Miracle-dz/UniFAR-FTUs huggingface dataset:
Category Learning in Humans by Equivalence Constraints

We investigate human category learning from partial inform ation provided by equivalence constraints. Human participants lear ned to classify stimuli on the basis of either positive or negative equivale nce constraints, that is, when informed that two exemplars belong to the same c at gory or to different categories, respectively. We discovered th at when provided with positive constraints, participant categorizat ion performance is distributed normally, but when provided with negative cons traints performance distribution is bimodal. A constrained EM algorit hm was used with identical constraint information to simulate the expe rimental setup. Results of the EM clustering algorithm showed surprising qu alitative similarity to human results, with bimodality for negative b ut not positive constraints. Taken together, these results indicate that p ositive constraints provide information that may be used intuitively for catego rization, while use of negative constraint information may require a less na tural rulebased strategy, which most participants failed to implemen t. These results are consistent with the view that humans naturally use similaritybased representations (prototypes or exemplars) as oppose d to rule-based strategies (e.g., strategies that use decision boundaries ).
A COMPARATIVE STUDY FOR CORROSION COMBATING EFFECTS OF NEWLY SYNTHESIZED SCHIFF’S BASE DERIVATIVES IN 2.5 M NITRIC ACID, HYDROCHLORIC ACID AND IN THEIR BINARY MIXTURE ON ALUMINIUM

The inhibition efficiency of three newly synthesized Schiff’s bases Viz. N-(4-N,N’ dimethyl amino benzal)-1naphthyl amine (SB1), N-(4-methoxy benzal)-1-naphthyl amine (SB2) and N-(4-methoxy benzal)-2amino pyridine (SB3) in combating corrosion of aluminium immersed in 2.5M nitric acid solution, 2.5M hydrochloric acid solution alone and in binary mixture of nitric and hydrochloric acid solution has been evaluated by weightloss and thermometric methods. Corrosion combating efficiency increases with increasing inhibitor concentration and was found maximum in HCl alone followed by mixture and HNO3 alone. The adsorption of these derivatives on aluminum surface is in accordance with Langmuir adsorption isotherm.It was found that SB2 is better corrosion inhibitor than SB1and SB3. Results obtained by both the methods have been found to be in good agreement with each other.
FuZZan: Efficient Sanitizer Metadata Design for Fuzzing

Fuzzing is one of the most popular and effective techniques for finding software bugs. To detect triggered bugs, fuzzers leverage a variety of sanitizers in practice. Unfortunately, sanitizers target long running experiments—e.g., developer test suites—not fuzzing, where execution time is highly variable ranging from extremely short to long. Design decisions made for developer test suites introduce high overhead on short lived fuzzing executions, decreasing the fuzzer’s throughput and thereby reducing effectiveness. The root cause of this sanitization overhead is the heavyweight metadata structure that is optimized for frequent metadata operations over long executions. To address this, we design new metadata structures for sanitizers, and propose FuZZan to automatically select the optimal metadata structure without any user configuration. Our new metadata structures have the same bug detection capabilities as the ones they replace. We implement and apply these ideas to Address Sanitizer (ASan), which is the most popular sanitizer. Our evaluation shows that on the Google fuzzer test suite, FuZZan improves fuzzing throughput over ASan by 48% starting with Google’s provided seeds (52% when starting with empty seeds on the same applications). Due to this improved throughput, FuZZan discovers 13% more unique paths given the same 24 hours and finds bugs 42% faster. Furthermore, FuZZan catches all bugs ASan does; i.e., we have not traded precision for performance. Our findings show that sanitizer performance overhead is avoidable when metadata structures are designed for fuzzing, and that the performance difference will have a meaningful difference in squashing software bugs.
Multi-objective topology optimization and losses reduction in a permanent magnet excited synchronous machine

