Miracle-dz/UniFAR-FTUs huggingface dataset:
Spain-Net: Spatially-Informed Stereophonic Music Source Separation

With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While existing deep learning models implicitly absorb the spatial information conveyed by the multi-channel input signals, we argue that a more explicit and active use of spatial information could not only improve the separation process but also provide an entry-point for many user-interaction based tools. To this end, we introduce a control method based on the stereophonic location of the sources of interest, expressed as the panning angle. We present various conditioning mechanisms, including the use of raw angle and its derived feature representations, and show that spatial information helps. Our proposed approaches improve the separation performance compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based simulated experiments. Furthermore, the proposed methods allow for the disentanglement of same-class instruments, for example, in mixtures containing two guitar tracks. Finally, we also demonstrate that our approach is robust to incorrect source panning information, which can be incurred by our proposed user interaction.
Deep Convolutional Autoencoder for Assessment of Anomalies in Multi-stream Sensor Data

A fully convolutional autoencoder is developed for the detection of anomalies in multi-sensor vehicle drive-cycle data from the powertrain domain. Preliminary results collected on real-world powertrain data show that the reconstruction error of faulty drive cycles deviates significantly relative to the reconstruction of healthy drive cycles using the trained autoencoder. The results demonstrate applicability for identifying faulty drive-cycles, and for improving the accuracy of system prognosis and predictive maintenance in connected vehicles.
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

Convolutional neural network (CNN) architectures are generally heavy on memory and computational requirements which make them infeasible for embedded systems with limited hardware resources. We propose dual convolutional kernels (DualConv) for constructing lightweight deep neural networks. DualConv combines $3\times 3$ and $1\times 1$ convolutional kernels to process the same input feature map channels simultaneously and exploits the group convolution technique to efficiently arrange convolutional filters. DualConv can be employed in any CNN model such as VGG-16 and ResNet-50 for image classification, you only look once (YOLO) and R-CNN for object detection, or fully convolutional network (FCN) for semantic segmentation. In this work, we extensively test DualConv for classification since these network architectures form the backbone for many other tasks. We also test DualConv for image detection on YOLO-V3. Experimental results show that, combined with our structural innovations, DualConv significantly reduces the computational cost and number of parameters of deep neural networks while surprisingly achieving slightly higher accuracy than the original models in some cases. We use DualConv to further reduce the number of parameters of the lightweight MobileNetV2 by 54% with only 0.68% drop in accuracy on CIFAR-100 dataset. When the number of parameters is not an issue, DualConv increases the accuracy of MobileNetV1 by 4.11% on the same dataset. Furthermore, DualConv significantly improves the YOLO-V3 object detection speed and improves its accuracy by 4.4% on PASCAL visual object classes (VOC) dataset.
Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge

Natural language processing (NLP) aims at investigating the interactions between agents and humans, processing and analyzing large amounts of natural language data. Large-scale language models play an important role in current natural language processing. However, the challenges of explainability and complexity come along with the developments of language models. One way is to introduce logical relations and rules into natural language processing models, such as making use of Automated Planning. Automated planning (AI planning) focuses on building symbolic domain models and synthesizing plans to transit initial states to goals based on domain models. Recently, there have been plenty of works related to these two fields, which have the abilities to generate explicit knowledge, e.g., preconditions and effects of action models, and learn from tacit knowledge, e.g., neural models, respectively. Integrating AI planning and natural language processing effectively improves the communication between human and intelligent agents. This paper outlines the commons and relations between AI planning and natural language processing, argues that each of them can effectively impact on the other one by five areas: (1) planning-based text understanding, (2) planning-based natural language processing, (3) planning-based explainability, (4) text-based human-robot interaction, and (5) applications. We also explore some potential future issues between AI planning and natural language processing. To the best of our knowledge, this survey is the first work that addresses the deep connections between AI planning and Natural language processing.
Optimizing quantum circuits with Riemannian gradient flow

