Miracle-dz/UniFAR-FTUs huggingface dataset:
PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories

We present PPQ-trajectory, a spatio-temporal quantization based solution for querying large dynamic trajectory data. PPQ-trajectory includes a partition-wise predictive quantizer (PPQ) that generates an error-bounded codebook with autocorrelation and spatial proximity-based partitions. The codebook is indexed to run approximate and exact spatio-temporal queries over compressed trajectories. PPQ-trajectory includes a coordinate quadtree coding for the codebook with support for exact queries. An incremental temporal partition-based index is utilised to avoid full reconstruction of trajectories during queries. An extensive set of experimental results for spatio-temporal queries on real trajectory datasets is presented. PPQ-trajectory shows significant improvements over the alternatives with respect to several performance measures, including the accuracy of results when the summary is used directly to provide approximate query results, the spatial deviation with which spatio-temporal path queries can be answered when the summary is used as an index, and the time taken to construct the summary. Superior results on the quality of the summary and the compression ratio are also demonstrated.
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer

Visual localization occupies an important position in many areas such as Augmented Reality, robotics and 3D reconstruction. The state-of-the-art visual localization methods perform pose estimation using geometry based solver within the RANSAC framework. However, these methods require accurate pixel-level matching at high image resolution, which is hard to satisfy under significant changes from appearance, dynamics or perspective of view. End-to-end learning based regression networks provide a solution to circumvent the requirement for precise pixel-level correspondences, but demonstrate poor performance towards cross-scene generalization. In this paper, we explicitly add a learnable matching layer within the network to isolate the pose regression solver from the absolute image feature values, and apply dimension regularization on both the correlation feature channel and the image scale to further improve performance towards generalization and large viewpoint change. We implement this dimension regularization strategy within a two-layer pyramid based framework to regress the localization results from coarse to fine. In addition, the depth information is fused for absolute translational scale recovery. Through experiments on real world RGBD datasets we validate the effectiveness of our design in terms of improving both generalization performance and robustness towards viewpoint change, and also show the potential of regression based visual localization networks towards challenging occasions that are difficult for geometry based visual localization methods.
Nematic antiferromagnetism and deconfined criticality from the interplay between electron-phonon and electron-electron interactions

Systems with strong electron-phonon couplings typically exhibit various forms of charge order, while strong electron-electron interactions lead to magnetism. We use determinant quantum Monte Carlo (DQMC) calculations to solve a model on a square lattice with a caricature of these interactions. In the limit where electron-electron interactions dominate it has antiferromagnetic (AF) order, while where electron-phonon coupling dominates there is columnar valence-bond solid (VBS) order. We find a novel intervening phase that hosts coexisting nematic and antiferromagnetic orders. We have also found evidence of a Landau-forbidden continuous quantum phase transition with an emergent $O(4)$ symmetry between the VBS and the nematic antiferromagnetic phases.
Speakerfilter-Pro: an improved target speaker extractor combines the time domain and frequency domain

Speaker extraction is to extract the target speaker from the mixture using a short piece of anchor speech. This paper introduces an improved target speaker extractor, referred to as Speakerfilter-Pro, based on previous Speakerfilter model. The Speakerfilter uses a bi-direction gated recurrent unit (BGRU) module to characterize the target speaker from anchor speech and uses a convolutional recurrent network (CRN) module to separate the target speech from a noisy signal. Different from the Speakerfilter, the Speakerfilter-Pro sticks a WaveUNet module in the beginning and the ending, respectively. The Wave-UNet has been proven to have a better ability to perform speech separation in the time domain. In order to extract the target speaker information better, the complex spectrum instead of the magnitude spectrum is utilized as the input feature for the CRN module. Experiments are conducted on the two-speaker dataset (WSJO-mix2) which is widely used for speaker extraction. The systematic evaluation shows that the Speakerfilter-Pro outperforms the Speakerfilter and other baselines, and achieves a signal-to-distortion ratio (SDR) of 14.95 dB.
Molecular docking and in silico ADMET studies of silibinin and glycyrrhetic acid anti-inflammatory activity