This paper proposes the multi-objective topology optimization of the Electrically Controlled Permanent-Magnet Synchronous Machine (ECPMSM) using the Level Set Method and Continuum Design Sensitivity Analysis (CDSA) in order to reduce both the electromagnetic losses and the Cogging Torque (CT). The back-electromotive force (the back EMF) is taken into account in the multiobjective optimization. The losses of the ECPMSM such as the stator iron losses, rotor eddy current losses and the hysteresis losses are calculated by the 3D timeharmonic Finite Element (FE) analysis. During an iterative shape and topology optimization process, the field velocity in the Level Set Method is evaluated using the CDSA approach. Simulation outcomes show that the applied method leads to a significant reduction of both the electromagnetic losses and the CT and additionally minimizes the higher harmonics in the back-EMF.
Characterization of Impaired Ventricular Repolarization by Quantification of QT Delayed Response to Heart Rate Changes in Stress Test

Increased spatio-temporal heterogeneity in ventricular repolarization is related to cardiac instabilities. Slowed adaptation of the QT interval to sudden abrupt changes in heart rate (HR), measured by the time constant of a first-order-like step response, has been identified as a marker of arrhythmic risk. Since abrupt HR changes are difficult to induce in patients, ramp-like HR variations observed in exercise stress test are considered in this work. The first-order-system time constant is estimated as the delay between the QT series response and the series of memoryless expected QT series from the instantaneous HR, ob-tained from a fitted hyperbolic regression model. The delay was estimated by minimizing the mean square error separately in the exercise and recovery phases. We applied this procedure to a subset of 251 patients of Tampere university hospital with different risk levels for Coronary Artery Disease (110 nonCAD, 39 low-risk, 12 mild-risk, 90 high-risk). The time constant values ranged from 17s to 49s, which are comparable with the time lags reported in other studies in response to step-like HR changes. The delays in exercise and recovery phases and the difference between these delays are markers with capacity for CAD risk stratification.
Digital Contact Tracing Using IP Colocation

The spread of an infectious disease through a population can be modeled using a network or a graph. In digital advertising, internet device graphs are graph data sets that organize identifiers produced by mobile phones, PCs, TVs, and tablets as they access media on the internet. Characterized by immense scale, they have become ubiquitous as they enable targeted advertising, content customization and tracking. This paper posits that internet device graphs, in particular those based on IP colocation, can provide utility for contact tracing and predicting and modeling the spread of infectious disease. Starting the week of March 16th, 2020, in the United States, many individuals began to ‘shelter-in-place’ as schools and workplaces across the nation closed because of the COVID-19 pandemic. This paper quantifies the effect of the shelter-in-place orders on a large scale internet device graph with more than a billion nodes by studying the graph before and after orders went into effect. The structure of the graph suggests behavior least conducive to transmission of infection occurred in the US between April 12th and 19th, 2020. This paper also discusses the utility of device graphs for i) contact tracing, ii) prediction of ‘hot spots’, iii) simulation of infectious disease spread, and iv) delivery of advertisement-based warnings to potentially exposed individuals. The paper also posits an overarching question: can systems and datasets amassed by entities in the digital ad ecosystem aid in the fight against COVID-19?
Model Agnostic Combination for Ensemble Learning

Ensemble of models is well known to improve single model performance. We present a novel ensembling technique coined MAC that is designed to find the optimal function for combining models while remaining invariant to the number of sub-models involved in the combination. Being agnostic to the number of sub-models enables addition and replacement of sub-models to the combination even after deployment, unlike many of the current methods for ensembling such as stacking, boosting, mixture of experts and super learners that lock the models used for combination during training and therefore need retraining whenever a new model is introduced into the ensemble. We show that on the Kaggle RSNA Intracranial Hemorrhage Detection challenge, MAC outperforms classical average methods, demonstrates competitive results to boosting via XGBoost for a fixed number of sub-models, and outperforms it when adding sub-models to the combination without retraining.
Unsupervised Deep Learning of Incompressible Fluid Dynamics

Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer aided aerodynamic design of automobiles or airplanes to simulations of physical effects in CGI to research in meteorology. Recent differentiable fluid simulations allow gradient based methods to optimize e.g. fluid control systems in an informed manner. Solving the partial differential equations governed by the dynamics of the underlying physical systems, however, is a challenging task and current numerical approximation schemes still come at high computational costs. In this work, we propose an unsupervised framework that allows powerful deep neural networks to learn the dynamics of incompressible fluids end to end on a grid-based representation. For this purpose, we introduce a loss function that penalizes residuals of the incompressible Navier Stokes equations. After training, the framework yields models that are capable of fast and differentiable fluid simulations and can handle various fluid phenomena such as the Magnus effect and Karman vortex streets. Besides demonstrating its real-time capability on a GPU, we exploit our approach in a control optimization scenario.
Robot Perception enables Complex Navigation Behavior via Self-Supervised Learning

