Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Automatic Speech Recognition Integrated with CTC-Based Voice Activity Detection

This paper integrates a voice activity detection (VAD) function with end-to-end automatic speech recognition toward an online speech interface and transcribing very long audio recordings. We focus on connectionist temporal classification (CTC) and its extension of CTC/attention architectures. As opposed to an attention-based architecture, input-synchronous label prediction can be performed based on a greedy search with the CTC (pre-)softmax output. This prediction includes consecutive long blank labels, which can be regarded as a non-speech region. We use the labels as a cue for detecting speech segments with simple thresholding. The threshold value is directly related to the length of a non-speech region, which is more intuitive and easier to control than conventional VAD hyperparameters. Experimental results on unsegmented data show that the proposed method outperformed the baseline methods using the conventional energy-based and neural-network-based VAD methods and achieved an RTF less than 0.2. The proposed method is publicly available.1
Deep Learning Based Spatial User Mapping on Extra Large MIMO Arrays

In an extra-large scale MIMO (XL-MIMO) system, the antenna arrays have a large physical size that goes beyond the dimensions in traditional MIMO systems. Because of this large dimensionality, the optimization of an XL-MIMO system leads to solutions with prohibitive complexity when relying on conventional optimization tools. In this paper, we propose a design based on machine learning for the downlink of a multi-user setting with linear pre-processing, where the goal is to select a limited mapping area per user, i.e. a small portion of the array that contains the beamforming energy to the user. We refer to this selection as spatial user mapping (SUM). Our solution relies on learning using deep convolutional neural networks with a distributed architecture that is built to manage the large system dimension. This architecture contains one network per user where all the networks work in parallel and exploit specific non-stationary properties of the channels along the array. Our results show that, once the parallel networks are trained, they provide the optimal SUM solution in more than $80\%$ of the instances, resulting in a negligible sum-rate loss compared to a system using the optimal SUM solution while providing an insightful approach to rethink these kinds of problems that have no closed-form solution.
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search

Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that most of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method to guide the search towards regions with promising actions, yielding better solutions at lower computational costs.
Quantum black holes in bootstrapped Newtonian gravity

We analyse the classical configurations of a bootstrapped Newtonian potential generated by homogeneous spherically symmetric sources in terms of a quantum coherent state. We first compute how the mass and mean wavelength of these solutions scale in terms of the number of quanta in the coherent state. We then note that the classical relation between the ADM mass and the proper mass of the source naturally gives rise to a Generalised Uncertainty Principle for the size of the gravitational radius in the quantum theory. Consistency of the mass and wavelength scalings with this GUP requires the compactness remains at most of order one even for black holes, and the corpuscular predictions are thus recovered, with the quantised horizon area expressed in terms of the number of quanta in the coherent state. Our findings could be useful for analysing the classicalization of gravity in the presence of matter and the avoidance of singularities in the gravitational collapse of compact sources.
Ultrafast functional magnetic resonance imaging reveals neuroplasticity-driven timing modulations

Functional Magnetic Resonance Imaging (fMRI) is predominantly harnessed for spatially mapping activation foci along distributed pathways. However, resolving dynamic information on activation sequence remains elusive. Here, we show an ultra-fast fMRI (ufMRI) approach - a facilitating non-invasive methodology for mapping Blood-Oxygenation-Level-Dependent (BOLD) response timings in distributed pathways with high spatiotemporal sensitivity and resolution. The mouse visual pathway was investigated under both normal and dark reared conditions. Results show that BOLD responses of normal reared mice preserve the neural input order from onset to peak times. However, modulatory effects in cortical responses due to dark rearing are only measurable at early response timings while a general delay in responses is measured at later timings. Our findings highlight the importance of robustly measuring early BOLD timings and pave the way for a better understanding and interpretation of functional MRI in healthy and aberrant conditions.
Multi-channel Acoustic Modeling using Mixed Bitrate OPUS Compression

Recent literature has shown that a learned front end with multi-channel audio input can outperform traditional beam-forming algorithms for automatic speech recognition (ASR). In this paper, we present our study on multi-channel acoustic modeling using OPUS compression with different bitrates for the different channels. We analyze the degradation in word error rate (WER) as a function of the audio encoding bitrate and show that the WER degrades by 12.6% relative with 16kpbs as compared to uncompressed audio. We show that its always preferable to have a multi-channel audio input over a single channel audio input given limited bandwidth. Our results show that for the best WER, when one of the two channels can be encoded with a bitrate higher than 32kbps, its optimal to encode the other channel with the highest bitrate possible. For bitrates lower than that, its preferable to distribute the bitrate equally between the two channels. We further show that by training the acoustic model on mixed bitrate input, up to 50% of the degradation can be recovered using a single model.
Bidirectional Trajectory Computation for Odometer-Aided Visual-Inertial SLAM

