Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition

Transformer based end-to-end modelling approaches with multiple stream inputs have been achieved great success in vari-ous automatic speech recognition (ASR) tasks. An important issue associated with such approaches is that the intermediate features derived from each stream might have similar representations and thus it is lacking of feature diversity, such as the descriptions related to speaker characteristics. To address this issue, this paper proposed a novel multi-level acoustic feature extraction framework that can be easily combined with Transformer based ASR models. The framework consists of two input streams: a shallow stream with high-resolution spectrograms and a deep stream with low-resolution spectrograms. The shallow stream is used to acquire traditional shallow features that is beneﬁcial for the classiﬁcation of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity. A feature correlation based fusion strategy is used to aggregate both features across the frequency and time domains and then fed into the Transformer encoder-decoder module. By using the proposed multi-level acoustic feature extraction framework, state-of-the-art word error rate of 21.7% and 2.5% were obtained on the HKUST Mandarin telephone and Librispeech speech recognition tasks respectively.
Flow Synthesis Based Visual Servoing Frameworks for Monocular Obstacle Avoidance Amidst High-Rises

We propose a novel flow synthesis based visual servoing framework enabling long-range obstacle avoidance for Micro Air Vehicles (MAV) flying amongst tall skyscrapers. Recent deep learning based frameworks use optical flow to do high-precision visual servoing. In this paper, we explore the question: can we design a surrogate flow for these high-precision visual-servoing methods, which leads to obstacle avoidance? We revisit the concept of saliency for identifying high-rise structures in/close to the line of attack amongst other competing skyscrapers and buildings as a collision obstacle. A synthesised flow is used to displace the salient object segmentation mask. This flow is so computed that the visual servoing controller maneuvers the MAV safely around the obstacle. In this approach, we use a multi-step Cross-Entropy Method (CEM) based servo control to achieve flow convergence, resulting in obstacle avoidance. We use this novel pipeline to successfully and persistently maneuver high-rises and reach the goal in simulated and photo-realistic real-world scenes. We conduct extensive experimentation and compare our approach with optical flow and short-range depth-based obstacle avoidance methods to demonstrate the proposed framework’s merit. Additional Visualisation can be found at https://sites.google. com/view/monocular-obstacle/home
Video-based Smoky Vehicle Detection with A Coarse-to-Fine Framework

Automatic smoky vehicle detection in videos is a superior solution to the traditional expensive remote sensing one with ultraviolet-infrared light devices for environmental protection agencies. However, it is challenging to distinguish vehicle smoke from shadow and wet regions coming from rear vehicle or clutter roads, and could be worse due to limited annotated data. In this paper, we first introduce a real-world large-scale smoky vehicle dataset with 75,000 annotated smoky vehicle images, facilitating the effective training of advanced deep learning models. To enable fair algorithm comparison, we also build a smoky vehicle video dataset including 163 long videos with segment-level annotations. Moreover, we present a new Coarse-to-fine Deep Smoky vehicle detection (CoDeS) framework for efficient smoky vehicle detection. The CoDeS first leverages a light-weight YOLO detector for fast smoke detection with high recall rate, and then applies a smoke-vehicle matching strategy to eliminate non-vehicle smoke, and finally uses a elaborately-designed 3D model to further refine the results in spatial temporal space. Extensive experiments in four metrics demonstrate that our framework is significantly superior to those hand-crafted feature based methods and recent advanced methods. The code and dataset will be released at https://github.com/pengxj/smokyvehicle.
Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles when learning from scratch. Our study closes this gap and showcases the potential of muscle actuators for core robotics challenges in terms of data-efficiency, hyperparameter sensitivity, and robustness.
Solving 2D and 3D Lattice Models of Correlated Fermions—Combining Matrix Product States with Mean-Field Theory