Learning visuomotor control policies in robotic systems is a fundamental problem when aiming for long-term behavioral autonomy. Recent supervised-learning-based vision and motion perception systems, however, are often separately built with limited capabilities, while being restricted to few behavioral skills such as passive visual odometry (VO) or mobile robot visual localization. Here we propose an approach to unify those successful robot perception systems for active target-driven navigation tasks via reinforcement learning (RL). Our method temporally incorporates compact motion and visual perception data - directly obtained using self-supervision from a single image sequence - to enable complex goal-oriented navigation skills. We demonstrate our approach on two real-world driving dataset, KITTI and Oxford RobotCar, using the new interactive CityLearn framework. The results show that our method can accurately generalize to extreme environmental changes such as day to night cycles with up to an 80% success rate, compared to 30% for a vision-only navigation systems.
A New Run-based Connected Component Labeling for Efficiently Analyzing and Processing Holes

This article introduces a new connected component labeling and analysis algorithm for foreground and background labeling that computes the adjacency tree. The computation of features (bounding boxes, first statistical moments, Euler number) is done on-the-fly. The transitive closure enables an efficient hole processing that can be filled while their features are merged with the surrounding connected component without the need to rescan the image. A comparison with existing algorithms shows that this new algorithm can do all these computations faster than algorithms processing black and white components.
Stick‐slip and convergence of feedback‐controlled systems with Coulomb friction

An analysis of stick‐slip behavior and convergence of trajectories in the feedback‐controlled motion systems with discontinuous Coulomb friction is provided. A closed‐form parameter‐dependent stiction region, around an invariant equilibrium set, is proved to be always reachable and globally attractive. It is shown that only asymptotic convergence can be achieved, with at least one but mostly an infinite number of consecutive stick‐slip cycles, independent of the initial conditions. Theoretical developments are supported by a number of numerical results with dedicated convergence examples.
Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications

Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.
Quantum simulation and circuit design for solving multidimensional Poisson equations

Many methods solve Poisson equations by using grid techniques which discretize the problem in each dimension. Most of these algorithms are subject to the curse of dimensionality, so that they need exponential runtime. In the paper "Quantum algorithm and circuit design solving the Poisson equation" a quantum algorithm is shown running in polylog time to produce a quantum state representing the solution of the Poisson equation. In this paper a quantum simulation of an extended circuit design based on this algorithm is made on a classical computer. Our purpose is to test an efficient circuit design which can break the curse of dimensionality on a quantum computer. Due to the exponential rise of the Hilbert space this design is optimized on a small number of qubits. We use Microsoft's Quantum Development Kit and its simulator of an ideal quantum computer to validate the correctness of this algorithm.
Antiferromagnetic long-range order in the 
5d1
 double-perovskite 
Sr2MgReO6

The double-perovskite A$_2$BB'O$_6$ with heavy transition metal ions on the ordered B' sites is an important family of compounds to study the interplay between electron correlation and spin-orbit coupling (SOC). Here we prepared high-quality Sr$_2$MgReO$_6$ powder and single-crystal samples and performed non-resonant and resonant synchrotron x-ray diffraction experiments to investigate its magnetic ground state. By combining the magnetic susceptibility and heat capacity measurements, we conclude that Sr$_2$MgReO$_6$ exhibits a layered antiferromagnetic (AF) order at temperatures below $\sim$ 55 K with a propagation vector q = (001), which contrasts the previously suspected spin glass state. Our works clarify the magnetic order in Sr$_2$MgReO$_6$ and demonstrate it as a candidate system to look for magnetic octupolar orders and exotic spin dynamics.
Does it matter how well I know what you’re thinking? Opponent Modelling in an RTS game