Odometer-aided visual-inertial SLAM systems typically have a good performance for navigation of wheeled platforms, while they usually suffer from degenerate cases before the first turning. In this paper, firstly we perform an observability analysis w.r. t. the extrinsic parameters before the first turning, which is a complement of the existing results of observability analyses. Secondly, inspired by the above observability analyses, we propose a bidirectional trajectory computation method, by which the poses before the first turning are refined in the backward computation thread, and the real-time trajectory is adjusted accordingly. Experimental results prove that our proposed method not only solves the problem of the unobservability of accelerometer bias and extrinsic parameters before the first turning, but also results in more accurate trajectories in comparison with the state-of-the-art approaches.
Finding large balanced subgraphs in signed networks

Signed networks are graphs whose edges are labelled with either a positive or a negative sign, and can be used to capture nuances in interactions that are missed by their unsigned counterparts. The concept of balance in signed graph theory determines whether a network can be partitioned into two perfectly opposing subsets, and is therefore useful for modelling phenomena such as the existence of polarized communities in social networks. While determining whether a graph is balanced is easy, finding a large balanced subgraph is hard. The few heuristics available in the literature for this purpose are either ineffective or non-scalable. In this paper we propose an efficient algorithm for finding large balanced subgraphs in signed networks. The algorithm relies on signed spectral theory and a novel bound for perturbations of the graph Laplacian. In a wide variety of experiments on real-world data we show that our algorithm can find balanced subgraphs much larger than those detected by existing methods, and in addition, it is faster. We test its scalability on graphs of up to 34 million edges.
On the Submodularity of Diffusion Models: Equivalent Conditions and Applications

The diffusion model has been a crucial component in studies about social networks. Many studies, especially these about influence maximization concern the proof of the submodularity of particular diffusion models. Such proofs have been model-dependent and are somewhat ad hoc. In this paper, we prove a theorem that provides a necessary and sufficient condition for a diffusion model to be submodular. This theorem can be used to justify the submodularity of an arbitrary diffusion model. We also apply this theorem to build a projection operator that maps an arbitrary diffusion model into a submodular one. Moreover, we use the established theorem to propose a diffusion model of multiple heterogeneous pieces of information that partially features submodularity.
Uncertainty Weighted Causal Graphs

Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for representing and infering new causal information. In previous works, we have generated automatically causal graphs associated to a given concept by analyzing sets of documents and extracting and representing the found causal information in that visual way. The retrieved information shows that causality is frequently imperfect rather than exact, feature gathered by the graph. In this work we will attempt to go a step further modelling the uncertainty in the graph through probabilistic improving the management of the imprecision in the quoted graph.
Quantifying Hypothesis Space Misspecification in Learning From Human–Robot Demonstrations and Physical Corrections

The human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses on how robots can use such inputs—such as, demonstrations or corrections—to learn intended objectives. These techniques assume that the human‘s desired objective already exists within the robot's hypothesis space. In reality, this assumption is often inaccurate: there will always be situations where the person might care about aspects of the task that the robot does not know about. Without this knowledge, the robot cannot infer the correct objective. Hence, when the robot's hypothesis space is misspecified, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. In this article, we posit that the robot should reason explicitly about how well it can explain human inputs given its hypothesis space and use that situational confidence to inform how it should incorporate the human input. We demonstrate our method on a 7 degrees-of-freedom robot manipulator in learning from two important types of human inputs: demonstrations of motion planning tasks and physical corrections during the robot's task execution.
Persistent spectral based machine learning (PerSpect ML) for drug design

In this paper, we propose persistent spectral based machine learning (PerSpect ML) models for drug design. Persistent spectral models, including persistent spectral graph, persistent spectral simplicial complex and persistent spectral hypergraph, are proposed based on spectral graph theory, spectral simplicial complex theory and spectral hypergraph theory, respectively. Different from all previous spectral models, a filtration process, as proposed in persistent homology, is introduced to generate multiscale spectral models. More specifically, from the filtration process, a series of nested topological representations, i,e. , graphs, simplicial complexes, and hypergraphs, can be systematically generated and their spectral information can be obtained. Persistent spectral variables are defined as the function of spectral variables over the filtration value. Mathematically, persistent multiplicity (of zero eigenvalues) is exactly the persistent Betti number (or Betti curve). We consider 11 persistent spectral variables and use them as the feature for machine learning models in protein-ligand binding affinity prediction. We systematically test our models on three most commonly-used databases, including PDBbind-2007, PDBbind-2013 and PDBbind-2016. Our results, for all these databases, are better than all existing models, as far as we know. This demonstrates the great power of our PerSpect ML in molecular data analysis and drug design.
Multipath Division Multiple Access for 5G Millimeter Wave Cellular Systems

Future 5G communication systems require more demanding performances than the existing cellular communication systems, e.g., 10 to 100 Mbps user data rate and much larger cellular spectrum efficiency. The well-used multiple access methods like CDMA and OFDMA are hard to achieve these challenging requirements simultaneously even with advanced signal processing techniques and base station cooperation. Recently, massive MIMO has gain much attention since it provides large signal dimensions that can be used to improve future 5G cellular system performance. This tutorial paper describes a recently proposed multiple access scheme called multipath division multiple access (MDMA) based on massive antennas and multipath channel characteristics in millimeter wave band, which offers uniform user data rate and achieves high cellular spectrum efficiency for 5G systems. We describe the fundamental principle and show the uplink and downlink block diagrams, control signaling and the call setup process. Besides, benefits of using MDMA as a multiple access scheme are discussed. Finally, practical concerns are addressed for future research directions.
An Automated Testing Framework For Smart TV apps Based on Model Separation

