Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-based conditioning methods using variable frame rate for style-robust speaker verification

We propose an approach to extract speaker embeddings that are robust to speaking style variations in text-independent speaker verification. Typically, speaker embedding extraction includes training a DNN for speaker classification and using the bottleneck features as speaker representations. Such a network has a pooling layer to transform frame-level to utterance-level features by calculating statistics over all utterance frames, with equal weighting. However, self-attentive embeddings perform weighted pooling such that the weights correspond to the importance of the frames in a speaker classification task. Entropy can capture acoustic variability due to speaking style variations. Hence, an entropy-based variable frame rate vector is proposed as an external conditioning vector for the self-attention layer to provide the network with information that can address style effects. This work explores five different approaches to conditioning. The best conditioning approach, concatenation with gating, provided statistically significant improvements over the x-vector baseline in 12/23 tasks and was the same as the baseline in 11/23 tasks when using the UCLA speaker variability database. It also significantly outperformed self-attention without conditioning in 9/23 tasks and was worse in 1/23. The method also showed significant improvements in multi-speaker scenarios of SITW.
On the Calculation of the Variance of Algebraic Variables in Power System Dynamic Models With Stochastic Processes

This letter presents a technique to calculate the variance of algebraic variables of power system models represented as a set of stochastic differential-algebraic equations. The technique utilizes the solution of a Lyapunov equation and requires the calculation of the state matrix of the system. The IEEE 14-bus system serves to demonstrate the accuracy of the proposed technique over a wide range of variances of stochastic processes. The accuracy is evaluated by comparing the results with those obtained with Monte Carlo time domain simulations. Finally, a case study based on a 1479-bus dynamic model of the all-island Irish transmission system shows the computational efficiency of the proposed approach compared to the Monte Carlo method.
Deep Neural Networks pruning via the Structured Perspective Regularization

In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, and therefore a large amount of parameters, which makes training, storage and inference expensive. This motivated a stream of research about compressing the original networks into smaller ones without excessively sacrificing performances. Among the many proposed compression approaches, one of the most popular is \emph{pruning}, whereby entire elements of the ANN (links, nodes, channels, \ldots) and the corresponding weights are deleted. Since the nature of the problem is inherently combinatorial (what elements to prune and what not), we propose a new pruning method based on Operational Research tools. We start from a natural Mixed-Integer-Programming model for the problem, and we use the Perspective Reformulation technique to strengthen its continuous relaxation. Projecting away the indicator variables from this reformulation yields a new regularization term, which we call the Structured Perspective Regularization, that leads to structured pruning of the initial architecture. We test our method on some ResNet architectures applied to CIFAR-10, CIFAR-100 and ImageNet datasets, obtaining competitive performances w.r. t. ~the state of the art for structured pruning.
Simplifying Dataflow Dialogue Design

In \citep{andreas2020task-oriented}, a dataflow (DF) based dialogue system was introduced, showing clear advantages compared to many commonly used current systems. This was accompanied by the release of SMCalFlow, a practically relevant, manually annotated dataset, more detailed and much larger than any comparable dialogue dataset. Despite these remarkable contributions, the community has not shown further interest in this direction. What are the reasons for this lack of interest? And how can the community be encouraged to engage in research in this direction? One explanation may be the perception that this approach is too complex - both the the annotation and the system. This paper argues that this perception is wrong: 1) Suggestions for a simplified format for the annotation of the dataset are presented, 2) An implementation of the DF execution engine is released\footnote{https://github.com/telepathylabsai/OpenDF}, which can serve as a sandbox allowing researchers to easily implement, and experiment with, new DF dialogue designs. The hope is that these contributions will help engage more practitioners in exploring new ideas and designs for DF based dialogue systems.
Evaluation of the performance of an optimized road surface/lighting combination

Taking into account the actual photometric characteristics of a road surface to design and then adjust a lighting installation is seldom done. In a lighting renovation, one of the Commission Internationale de l'Eclairage's standard r-tables is arbitrarily chosen although they are no longer representative of the characteristics of current road surfaces. The objective of the study is to assess an optimized, evolutive pavement and lighting combination, called Lumiroute®. To do this, two conventional designed sections were compared with two Lumiroute® sections. On-site measurements of road photometry, luminance and power consumption were conducted at regular intervals for three years. This paper presents the results of the photometric study together with an economic analysis. The Lumiroute® sections offer optimized performance and increased efficiency in comparison with the ordinary sections, particularly with regard to light and energy performance.
Learning Symmetric Rules with SATNet