Purpose: To use in silico docking analysis and ADMET prediction of silibinin and glycyrrhetic acid to determine their pharmacokinetic and pharmacodynamic properties as therapeutic molecules against inflammatory disorders. Methods: The study utilized plant-derived compounds with known anti-inflammatory activity. Three important enzymes, including COX-2, 5β-reductase and phospholipase A2, that are involved in the mediation of inflammatory processes, were chosen as protein targets for the ligands (silibinin and glycyrrhetic acid). Q-Site Finder and admetSAR were employed for active site prediction and ADMET profile, respectively. Furthermore, protein-ligand complexes were visually inspected by LigPlot and Chimera. Results: Post-docking analysis confirmed strong interaction of silibinin and glycyrrhetic acid with their respective targets. ADMET profiles for both compounds were very promising. Both ligands (silibinin and glycyrrhetic acid) showed strong binding energy for all three target proteins (-7.5 to -10.9 kcal/mol). Moreover, Asp347, Gln350, Gly354, Gln192, His351, Ser579 and Phe580 were the common interacting residues in the target proteins for both ligands. Conclusion: Glycyrrhetic acid possesses superior ADMET profile to silibinin. Hydrophobic interactions between the two ligands (glycyrrhetinic acid and silibinin) and the three target proteins (COX-2, phospholipase A2 and 5β-reductase) are significant. Keywords: Silibinin, Glycyrrhetic acid, ADMET, Docking studies, Phospholipase A2, COX-2, 5β-Reductase
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizable dynamics model robust to changes in dynamics remains a challenge since the target transition dynamics follow a multi-modal distribution. In this paper, we present a new model-based RL algorithm, coined trajectory-wise multiple choice learning, that learns a multi-headed dynamics model for dynamics generalization. The main idea is updating the most accurate prediction head to specialize each head in certain environments with similar dynamics, i.e., clustering environments. Moreover, we incorporate context learning, which encodes dynamics-specific information from past experiences into the context latent vector, enabling the model to perform online adaptation to unseen environments. Finally, to utilize the specialized prediction heads more effectively, we propose an adaptive planning method, which selects the most accurate prediction head over a recent experience. Our method exhibits superior zero-shot generalization performance across a variety of control tasks, compared to state-of-the-art RL methods. Source code and videos are available at this https URL.
Out-of-distribution detection for regression tasks: parameter versus predictor entropy

It is crucial to detect when an instance lies downright too far from the training samples for the machine learning model to be trusted, a challenge known as out-of-distribution (OOD) detection. For neural networks, one approach to this task consists of learning a diversity of predictors that all can explain the training data. This information can be used to estimate the epistemic uncertainty at a given newly observed instance in terms of a measure of the disagreement of the predictions. Evaluation and certification of the ability of a method to detect OOD require specifying instances which are likely to occur in deployment yet on which no prediction is available. Focusing on regression tasks, we choose a simple yet insightful model for this OOD distribution and conduct an empirical evaluation of the ability of various methods to discriminate OOD samples from the data. Moreover, we exhibit evidence that a diversity of parameters may fail to translate to a diversity of predictors. Based on the choice of an OOD distribution, we propose a new way of estimating the entropy of a distribution on predictors based on nearest neighbors in function space. This leads to a variational objective which, combined with the family of distributions given by a generative neural network, systematically produces a diversity of predictors that provides a robust way to detect OOD samples.
Development of Brain Network Connectivity in Risky Drinkers During Adolescence

Alcohol consumption is common in adolescence, a time when the human brain undergoes substantial development, raising concerns about the neurodevelopmental impact of drinking alcohol, especially at high levels. Risky drinking may adversely affect the developing white matter, comprised of axonal fibre pathways that integrate anatomically distributed, and functionally specialised, neural systems. We used diffusion-weighted magnetic resonance imaging (MRI) to perform the first prospective, comprehensive and regionally unbiased connectome-wide analysis of longitudinal changes in inter-regional structural connectivity between 16.5 and 18.8 years of age, comparing adolescents who reported frequent risky drinking (n = 20) with those who reported limited risky drinking (n = 19) across this developmental period. We found no difference in the development of structural connectivity between these groups, regardless of whether inter-regional connections were quantified using streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, or radial diffusivity. These findings suggest that risky drinking may have limited adverse effects on the development of inter-regional structural connectivity during mid to late adolescence.
ExplanationLP: Abductive Reasoning for Explainable Science Question Answering