Variational quantum algorithms are a promising class of algorithms that can be performed on currently available quantum computers. In most settings, the free parameters of a variational circuit are optimized using a classical optimizer that updates parameters in Euclidean geometry. Since quantum circuits are elements of the special unitary group, we can consider an alternative optimization perspective that depends on the structure of this group. In this work, we investigate a Riemannian optimization scheme over the special unitary group and we discuss its implementation on a quantum computer. We illustrate that the resulting Riemannian gradient-flow algorithm has favorable optimization properties for deep circuits and that an approximate version of this algorithm can be performed on near-term hardware.
Fast Inverter Control by Learning the OPF Mapping Using Sensitivity-Informed Gaussian Processes

Fast inverter control is a desideratum towards the smoother integration of renewables. Adjusting inverter injection setpoints for distributed energy resources can be an effective grid control mechanism. However, finding such setpoints optimally requires solving an optimal power flow (OPF), which can be computationally taxing in real time. Previous works have proposed learning the mapping from grid conditions to OPF minimizers using Gaussian processes (GPs). This GP-OPF model predicts inverter setpoints when presented with a new instance of grid conditions. Training enjoys closed-form expressions, and GP-OPF predictions come with confidence intervals. To improve upon data efficiency, we uniquely incorporate the sensitivities (partial derivatives) of the OPF mapping into GP-OPF. This expedites the process of generating a training dataset as fewer OPF instances need to be solved to attain the same accuracy. To further reduce computational efficiency, we approximate the kernel function of GP-OPF leveraging the concept of random features, which is neatly extended to sensitivity data. We perform sensitivity analysis for the second-order cone program (SOCP) relaxation of the OPF, whose sensitivities can be computed by merely solving a system of linear equations. Extensive numerical tests using real-world data on the IEEE 13- and 123-bus feeders corroborate the merits of GP-OPF.
Exhaustivity and anti-exhaustivity in the RSA framework: Testing the effect of prior beliefs

During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs. In this paper, we focus on the influence of prior beliefs on so-called exhaustivity interpretations, whereby a sentence such as Mary came is understood to mean that only Mary came. Two theoretical origins for exhaustivity effects have been proposed in the previous literature. On the one hand are perspectives that view these inferences as the result of a purely pragmatic process (as in the classical Gricean view, and more recent Bayesian approaches); on the other hand are proposals that treat them as the result of an encapsulated semantic mechanism (Chierchia, Fox & Spector 2012). We gain traction on adjudicating between these two approaches with new theoretical and experimental evidence, focusing on the behavior of different models for exhaustivity effects, all of which fit under the Rational Speech Act modeling framework (RSA, Frank & Goodman, 2012). Some (but not all! ) of these models include an encapsulated semantic mechanism. Theoretically, we demonstrate that many RSA models predict not only exhaustivity, but also anti-exhaustivity, whereby "Mary came" would convey that Mary and someone else came. We evaluate these models against data obtained in a new study which tested the effects of prior beliefs on both production and comprehension, improving on previous empirical work. We find that the models which have the best fit to human behavior include an encapsulated exhaustivity mechanism. We conclude that, on the one hand, in the division of labor between semantics and pragmatics, semantics plays a larger role than is often thought, but, on the other hand, the tradeoff between informativity and cost which characterizes all RSA models does play a central role for genuine pragmatic effects.
QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms

This study focuses on designing and developing a mathematically based quadcopter rotational dynamics simulation framework for testing reinforcement learning (RL) algorithms in many flexible configurations. The design of the simulation framework aims to simulate both linear and nonlinear representations of a quadcopter by solving initial value problems for ordinary differential equation (ODE) systems. In addition, the simulation environment is capable of making the simulation deterministic/stochastic by adding random Gaussian noise in the forms of process and measurement noises. In order to ensure that the scope of this simulation environment is not limited only with our own RL algorithms, the simulation environment has been expanded to be compatible with the OpenAI Gym toolkit. The framework also supports multiprocessing capabilities to run simulation environments simultaneously in parallel. To test these capabilities, many state-of-the-art deep RL algorithms were trained in this simulation framework and the results were compared in detail. Keywords—Quadcopter Rotational Dynamics, Simulation Frameworks, Reinforcement Learning, Control Algorithms, UAVs.
Judging a Socially Assistive Robot by Its Cover: The Effect of Body Structure, Outline, and Color on Users’ Perception