SATNet is a differentiable constraint solver with a custom backpropagation algorithm, which can be used as a layer in a deep-learning system. It is a promising proposal for bridging deep learning and logical reasoning. In fact, SATNet has been successfully applied to learn, among others, the rules of a complex logical puzzle, such as Sudoku, just from input and output pairs where inputs are given as images. In this paper, we show how to improve the learning of SATNet by exploiting symmetries in the target rules of a given but unknown logical puzzle or more generally a logical formula. We present SymSATNet, a variant of SATNet that translates the given symmetries of the target rules to a condition on the parameters of SATNet and requires that the parameters should have a particular parametric form that guarantees the condition. The requirement dramatically reduces the number of parameters to learn for the rules with enough symmetries, and makes the parameter learning of SymSATNet much easier than that of SATNet. We also describe a technique for automatically discovering symmetries of the target rules from examples. Our experiments with Sudoku and Rubik's cube show the substantial improvement of SymSATNet over the baseline SATNet.
Advances in Structural Systems for Tall Buildings: Emerging Developments for Contemporary Urban Giants

New developments of tall buildings of ever-growing heights have been continuously taking place worldwide. Consequently, many innovations in structural systems have emerged. This paper presents a retrospective survey of the main structural systems for tall buildings with emphasis on the advancements of recent, emerging, and potentially emerging systems. A structural systems chart that was previously developed by the authors has been updated in this paper to recognize, categorize and add the more recent structural systems. Recent trends of tubular structural systems in modified forms including the braced megatubes and diagrids are presented. Core-outrigger structural systems are discussed with emphasis on their adaptability. The potential of employing superframes for stand-alone and conjoined megatall buildings is predicted. As a means to solve today’s various project-specific complex design requirements, different mixed structural systems for supertall and megatall buildings are presented. This paper also discusses the widespread application of composite structural systems and recent trends of concrete cores for contemporary tall buildings. Finally, the future of tall buildings is predicted as the race for height continues.
Analysis of Individual Conversational Volatility in Tandem Telecollaboration for Second Language Learning

Second language (L2) learning can be enabled by tandem collaboration where students are grouped in video conference calls while learning the native language of other student(s) on the calls. This places students in an online environment where the more outgoing can actively contribute and engage in dialogue while those more shy and unsure of their second language language skills can sit back and coast through the calls. We have built and deployed the L2L system which records timings of conversational utterances from all participants in a call. We generate visualisations including participation rates and timelines for each student in each call and present these on a dashboard. Students can self-reflect and perhaps target improving their levels of engagement for subsequent calls. We have recently developed a measure called personal conversational volatility for how dynamic has been each student’s contribution to the dialogue in each call. This measures whether a student’s contribution was interactive with a mixture of interjections perhaps interrupting and agreeing with others combined with longer contributions, or whether it consisted of regular duration contributions with not much mixing. We present an analysis of conversational volatility measures of a sample of 19 individual English-speaking students from our University at lower intermediate-intermediate level (B1/B2) in their target language which was French, in each of 86 tandem telecollaboration calls over one teaching semester. Our analysis shows that students varied considerably in how their individual levels of engagement changed as their telecollaboration meetings progressed. Some students got more involved in the dialogue from one meeting to the next while others did not change their interaction levels at all. The reasons for this are not clear from the data we have and point to a need for further investigation into the nature of online tandem telecollaboration meetings. In particular there is a need to look into the nature of the interactions and see if the choices of discussion topics were too difficult for some lower intermediate students and that may have influenced their engagement in some way.
Towards a Grounded Theory of Causation for Embodied AI

There exist well-developed frameworks for causal modelling, but these require rather a lot of human domain expertise to define causal variables and perform interventions. In order to enable autonomous agents to learn abstract causal models through interactive experience, the existing theoretical foundations need to be extended and clarified. Existing frameworks give no guidance regarding variable choice / representation, and more importantly, give no indication as to which behaviour policies or physical transformations of state space shall count as interventions. The framework sketched in this paper describes actions as transformations of state space, for instance induced by an agent running a policy. This makes it possible to describe in a uniform way both transformations of the micro-state space and abstract models thereof, and say when the latter is veridical / grounded / natural. We then introduce (causal) variables, define a mechanism as an invariant predictor, and say when an action can be viewed as a ``surgical intervention'', thus bringing the objective of causal representation \&intervention skill learning into clearer focus.
Young, Blue, and Isolated Stellar Systems in the Virgo Cluster. I. 2D Optical Spectroscopy