Correlated electron states are at the root of many important phenomena including unconventional superconductivity (USC), where electron-pairing arises from repulsive interactions. Computing the properties of correlated electrons, such as the critical temperature $T_c$ for the onset of USC, efficiently and reliably from the microscopic physics with quantitative methods remains a major challenge for almost all models and materials. In this theoretical work we combine matrix product states (MPS) with static mean field (MF) to provide a solution to this challenge for quasi-one-dimensional (Q1D) systems: Two- and three-dimensional (2D/3D) materials comprised of weakly coupled correlated 1D fermions. This MPS+MF framework for the ground state and thermal equilibrium properties of Q1D fermions is developed and validated for attractive Hubbard systems first, and further enhanced via analytical field theory. We then deploy it to compute $T_c$ for superconductivity in 3D arrays of weakly coupled, doped and repulsive Hubbard ladders. The MPS+MF framework thus enables the reliable, quantitative and unbiased study of USC and high-$T_c$ superconductivity - and potentially many more correlated phases - in fermionic Q1D systems from microscopic parameters, in ways inaccessible to previous methods. It opens the possibility of designing deliberately optimized Q1D superconductors, from experiments in ultracold gases to synthesizing new materials.
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems

Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasingly, ML approaches are widely adopted than heuristic approaches for cybersecurity because they learn directly from data. Data is critical for the development of ML systems, and becomes potential targets for attackers. Basically, data poisoning or contamination is one of the most common techniques used to fool ML models through data. This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation when developing novel models, especially for intrusion detection systems.
Power Response and Modelling Aspects of Power Electronic Loads in Case of Voltage Drops

—In this paper, the power response of power electronic loads in case of voltage drops are measured and their dynamics are analysed. Based on this, dynamic simulation models are derived which can be used for voltage stability investigations. For this, four loads with different power factor techniques are considered. In addition, the impact of the grid impedance and input ﬁlter on the power response are measured in the laboratory. Based on the measurements, the simulation models are described. It is also outlined under which aspects the components of the loads are normally dimensioned if no detailed information is available. A comparison with the measurements demonstrates that the simulation models capture the main dynamics. At the end of the paper, the load models are compared to a constant power load in a short-term voltage stability use case. The results indicate that the power electronic loads have a more positive inﬂuence on short-term voltage stability in case of voltage drops. Overall, the contributions of the paper are the identiﬁcation of the basic power dynamics of power electronic loads for different voltage drops and a subsequent derivation of suitable simulation load models for voltage stability investigations.
Memory-free Online Change-point Detection: A Novel Neural Network Approach

Change-point detection (CPD), which detects abrupt changes in the data distribution, is recognized as one of the most significant tasks in time series analysis. Despite the extensive literature on offline CPD, unsupervised online CPD still suffers from major challenges, including scalability, hyperparameter tuning, and learning constraints. To mitigate some of these challenges, in this paper, we propose a novel deep learning approach for unsupervised online CPD from multi-dimensional time series, named Adaptive LSTM-Autoencoder Change-Point Detection (ALACPD). ALACPD exploits an LSTM-autoencoder-based neural network to perform unsupervised online CPD. It continuously adapts to the incoming samples without keeping the previously received input, thus being memory-free. We perform an extensive evaluation on several real-world time series CPD benchmarks. We show that ALACPD, on average, ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation, and it is on par with the best performer in terms of the accuracy of the estimated change-points. The implementation of ALACPD is available online on Github\footnote{\url{https://github.com/zahraatashgahi/ALACPD}}.
Light-induced magnetization driven by interorbital charge motion in a spin-orbit assisted Mott insulator alpha-RuCl3

In a honeycomb-lattice spin-orbit assisted Mott insulator α -RuCl 3 , an ultrafast magnetization is induced by circularly polarized excitation below the Mott gap. Photo-carriers play an important role, which are generated by turning down the synergy of the on-site Coulomb interaction and the spin-orbit interaction realizing the insulator state. An ultrafast 6- fs measurement of photo-carrier dynamics and a quantum mechanical analysis clarify the mechanism, according to which the magnetization emerges from a coherent charge motion between different t 2g orbitals (d yz -d xz -d xy ) of Ru 3+ ions. This ultrafast magnetization is weakened in the antiferromagnetic (AF) phase, which is opposite to the general tendency that the inverse Faraday effect is larger in AF compounds than in paramagnetic ones. This temperature dependence indicates that the interorbital charge motion is affected by pseudo-spin rotational symmetry breaking in the AF phase.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process. Thereby, automating such process has become an active area of research and of interest in industrial applications. In this paper, a novel method, called Meta-learning and Causality Based Feature Engineering (MACFE), is proposed; our method is based on the use of meta-learning, feature distribution encoding, and causality feature selection. In MACFE, meta-learning is used to ﬁnd the best transformations, then the search is accelerated by pre-selecting “original” features given their causal relevance. Experimental evaluations on popular classiﬁcation datasets show that MACFE can improve the prediction performance across eight classiﬁers, outperforms the cur-rent state-of-the-art methods in average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works.
The Burning Number Conjecture Holds Asymptotically