Socially assistive robots (SARs) aim to provide assistance through social interaction. Previous studies contributed to understanding users’ perceptions and preferences regarding existing commercially available SARs. Yet very few studies regarding SARs’ appearance used designated SAR designs, and even fewer evaluated isolated visual qualities (VQs). In this work, we aim to assess the effect of isolated VQs systematically. To achieve this, we first conducted market survey and deconstructed the VQs attributed to SARs. Then, a reconstruction of body structure, outline, and color scheme was done, resulting in the creation of 30 new SAR models that differ in their VQs, allowing us to isolate one character at a time. We used these new designs to evaluate users’ preferences and perceptions in two empirical studies. Our empirical findings link VQs with perceptions of SAR characteristics. These can lead to forming guidelines for the industrial design processes of new SARs to match user expectations.
The case of itinerant magnetism in CaMn2Al10: self-consistent renormalisation (SCR) theory study

We have applied the powerful self-consistent renormalization theory of spin fluctuations for the system CaMn2Al10 discovered in 2015 and was conjectured to be an itinerant magnet. We have calculated the inverse static i.e. (paramagnetic) susceptibility and have compared it with the experimental data (Steinke et al 2015 Phys. Rev. B 92 020413). The agreement is very good. We have calculated spin fluctuations at various temperatures and have also estimated the strength of the electronic correlation i.e. (U = 0.3136 eV) in the Hubbard Hamiltonian. Based on our quantitative explanation of the inverse static i.e. (paramagnetic) susceptibility data within the framework of self-consistent renormalization theory, we can decisively conclude CaMn2Al10 exhibits the phenomena of itinerant magnetism. Further, our density functional theory (DFT) and DFT + U calculations corroborate the strong Mn-Al hybridization which is the key to the itinerant magnetism in this system. Our estimated correlations strength will provide a foundation for further studies of itinerant magnetism in this system.
Scrambling Dynamics and Out-of-Time-Ordered Correlators in Quantum Many-Body Systems

This tutorial article introduces the physics of quantum information scrambling in quantum many-body systems. The goals are to understand how to precisely quantify the spreading of quantum information and how causality emerges in complex quantum systems. We introduce a general framework to study the dynamics of quantum information, including detection and decoding. We show that the dynamics of quantum information is closely related to operator dynamics in the Heisenberg picture, and, under certain circumstances, can be precisely quantified by the so-called out-of-time ordered correlator~(OTOC). The general behavior of OTOC is discussed based on several toy models, including the Sachdev-Ye-Kitaev model, random circuit models, and Brownian models, in which OTOC is analytically tractable. We introduce numerical methods, including exact diagonalization and tensor network methods, to calculate OTOC for generic quantum many-body systems. We also survey current experimental schemes for measuring OTOC in various quantum simulators.
LiDiTE: A Full-Fledged and Featherweight Digital Twin Framework

The rising of the Cyber-Physical System (CPS) and the Industry 4.0 paradigms demands the design and implementation of Digital Twin Frameworks (DTFs) that may support the quick build of reliable Digital Twins (DTs) for experimental and testing purposes. Most of the current DTF proposals allow the generation of DTs at a good pace but affect generality, scalability, portability, and completeness. As a consequence, current DTF are mostly domain-specific and hardly span several application domains (e.g., from simple IoT deployments to the modeling of complex critical infrastructures). Furthermore, the generated DTs often requires a high amount of computational resource to run. In this paper, we present LiDiTE, a solution based on a novel reference model for general-purpose DTFs. LiDiTE overcomes the limitations of state-of-the-art tools by supporting the fine-grained development of real-world complexity scenarios. To achieve that, LiDiTE builds on technologies that favor scalability, reuse, and extensibility of scenarios. We show such features by building the DT of real critical infrastructure and evaluating the performance of our DT against those of the real system. Further contributions of this paper include open access to the source code of LiDiTE and the experimental dataset.
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

The rising popularity of self-driving cars has led to the emergence of a new research field in recent years: Autonomous racing. Researchers are developing software and hardware for high-performance race vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic, and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods, and approaches used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field, we conclude with a summary of open research challenges that will guide future researchers in this field.
Information-Theoretic Analysis of Minimax Excess Risk