Opponent Modelling tries to predict the future actions of opponents, and is required to perform well in multiplayer games. There is a deep literature on learning an opponent model, but much less on how accurate such models must be to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS) and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their modelling of the opponent in a simple Real-Time Strategy game. We find that in this domain RHEA is much more sensitive to the accuracy of an opponent model than MCTS. MCTS generally does better even with an inaccurate model, while this will degrade RHEA’s performance. We show that faced with an unknown opponent and a low computational budget it is better not to use any explicit model with RHEA, and to model the opponent’s actions within the tree as part of the MCTS algorithm.
The SPPD System for Schema Guided Dialogue State Tracking Challenge

This paper introduces one of our group's work on the Dialog System Technology Challenges 8 (DSTC8), the SPPD system for Schema Guided dialogue state tracking challenge. This challenge, named as Track 4 in DSTC8, provides a brand new and challenging dataset for developing scalable multi-domain dialogue state tracking algorithms for real world dialogue systems. We propose a zero-shot dialogue state tracking system for this task. The key components of the system is a number of BERT based zero-shot NLU models that can effectively capture semantic relations between natural language descriptions of services' schemas and utterances from dialogue turns. We also propose some strategies to make the system better to exploit information from longer dialogue history and to overcome the slot carryover problem for multi-domain dialogues. The experimental results show that the proposed system achieves a significant improvement compared with the baseline system.
Correlation between electronic and structural orders in 
1T−TiSe2

The correlation between electronic and crystal structures of 1T-TiSe2 in the charge density wave (CDW) state is studied by x-ray diffraction. Three families of reflections are used to probe atomic displacements and the orbital asymmetry in Se. Two distinct onset temperatures are found, TCDW and a lower T* indicative for an onset of Se out-of-plane atomic displacements. T* coincides with a DC resistivity maximum and the onset of the proposed gyrotropic electronic structure. However, no indication for chirality is found. The relation between the atomic displacements and the transport properties is discussed in terms of Ti 3d and Se 4p states that only weakly couple to the CDW order.
Mobile Delivery Robots: Mixed Reality-Based Simulation Relying on ROS and Unity 3D

In the context of Intelligent Transportation Systems and the delivery of goods, new technology approaches need to be developed in order to cope with certain challenges that last mile delivery entails, such as navigation in an urban environment. Autonomous delivery robots can help overcome these challenges. We propose a method for performing mixed reality (MR) simulation with ROS-based robots using Unity, which synchronizes the real and virtual environment, and simultaneously uses the sensor information of the real robots to locate themselves and project them into the virtual environment, so that they can use their virtual doppelganger to perceive the virtual world. Using this method, real and virtual robots can perceive each other and the environment in which the other party is located, thereby enabling the exchange of information between virtual and real objects. Through this approach a more realistic and reliable simulation can be obtained. Results of the demonstrated use-cases verified the feasibility and efficiency as well as the stability of implementing MR using Unity for Robot Operating System (ROS)-based robots.
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing FVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the final answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. On top of the multi-layer graph representations, we propose a modality-aware heterogeneous graph convolutional network to capture evidence from different layers that is most relevant to the given question. Specifically, the intra-modal graph convolution selects evidence from each modality and cross-modal graph convolution aggregates relevant information across different graph layers. By stacking this process multiple times, our model performs iterative reasoning across three modalities and predicts the optimal answer by analyzing all question-oriented evidence. We achieve a new state-of-the-art performance on the FVQA task and demonstrate the effectiveness and interpretability of our model with extensive experiments.
Causal Knowledge Extraction from Scholarly Papers in Social Sciences

The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge. In this paper, we seek to develop natural language processing (NLP) models to accelerate the extraction of relationships from scholarly papers in social sciences, identify hypotheses from these papers, and extract the cause-and-effect entities. Specifically, we develop models to 1) classify sentences in scholarly documents in business and management as hypotheses (hypothesis classification), 2) classify these hypotheses as causal relationships or not (causality classification), and, if they are causal, 3) extract the cause and effect entities from these hypotheses (entity extraction). We have achieved high performance for all the three tasks using different modeling techniques. Our approach may be generalizable to scholarly documents in a wide range of social sciences, as well as other types of textual materials.