The burning number b ( G ) of a graph G is the smallest number of turns required to burn all vertices of a graph if at every turn a new ﬁre is started and existing ﬁres spread to all adjacent vertices. The Burning Number Conjecture of Bonato et al. (2016) postulates that b ( G ) ≤ (cid:6) √ n (cid:7) for all graphs G on n vertices. We prove that this conjecture holds asymptotically, that is b ( G ) ≤ (1 + o (1)) √ n .
Online trajectory prediction for metropolitan scale mobility digital twin

Knowing "what is happening" and "what will happen" of the mobility in a city is the building block of a data-driven smart city system. In recent years, mobility digital twin that makes a virtual replication of human mobility and predicting or simulating the fine-grained movements of the subjects in a virtual space at a metropolitan scale in near real-time has shown its great potential in modern urban intelligent systems. However, few studies have provided practical solutions. The main difficulties are four-folds: 1) the daily variation of human mobility is hard to model and predict; 2) the transportation network enforces a complex constraints on human mobility; 3) generating a rational fine-grained human trajectory is challenging for existing machine learning models; and 4) making a fine-grained prediction incurs high computational costs, which is challenging for an online system. Bearing these difficulties in mind, in this paper we propose a two-stage human mobility predictor that stratifies the coarse and fine-grained level predictions. In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level. In the second stage, the coarse predictions are resolved to a fine-grained level via a probabilistic trajectory retrieval method, which offloads most of the heavy computations to the offline phase. We tested our method using a real-world mobile phone GPS dataset in the Kanto area in Japan, and achieved good prediction accuracy and a time efficiency of about 2 min in predicting future 1h movements of about 220K mobile phone users on a single machine to support more higher-level analysis of mobility prediction.
VidConv: A modernized 2D ConvNet for Efficient Video Recognition

Since being introduced in 2020, Vision Transformers (ViT) has been steadily breaking the record for many vision tasks and are often described as ``all-you-need"to replace ConvNet. Despite that, ViTs are generally computational, memory-consuming, and unfriendly for embedded devices. In addition, recent research shows that standard ConvNet if redesigned and trained appropriately can compete favorably with ViT in terms of accuracy and scalability. In this paper, we adopt the modernized structure of ConvNet to design a new backbone for action recognition. Particularly, our main target is to serve for industrial product deployment, such as FPGA boards in which only standard operations are supported. Therefore, our network simply consists of 2D convolutions, without using any 3D convolution, long-range attention plugin, or Transformer blocks. While being trained with much fewer epochs (5x-10x), our backbone surpasses the methods using (2+1)D and 3D convolution, and achieve comparable results with ViT on two benchmark datasets.
Exact Wavelike Solutions in General Relativity

We conduct a review of the basic definitions and the principle results in the study of wavelike spacetimes, that is spacetimes whose metric models massless radiation moving at the speed of light. In particular, we motivate and connect their various definitions, outline their coordinate descriptions and present some classical results in their study in a language more accessible to modern readers, including the existence of “null coordinates” and the construction of Penrose limits. We also present a thorough summary of recent work on causality in pp-waves, and progress in addressing an open question in the field – the Ehlers– Kundt conjecture. ∗Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA †Department of Mathematics, Eberhard Karls Universität Tübingen, Germany ‡Department of Mathematics, Clark University, Worcester, MA, USA Corresponding author: Cian Roche, roche@mit.edu ar X iv :2 20 7. 03 59 1v 1 [ gr -q c] 7 J ul 2 02 2
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base

Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query. Despite the strong causal effects between NE and GC, previous works fail to directly model such causalities in their pipeline, hindering the learning of subtask correlations. Also, the sequence-generation process for GC in previous works induces ambiguity and exposure bias, which further harms accuracy. In this work, we formalize semantic parsing into two stages. In the first stage (graph structure generation), we propose a causal-enhanced table-filler to overcome the issues in sequence-modelling and to learn the internal causalities. In the second stage (relation extraction), an efficient beam-search algorithm is presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD 1.0 indicate that our method surpasses previous state-of-the-arts by a large margin (17%) while remaining time and space efficiency. The code and models are available at https://github.com/AOZMH/Crake.
Torsors for finite group schemes of bounded height

Let G$G$ be a nontrivial finite étale tame group scheme over a global field F$F$ . We define height functions on the set of G$G$ ‐torsors over F$F$ , which generalize the usual heights such as the discriminant. As an analogue of the Malle conjecture for group schemes, we formulate a conjecture on the asymptotic behavior of the number of G$G$ ‐torsors over F$F$ of bounded height. This is a special case of our more general “stacky Batyrev–Manin” conjecture [18]. The conjectured asymptotic is proven for the case when G$G$ is commutative. When F$F$ is a number field, the leading constant is expressed as a product of certain arithmetic invariants of G$G$ and the volume of a space attached to G$G$ . Moreover, an equidistribution property of G$G$ ‐torsors in the space is established.
TOWARDS EFFICIENT AND RELIABLE PREDICTION-BASED CONTROL USING EDMD

While Koopman-based techniques like extended Dynamic Mode Decomposition (eDMD) are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a ﬁnite dictionary and only ﬁnitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously avoiding the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and model predictive control with guarantees.
Ecological Analysis of Disturbance and Disturbance Intensity Estimates for Rainwater Harvesting

From an ecological perspective, the nature of rainwater harvesting with the characteristics of artificial disturbance is a manual disturbance. So it is very important to confirm the degree of ecological disturbance that occurs due to rainwater harvesting. Drawing from ecological disturbance theory, we can utilize both the distance method and dynamic curve method to estimate the level of ecological disturbance and degree of rainwater harvesting with a historical reference point value of the environment as a frame of reference, and with the 20 indicators such as surface runoff and evaluation index. Our examples show that the distance method and dynamic curve method are feasible for evaluating the ecological disturbance degree of rainwater harvesting. There are also research-scale effects in the study of ecological disturbance of rainwater harvesting that embody the comparability of different spatial and temporal ecological disturbance degrees. The comparability of ecological disturbance degrees of rainwater harvesting in an area at different scales or in different areas at the same scales is not strong, but it also generates some useful reference values. The ecological disturbance measurement in the same study area and at the same research-scale at different historical periods can be compared.
Trauma-informed approaches in primary healthcare and community mental healthcare: a mixed methods systematic review of system change interventions (TAPCARE study)

A trauma-informed approach is a framework for health system interventions that address the universal prevalence and impact of trauma. This mixed methods systematic review integrated evidence on the models, effects, and programme theories of trauma-informed primary healthcare and community mental healthcare. We searched five databases (January 1990-June 2021) and grey literature and consulted experts. The quantitative descriptive and qualitative framework syntheses were integrated through a line of argument and mapped onto a logic model. We included six studies that evaluated eight interventions with varied theoretical development, components, and outcomes. We found limited conflicting evidence for intervention effects on patient and provider psychological, behavioural, and health outcomes. Four studies reported improvement in provider psychological readiness and sense of community while three reported conflicting effects on provider behaviour. Four studies reported some improvement in patient readiness for disease management and access to services; however, the evidence for patient satisfaction was conflicting. Two studies found that patients and providers felt safe. While one study reported improvement in patient quality of life and chronic pain, another found nil effect on substance abuse, and three studies reported conflicting effects on mental health. Interventions worked as a whole package and through tailoring to the local context, staff education and self-care, safe environments, shared decision making. Intervention effects were moderated by contextual (health system values, policies, governance, business models, trauma-informed movement, organisational culture, social determinants of health) and intervention factors (buy-in from all staff, collective learning through conversations, equal attention to well-being of staff and patients, sustainable funding).