We use panoramic optical spectroscopy obtained with the Very Large Telescope/MUSE to investigate the nature of five candidate extremely isolated low-mass star-forming regions (Blue Candidates; hereafter, BCs) toward the Virgo cluster of galaxies. Four of the five (BC1, BC3, BC4, and BC5) are found to host several H ii regions and to have radial velocities fully compatible with being part of the Virgo cluster. All the confirmed candidates have mean metallicity significantly in excess of that expected from their stellar mass, indicating that they originated from gas stripped from larger galaxies. In summary, these four candidates share the properties of the prototype system SECCO 1, suggesting the possible emergence of a new class of stellar systems, intimately linked to the complex duty cycle of gas within clusters of galaxies. A thorough discussion of the nature and evolution of these objects is presented in a companion paper, where the results obtained here from the MUSE data are complemented with Hubble Space Telescope (optical) and Very Large Array (Hi) observations.
Jordan Decompositions of Tensors

We expand on an idea of Vinberg to take a tensor space and the natural Lie algebra that acts on it and embed their direct sum into an auxiliary algebra. Viewed as endomorphisms of this algebra, we associate adjoint operators to tensors. We show that the group actions on the tensor space and on the adjoint operators are consistent, which endows the tensor with a Jordan decomposition. We show that there is an essentially unique algebra structure that preserves the tensor structure and has a meaningful Jordan decomposition. We utilize aspects of the Jordan decomposition to study orbit separation and classification in examples relevant to tensor decomposition and quantum information.
Zero-Shot Building Control

Heating and cooling systems in buildings account for 31% of global energy use, much of which are regulated by Rule Based Controllers (RBCs) that neither maximise energy efﬁciency nor minimise emissions by interacting optimally with the grid. Control via Reinforcement Learning (RL) has been shown to signiﬁcantly improve building energy efﬁciency, but existing solutions require pre-training in simulators that are prohibitively expensive to obtain for every building in the world. In response, we show it is possible to perform safe, zero-shot control of buildings by combining ideas from system identiﬁcation and model-based RL. We call this combination PEARL (Probabilistic Emission-Abating Reinforcement Learning) and show it reduces emissions without pre-training, needing only a three hour commissioning period. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort.
Classification of ADHD Patients Using Kernel Hierarchical Extreme Learning Machine

Recently, the application of deep learning models to diagnose neuropsychiatric diseases from brain imaging data has received more and more attention. However, in practice, exploring interactions in brain functional connectivity based on operational magnetic resonance imaging data is critical for studying mental illness. Since Attention-Deficit and Hyperactivity Disorder (ADHD) is a type of chronic disease that is very difficult to diagnose in the early stages, it is necessary to improve the diagnosis accuracy of such illness using machine learning models treating patients before the critical condition. In this study, we utilize the dynamics of brain functional connectivity to model features from medical imaging data, which can extract the differences in brain function interactions between Normal Control (NC) and ADHD. To meet that requirement, we employ the Bayesian connectivity change-point model to detect brain dynamics using the local binary encoding approach and kernel hierarchical extreme learning machine for classifying features. To verify our model, we experimented with it on several real-world children's datasets, and our results achieved superior classification rates compared to the state-of-the-art models.
Auditing Purchases without SKU using Text Description and Similarity Search

In this paper, we focus on the problem of auditing MRO (maintenance, repair, and operating) purchases which do not have SKU (Stock-Keep Unit) Those specific purchases not only lack SKU but also contain short text description, making the audit processes even more difficult. Our goal is comparing recently purchases with older ones using only the description provided by the purchase process. Therefore, evaluating price differences can uncover possible flaws during the acquiring phase. However, the text of the items that we were working on was very small, with numbers due to the nature of the products and we have a limited amount of time to develop the solution which was 8 weeks. As result, we showed that working using a well-oriented methodology we were able to deliver a successful MVP and achieve the results expected for the client, extended its search database by 30%, and allowed it to have a recovery of up to 20 million dollars.
Enhanced Internal Quantum Efficiency of Bandgap-Engineered Green W-Shaped Quantum Well Light-Emitting Diode

To improve the internal quantum efficiency of green light-emitting diodes, we present the numerical design and analysis of bandgap-engineered W-shaped quantum well. The numerical results suggest significant improvement in the internal quantum efficiency of the proposed W-LED. The improvement is associated with significantly improved hole confinement due to the localization of indium in the active region, leading to improved radiative recombination rate. In addition, the proposed device shows reduced defect-assisted Shockley-Read-Hall (SRH) recombination rate as well as Auger recombination rate. Moreover, the efficiency rolloff in the proposed device is associated with increased built-in electromechanical field.
On two-variable guarded fragment logic with expressive local Presburger constraints