Two main concepts studied in machine learning theory are generalization gap (difference between train and test error) and excess risk (difference between test error and the minimum possible error). While information-theoretic tools have been used extensively to study the generalization gap of learning algorithms, the information-theoretic nature of excess risk has not yet been fully investigated. In this paper, some steps are taken toward this goal. We consider the frequentist problem of minimax excess risk as a zero-sum game between the algorithm designer and the world. Then, we argue that it is desirable to modify this game in a way that the order of play can be swapped. We then prove that, under some regularity conditions, if the world and designer can play randomly the duality gap is zero and the order of play can be changed. In this case, a Bayesian problem surfaces in the dual representation. This makes it possible to utilize recent information-theoretic results on minimum excess risk in Bayesian learning to provide bounds on the minimax excess risk. We demonstrate the applicability of the results by providing information theoretic insight on two important classes of problems: classification when the hypothesis space has finite VC-dimension, and regularized least squares.
Mass segregation in the hyades cluster

Using the Gaia colour-magnitude diagram, we assign masses to a catalogue of 979 confirmed members of the Hyades cluster and tails. By fitting the cumulative mass profile, stars within the tidal radius have a Plummer-like profile with half-mass radius rh of 5.75 pc. The tails are extended with rh = 69.35 pc and fall off more slowly than Plummer with density proportional to distance−1.36. The cluster stars are separated into two groups at BP-RP =2 or 0.56M⊙ to give a high mass (${\bar{M}} = 0.95 M_\odot$) and a low mass (${\bar{M}} = 0.32 M_\odot$) population. We show that: (i) the high mass population has a half-mass radius rh of 4.88 pc, whilst the low mass population has rh = 8.10 pc; (ii) despite the differences in spatial extent, the kinematics and binarity properties of the high and low mass populations are similar. They have isotropic velocity ellipsoids with mean 1d velocity dispersions σ of 0.427 and 0.415 km s−1 respectively. The dynamical state of the Hyades is far from energy equipartition ($\sigma \propto {\bar{M}}^{-1/2}$). We identify a new mass segregation instability for clusters with escape speed V. Populations with $V/\sigma \lesssim 2\sqrt{2}$ can never attain thermal equilibrium and equipartition. This regime encompasses many Galactic open and globular clusters. For the Hyades, there must be an outward energy flux of at least 9.5 × 10−4M⊙ km2 s−2Myr−1 to maintain its current configuration. The present mass loss of 0.26M⊙Myr−1 due to tidal stripping by itself implies a substantial energy flow beyond the required magnitude.
Multi-Scale STATCN: Self-Attention based Spatiotemporal Model for Short-Term Metro Origin-Destination Matrix Prediction

—Accurate prediction of short-term OD Matrix (i.e. the distribution of passenger ﬂows from various origins to destinations) is a crucial task in metro systems. It is highly challenging due to the constantly changing nature of spatiotemporal factors and the recent data collection problem. Recently, some deep learning-based models have been proposed for OD Matrix forecasting in ride-hailing or high way trafﬁc scenarios. However, these models can not sufﬁciently capture the complex spatiotemporal correlations among stations in metro networks due to different prior knowledge and contextual settings. In this paper, we propose a model, namely Multi-Scale STATCN, to address OD metro matrix prediction. Speciﬁcally, it ﬁrst proposes a data-driven method to solve the recent data collection problem. Then, it captures the dynamic spatial dependency in OD ﬂows among different stations by a global self-attention mechanism. Three temporal convolutional networks are leveraged to capture three temporal trends in OD ﬂow, i.e. recent trend, daily trend, weekly trend. Extensive experiments on three large-scale metro datasets demonstrate the superiority of our model over other competitors.
On Incorporating Inductive Biases into VAEs

We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into VAEs, and introduce a simple and effective alternative approach: Intermediary Latent Space VAEs(InteL-VAEs). InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es). This allows us to impose properties like sparsity or clustering on learned representations, and incorporate human knowledge into the generative model. Whereas changing the prior only indirectly encourages behavior through regularizing the encoder, InteL-VAEs are able to directly enforce desired characteristics. Moreover, they bypass the computation and encoder design issues caused by non-Gaussian priors, while allowing for additional flexibility through training of the parametric mapping function. We show that these advantages, in turn, lead to both better generative models and better representations being learned.