﬁnite acyclic quiver Γ and show that the category of ﬁnite dimensional A -modules is equivalent to a full subcategory of the category rep(Γ) of ﬁnite dimensional representations of Γ. Under this equivalence, the simple A -modules correspond exactly to the θ -stable representations of Γ for some stability parameter θ . This gives us necessary conditions for an A -module to be simple, conditions which are also suﬃcient if the module is in general position. Even though there are indecomposable modules that are not simple, we prove that a module in general position is always semisimple. We also discuss the construction of arbitrary ﬁnite dimensional modules using nilpotent representations of quivers. Finally, we apply our results to the case of a free product of ﬁnite groups when K has characteristic zero.
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning

In the classical Reinforcement Learning (RL) setting, one aims to find a policy that maximizes its expected return. This objective may be inappropriate in safety-critical domains such as healthcare or autonomous driving, where intrinsic uncertainties due to stochastic policies and environment variability may lead to catastrophic failures. This can be addressed by using the Conditional-Value-at-Risk (CVaR) objective to instill risk-aversion in learned policies. In this paper, we propose Adversarial Cvar Reinforcement Learning (ACReL), a novel adversarial meta-algorithm to optimize the CVaR objective in RL. ACReL is based on a max-min between a policy player and a learned adversary that perturbs the policy player's state transitions given a finite budget. We prove that, the closer the players are to the game's equilibrium point, the closer the learned policy is to the CVaR-optimal one with a risk tolerance explicitly related to the adversary's budget. We provide a gradient-based training procedure to solve the proposed game by formulating it as a Stackelberg game, enabling the use of deep RL architectures and training algorithms. Empirical experiments show that ACReL matches a CVaR RL state-of-the-art baseline for retrieving CVaR optimal policies, while also benefiting from theoretical guarantees.
Eight Years Superb Performance of a Motorcycles Race Track Pavement – A Design and Construction Review

"This paper is part of the conference proceedings for the 52nd Associated Schools of Construction Annual International Conference in April 2016 at Brigham Young University in Provo, Utah. ; The primary purposes of this paper are to review the special pavement design and construction activities employed in construction of the Calabogie race track after a visual conduction survey to the surface conditions and interaction with the operation personnel and users to verify the performance of the track during the eight yearsâ€™ service, and the effectiveness of the construction strategies, materials and mix designs used in the asphalt paving eight years ago. It was found that the pavement demonstrated good condition after eight yearsâ€™ service including hosting numerous racing and competition events. Special construction methods, material designs and quality control and quality assurance procedures are the major factors contributing to the success of the track. This paper reviews the hot mix asphalt design, and special construction techniques employed during the construction including the project specifications, special paving techniques, field quality control and assurance, hydrated lime application in the mix and during paving. The review of the construction of the race track surface paving will have referential value for asphalt paving activities in highway construction"
Rose-Hulman Undergraduate Mathematics Journal Rose-Hulman Undergraduate Mathematics Journal A Procedure for Determining the Exact Solution to a 2x2 First A Procedure for Determining the Exact Solution to a 2x2 First Order Homogeneous Nonautonomous System Order Homogeneous Nonautonomous System

equations when seeking a solution, the method presented as the primary result solves a nonlinear equation in order to construct the exact solution of a linear system. The author’s findings show that the exact solution to a 2 × 2 nonautonomous system is directly related to the solution of a Riccati differential equation that is constructed from the coefficients in the matrix A ( t ) of a system x ′ = A ( t ) x , and the general structure of the exact solution to a nonautonomous system is similar to the general form of the exact solution for an autonomous system. However, without a method of solving the general Riccati differential equation, a procedure for determining the exact solution to a general 2 × 2 nonautonomous system remains elusive. Nevertheless, the main result does develop an approach for finding solutions to many complicated special cases.
Communication Strategies of New Media for Women Leaders-taking Lani-Rob Ray, Philippine Vice President as An Example

The influence of network news and social internet is surging on the background of new media through which Women leaders interact with netizes, consequently strategies on new media attract the attention of researchers. Having sort out relevant theories, we have analyzed online new media of Lani·Rob Ray, Philippine Vice President, and the main three accounts for three network. Review has made on topics and events from January to October, 2016 such as presidential election and China South Seas Arbitration Issue. We discover that under new media several methods have impacts on pubic domestically and internationally, taking advantages of new media worldly to express their statements objectively and prudently enables it possible to actively communicate with netizes, so the kindness and regulation abilities will be given full play. In the end, it provides new ways for china’s female leaders on communication strategies of new media to learn.
Relationship between Occupant Characteristics, Building Characteristics, and Pre-Fire Evacuation Time in Medium Plastic Industries in Thailand

An evaluation of Required Safe Egress Time (RSET) is a crucial step in performance based on fire safety engineering design. Moreover, pre-evacuation time is also essential for RSET. Consequently, pre-evacuation time is regarded as key aspects of evacuation process. The purpose of this cross-sectional study was to determine factors related to pre-evacuation time. Subjects were 375 workers among ten medium plastic industries. Data were collected by using a standardized occupant characteristics questionnaire, building characteristics survey and standardized observation form. Pearson’s correlation, ANOVA and Regression analysis were performed in the analysis. The findings revealed several factors significantly associated with pre-evacuation time (P<0.05). Those factors are number of building floors, building age, number of windows, number of fire exit doors, distance from work station to fire exit door, width of fire exit door. The occupant characteristics were also associated with pre-evacuation time, which are not participated in fire exercise and occupant attitude. Regression analysis covers eight affected variables from those analysis. Only four influenced variables consisted of occupant attitude in fire, number of fire exit door, no fire training participation, and distance from work station to fire exit door were tested by using regression analysis. A multiple regression model was run to predict pre-evacuation time from four influenced variables. These variables statistically significantly predicted pre-evacuation time, the regression model is a good fit of the data. These are crucial factors to determine pre-evacuation time in further research.