We consider the extension of two-variable guarded fragment logic with local Presburger quantifiers. These are quantifiers that can express properties such as ``the number of incoming blue edges plus twice the number of outgoing red edges is at most three times the number of incoming green edges'' and captures various description logics with counting, but without constant symbols. We show that the satisfiability of this logic is EXP-complete. While the lower bound already holds for the standard two-variable guarded fragment logic, the upper bound is established by a novel, yet simple deterministic graph theoretic based algorithm.
Quantitative and Metric Rewriting: Abstract, Non-Expansive, and Graded Systems

Modern mathematics begins with symbolic manipulation. The central role of signs and symbols per se is one of the main achievement of the Medieval culture [88] leading, among others, to the development of elementary or symbolic algebra. Starting from the latter, the syntactic manipulation of symbols more or less independently of their meaning — i.e. to what symbols stand for — has become an essential part of mathematical reasoning, not to say of reasoning in general. Today, symbolic manipulation is not just a pillar of mathematics, but it is at the very hearth of computation. Indeed, the symbolic manipulations of elementary algebra carry a computational content and, vice versa, computational processes can be fully described symbolically. Rewriting theory [94, 25] is the discipline that studies (the computational content of) symbolic manipulation in general. As such, rewriting has its origin both in symbolic algebra as the study of the algorithmic properties of equational reasoning, and in computability and programming language theory, where rewriting systems have been used to define symbolic models of computation — such as the _-calculus [17] and combinatory logic [34, 74] — as well as the (operational) semantics and implementation of programming languages [7]. In both cases, rewriting is motivated by the need to define operational notions of equality revealing the computational content of equational deductions. Remarkably, operationality is ultimately achieved by making equality asymmetric, so that the aforementioned computational content can be fully uncovered by orienting equations. Nowadays, these oriented equations (and the evolution thereof) are known as rewriting — or reduction — relations. All of that highlights a crucial trait of rewriting theory, namely its deep connection with equational reasoning. In fact, rewriting does not actually focus on arbitrary symbolic transformations, but with equality-preserving ones: a rewriting relation refines equality by making the latter operational, and it is thus contained in it.
The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge

The goal of AVA challenge is to provide vision-based benchmarks and methods relevant to accessibility. In this paper, we introduce the technical details of our submission to the CVPR2022 AVA Challenge. Firstly, we conducted some experiments to help employ proper model and data augmentation strategy for this task. Secondly, an effective training strategy was applied to improve the performance. Thirdly, we integrated the results from two different segmentation frameworks to improve the performance further. Experimental results demonstrate that our approach can achieve a competitive result on the AVA test set. Finally, our approach achieves 63.008\%AP@0.50:0.95 on the test set of CVPR2022 AVA Challenge.
Compilation Forking: A Fast and Flexible Way of Generating Data for Compiler-Internal Machine Learning Tasks

Compiler optimization decisions are often based on hand-crafted heuristics centered around a few established benchmark suites. Alternatively, they can be learned from feature and performance data produced during compilation. However, data-driven compiler optimizations based on machine learning models require large sets of quality data for training in order to match or even outperform existing human-crafted heuristics. In static compilation setups, related work has addressed this problem with iterative compilation. However, a dynamic compiler may produce diﬀerent data depending on dynamically-chosen compilation strategies, which aggravates the generation of comparable data. We propose compilation forking , a technique for generating consistent feature and performance data from arbitrary, dynamically-compiled programs. Diﬀerent versions of program parts with the same proﬁling and compilation history are executed within single program runs to minimize noise stemming from dynamic compilation and the runtime environment. Our approach facilitates large-scale performance evaluations of compiler optimization decisions. Addition-ally, compilation forking supports creating domain-speciﬁc compilation strategies based on machine learning by providing the data for model training. We implemented compilation forking in the GraalVM compiler in a programming-language-agnostic way. To assess the quality of the generated data, we trained several machine learning models to replace compiler heuristics for loop-related optimizations. The trained models perform equally well to the highly-tuned compiler heuristics when comparing the geometric means of benchmark suite performances. Larger impacts on few single benchmarks range from speedups of 20% to slowdowns of 17%. The presented approach can be implemented in any dynamic compiler. We believe that it can help to analyze compilation decisions and leverage the use of machine learning into dynamic compilation.