We propose a novel approach for answering and explaining multiple-choice science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as an abductive reasoning problem, constructing plausible explanations for each choice and then selecting the candidate with the best explanation as the final answer. Our system, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and extracting the facts that satisfy certain structural and semantic constraints. To extract the explanations, we employ a linear programming formalism designed to select the optimal subgraph. The graphs' weighting function is composed of a set of parameters, which we fine-tune to optimize answer selection performance. We carry out our experiments on the WorldTree and ARC-Challenge corpus to empirically demonstrate the following conclusions: (1) Grounding-Abstract inference chains provides the semantic control to perform explainable abductive reasoning (2) Efficiency and robustness in learning with a fewer number of parameters by outperforming contemporary explainable and transformer-based approaches in a similar setting (3) Generalisability by outperforming SOTA explainable approaches on general science question sets.
Recurrent Neural Based Electricity Load Forecasting of G-20 Members

Forecasting the actual amount of electricity with respect to the need/demand of the load is always been a challenging task for each power plants based generating stations. Due to uncertain demand of electricity at receiving end of station causes several challenges such as: reduction in performance parameters of generating and receiving end stations, minimization in revenue, increases the jeopardize for the utility to predict the future energy need for a company etc. With this issues, the precise forecasting of load at the receiving end station is very consequential parameter to establish the impeccable balance between supply and demand chain. In this paper, the load forecasting of G-20 members have been performed utilizing the Recurrent Neural Network coupled with sliding window approach for data generation. During the experimentation we have achieved Mean Absolute Test Error of 16.2193 TWh using LSTM.
Intercultural Conflict and Experiences of International Students in Turkey

Abstract Conflict is a part of daily life and it can occur for many reasons. In an intercultural environment, both international and local people can face conflict due to their interactions. Even though there has been an exponential increase in the growth of international students in Turkey in recent years, little is known about their experiences in the realm of intercultural conflicts. Therefore, the main objective of this study was to assess the occurrence of intercultural conflicts among international students at Anadolu University. In particular, the study sought to examine possible intercultural conflicts this group of students might experience, international students’ intercultural awareness, the major sources of the intercultural conflict and how the international students cope with intercultural conflict. A qualitative research method with phenomenological research design was utilized and semi-structured in-depth interviews were used to gather data. The analysis of the study was organized into six themes, namely: awareness, approach, experience, communication style, worldview change, and coping strategy. Except for the last theme, the obtained data aligned with the literature. Findings revealed that the international students experienced intercultural conflicts mainly due to lack of intercultural competence. Based on these findings, recommendations for future studies were suggested.
Learning Fast Approximations of Sparse Nonlinear Regression

The idea of unfolding iterative algorithms as deep neural networks has been widely applied in solving sparse coding problems, providing both solid theoretical analysis in convergence rate and superior empirical performance. However, for sparse nonlinear regression problems, a similar idea is rarely exploited due to the complexity of nonlinearity. In this work, we bridge this gap by introducing the Nonlinear Learned Iterative Shrinkage Thresholding Algorithm (NLISTA), which can attain a linear convergence under suitable conditions. Experiments on synthetic data corroborate our theoretical results and show our method outperforms state-of-the-art methods.
Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks

In this paper, we present a novel approach to identify the Spatio-temporal and environmental features that influence the safety of a road, and predict its accident proneness based on these features. A total of 14 features were compiled based on Time, Weather and Location (TWL) specificity along a road. To determine the influence each of the 14 features carries, a sensitivity study was performed using Principal Component Analysis. Using the locations of accident warnings, a Safety Index was developed to quantify how accident-prone a particular road is. We implement a novel approach to predict the Safety Index of a road- based on its TWL specificity by using a Graph Neural Network (GNN) architecture. The proposed architecture is uniquely suited for this application due to its ability to capture the complexities of the inherent nonlinear interlinking in a vast feature space. We employed a GNN to emulate the TWL feature vectors as individual nodes which were interlinked vis-à-vis edges of a graph. This model was verified to perform better than Logistic Regression, simple Feed- Forward Neural Networks, and even Long Short Term Memory (LSTM) Neural Networks. We validated our approach on a data set containing the alert locations along the routes of inter-state buses. The results achieved through this GNN architecture, using a TWL input feature space proved to be more feasible than the other predictive models, having reached a peak accuracy of 65%.
The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation

Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural language dialogs between a human Driver controlling a robot and a human Commander providing guidance towards navigation goals. In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move the robot to a sequence of target objects. We introduce a Localization from Dialog History (LDH) and a Navigation from Dialog History (NDH) task where a learned agent is given dialog and visual observations from the robot platform as input and must localize in the global map or navigate towards the next target object, respectively. RobotSlang is comprised of nearly 5k utterances and over 1k minutes of robot camera and control streams. We present an initial model for the NDH task, and show that an agent trained in simulation can follow the RobotSlang dialog-based navigation instructions for controlling a physical robot platform. Code and data are available at https://umrobotslang.github. io/.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation

Knowledge graphs (KGs) have helped neural-symbolic models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over the KG, such models can also "explain" which KG information was most relevant for making a given prediction. In this paper, we question whether these models are really behaving as we expect. We demonstrate that, through a reinforcement learning policy (or even simple heuristics), one can produce deceptively perturbed KGs which maintain the downstream performance of the original KG while significantly deviating from the original semantics and structure. Our findings raise doubts about KG-augmented models' ability to leverage KG information and provide plausible explanations.
RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.
Dynamics of actions of automorphisms of discrete groups $G$ on $\mathrm{Sub}_G$ and applications to lattices in Lie groups

For a discrete group $G$ and the compact space Sub$_G$ of (closed) subgroups of $G$ endowed with the Chabauty topology, we study the dynamics of actions of automorphisms of $G$ on Sub$_G$ in terms of distality and expansivity. We also study the structure and properties of lattices $\Gamma$ in a connected Lie group. In particular, we show that the unique maximal solvable normal subgroup of $\Gamma$ is polycyclic and the corresponding quotient of $\Gamma$ is either finite or admits a cofinite subgroup which is a lattice in a connected semisimple Lie group with certain properties. We also show that Sub$^c_\Gamma$, the set of cyclic subgroups of $\Gamma$, is closed in Sub$_\Gamma$. We prove that an infinite discrete group $\Gamma$ which is either polycyclic or a lattice in a connected Lie group, does not admit any automorphism which acts expansively on Sub$^c_\Gamma$, while only the finite order automorphisms of $\Gamma$ act distally on Sub$^c_\Gamma$. For an automorphism $T$ of a connected Lie group $G$ and a $T$-invariant lattice $\Gamma$ in $G$, we compare the behaviour of the actions of $T$ on Sub$_G$ and Sub$_\Gamma$ in terms of distality. We put certain conditions on the structure of the Lie group $G$ under which we show that $T$ acts distally on Sub$_G$ if and only if it acts distally on Sub$_\Gamma$. We construct counter examples to show that this does not hold in general if the conditions on the Lie group are relaxed.
A "DIY" data acquisition system for acoustic field measurements under harsh conditions

Monitoring active volcanos is an ongoing and important task helping to understand and predict volcanic eruptions. In recent years, analysing the acoustic properties of eruptions became more relevant. We present an inexpensive, lightweight, portable, easy to use and modular acoustic data acquisition system for field measurements that can record data with up to 100~kHz. The system is based on a Raspberry Pi 3 B running a custom build bare metal operating system. It connects to an external analog - digital converter with the microphone sensor. A GPS receiver allows the logging of the position and in addition the recording of a very accurate time signal synchronously to the acoustic data. With that, it is possible for multiple modules to effectively work as a single microphone array. The whole system can be build with low cost and demands only minimal technical infrastructure. We demonstrate a possible use of such a microphone array by deploying 20 modules on the active volcano \textit{Stromboli} in the Aeolian Islands by Sicily, Italy. We use the collected acoustic data to indentify the sound source position for all recorded eruptions.