Smart TV application (app) is a new technological software app that can deal with smart TV devices to add more functionality and features. Despite its importance nowadays, far too little attention has been paid to present a systematic approach to test this kind of app so far. In this paper, we present a systematic model-based testing approach for smart TV app. We used our new notion of model separation to use sub-models based on the user preference instead of the exhaustive testing to generate the test cases. Based on the constructed model, we generated a set of test cases to assess the selected paths to the chosen destination in the app. We also defined new mutation operators for smart TV app to assess our testing approach. The evaluation results showed that our approach can generate more comprehensive models of smart TV apps with less time as compared to manual exploratory testing. The results also showed that our approach can generate effective test cases in term of fault detection.
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
Probabilistic Complexity Classes through Semantics

In a recent paper, the author has shown how Interaction Graphs models for linear logic can be used to obtain implicit characterisations of non-deterministic complexity classes. In this paper, we show how this semantic approach to Implicit Complexity Theory (ICC) can be used to characterise deterministic and probabilistic models of computation. In doing so, we obtain correspondences between group actions and both deterministic and probabilistic hierarchies of complexity classes. As a particular case, we provide the first implicit characterisations of the classes PLogspace (un-bounded error probabilistic logarithmic space) and PPtime (unbounded error probabilistic polynomial time)
Maximal single-frequency electromagnetic response

Modern nanophotonic and meta-optical devices utilize a tremendous number of structural degrees of freedom to enhance light--matter interactions. A fundamental question is how large such enhancements can be. We develop an analytical framework to derive upper bounds to single-frequency electromagnetic response, across near- and far-field regimes, for any materials, naturally incorporating the tandem effects of material- and radiation-induced losses. Our framework relies on a power-conservation law for the polarization fields induced in any scatterer. It unifies previous theories on optical scattering bounds and reveals new insight for optimal nanophotonic design, with applications including far-field scattering, near-field local-density-of-states engineering, optimal wavefront shaping, and the design of perfect absorbers. Our bounds predict strikingly large minimal thicknesses for arbitrarily patterned perfect absorbers, ranging from 50--100 nm for typical materials at visible wavelengths to micrometer-scale thicknesses for polar dielectrics at infrared wavelengths. We use inverse design to discover metasurface structures approaching the minimum-thickness perfect-absorber bounds.
Dynamical phase transitions for the activity biased Ising model in a magnetic field

We consider large deviations of the dynamical activity—defined as the total number of configuration changes within a time interval—for mean-field and one-dimensional Ising models, in the presence of a magnetic field. We identify several dynamical phase transitions that appear as singularities in the scaled cumulant generating function of the activity. In particular, we find low-activity ferromagnetic states and a novel high-activity phase, with associated first- and second-order phase transitions. The high-activity phase has a negative susceptibility to the magnetic field. In the mean-field case, we analyse the dynamical phase coexistence that occurs on first-order transition lines, including the optimal-control forces that reproduce the relevant large deviations. In the one-dimensional model, we use exact diagonalisation and cloning methods to perform finite-size scaling of the first-order phase transition at non-zero magnetic field.
Effect of Top-Down Connections in Hierarchical Sparse Coding

Hierarchical sparse coding (HSC) is a powerful model to efficiently represent multidimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest interconnecting these subproblems as in predictive coding (PC) theory, which adds top-down connections between consecutive layers. In this study, we introduce a new model, 2-layer sparse predictive coding (2L-SPC), to assess the impact of this interlayer feedback connection. In particular, the 2L-SPC is compared with a hierarchical Lasso (Hi-La) network made out of a sequence of independent Lasso layers. The 2L-SPC and a 2-layer Hi-La networks are trained on four different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the 2L-SPC is faster to converge and generates a refined representation in the second layer compared to the Hi-La model. Third, we show that the 2L-SPC top-down connection accelerates the learning process of the HSC problem. Finally, the analysis of the emerging dictionaries shows that the 2L-SPC features are more generic and present a larger spatial extension.
Solving Billion-Scale Knapsack Problems

Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale. This paper examines KPs in a slightly generalized form and shows that they can be solved nearly optimally at scale via distributed algorithms. The proposed approach can be implemented fairly easily with off-the-shelf distributed computing frameworks (e.g. MPI, Hadoop, Spark). As an example, our implementation leads to one of the most efficient KP solvers known to date – capable to solve KPs at an unprecedented scale (e.g., KPs with 1 billion decision variables and 1 billion constraints can be solved within 1 hour). The system has been deployed to production and called on a daily basis, yielding significant business impacts at Ant Financial.