Miracle-dz/UniFAR-FTUs huggingface dataset:
InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt

Expressive text-to-speech (TTS) aims to synthesize different speaking style speech according to human's demands. Nowadays, there are two common ways to control speaking styles: (1) Pre-defining a group of speaking style and using categorical index to denote different speaking style. However, there are limitations in the diversity of expressiveness, as these models can only generate the pre-defined styles. (2) Using reference speech as style input, which results in a problem that the extracted style information is not intuitive or interpretable. In this study, we attempt to use natural language as style prompt to control the styles in the synthetic speech, e.g.,"Sigh tone in full of sad mood with some helpless feeling". Considering that there is no existing TTS corpus which is proper to benchmark this novel task, we first construct a speech corpus, whose speech samples are annotated with not only content transcriptions but also style descriptions in natural language. Then we propose an expressive TTS model, named as InstructTTS, which is novel in the sense of following aspects: (1) We fully take the advantage of self-supervised learning and cross-modal metric learning, and propose a novel three-stage training procedure to obtain a robust sentence embedding model, which can effectively capture semantic information from the style prompts and control the speaking style in the generated speech. (2) We propose to model acoustic features in discrete latent space and train a novel discrete diffusion probabilistic model to generate vector-quantized (VQ) acoustic tokens rather than the commonly-used mel spectrogram. (3) We jointly apply mutual information (MI) estimation and minimization during acoustic model training to minimize style-speaker and style-content MI, avoiding possible content and speaker information leakage from the style prompt.
Measurement-efficient quantum Krylov subspace diagonalisation

The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, their quantum generalisation can be much more powerful. However, quantum Krylov subspace algorithms are prone to errors due to inevitable statistical fluctuations in quantum measurements. To address this problem, we develop a general theoretical framework to analyse the statistical error and measurement cost. Based on the framework, we propose a quantum algorithm to construct the Hamiltonian-power Krylov subspace that can minimise the measurement cost. In our algorithm, the product of power and Gaussian functions of the Hamiltonian is expressed as an integral of the real-time evolution, such that it can be evaluated on a quantum computer. We compare our algorithm with other established quantum Krylov subspace algorithms in solving two prominent examples. It is shown that the measurement number in our algorithm is typically $10^4$ to $10^{12}$ times smaller than other algorithms. Such an improvement can be attributed to the reduced cost of composing projectors onto the ground state. These results show that our algorithm is exceptionally robust to statistical fluctuations and promising for practical applications.
Automated Time-frequency Domain Audio Crossfades using Graph Cuts

The problem of transitioning smoothly from one audio clip to another arises in many music consumption scenarios; especially as music consumption has moved from professionally curated and live-streamed radios to personal playback devices and services. Classically, transitioning from one song to another has been reliant on either pre-mixed transitions on recorded digital or physical media, hardware or software crossfading on the playback device, or professional transitions by a host or disk jockey (DJ). While options for software crossfading are ubiquitous on music streaming platforms and media players alike, these transitions pale in quality when compared to those manually applied by an audio engineer or DJ who can harmonically and rhythmically align tracks—and importantly—manually apply equalizer (EQ) ﬁlters during transitions. The application of EQ ﬁlters speciﬁcally allow for different transitions in different audio spectrums. For example, the bass register of one track can be made to replace the bass register of another track before transitioning the higher frequencies. Typically the task of deciding how and where to apply transitions in the frequency domain has been completed manually using a limited number of EQ ﬁlters. There
Support Exploration Algorithm for Sparse Support Recovery

We introduce a new algorithm promoting sparsity called {\it Support Exploration Algorithm (SEA)} and analyze it in the context of support recovery/model selection problems.The algorithm can be interpreted as an instance of the {\it straight-through estimator (STE)} applied to the resolution of a sparse linear inverse problem. SEA uses a non-sparse exploratory vector and makes it evolve in the input space to select the sparse support. We put to evidence an oracle update rule for the exploratory vector and consider the STE update. The theoretical analysis establishes general sufficient conditions of support recovery. The general conditions are specialized to the case where the matrix $A$ performing the linear measurements satisfies the {\it Restricted Isometry Property (RIP)}. Experiments show that SEA can efficiently improve the results of any algorithm. Because of its exploratory nature, SEA also performs remarkably well when the columns of $A$ are strongly coherent.
Fine Robotic Manipulation Without Force/Torque Sensor

Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis force/torque (F/T) sensor is installed between the robot's wrist and the end effector to measure the forces and torques exerted by the environment on the robot (the external wrench). While a typical 6-axis F/T sensor can offer highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot's internal signals are limited in scope. For instance, the estimation accuracy has mainly been validated in free-space motions and simple contacts, rather than tasks like assembly that require high-precision force control. In this letter, we present a Neural-Network-based solution to overcome these challenges. We offer a detailed discussion on model structure, training data categorization and collection, as well as fine-tuning strategies. These steps enable precise and reliable wrench estimations across a variety of scenarios. As an illustration, we demonstrate a pin insertion experiment with a 100-micron clearance and a hand-guiding experiment, both performed without external F/T sensors or joint torque sensors.
MOAT: Towards Safe BPF Kernel Extension

The Linux kernel makes considerable use of Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. BPF employs a verifier to statically check the security of user-supplied BPF code. Recent attacks show that BPF programs can evade security checks and gain unauthorized access to kernel memory, indicating that the verification process is not flawless. In this paper, we present MOAT, a system that isolates potentially malicious BPF programs using Intel Memory Protection Keys (MPK). Enforcing BPF program isolation with MPK is not straightforward; MOAT is carefully designed to alleviate technical obstacles, such as limited hardware keys and supporting a wide variety of kernel BPF helper functions. We have implemented MOAT in a prototype kernel module, and our evaluation shows that MOAT delivers low-cost isolation of BPF programs under various real-world usage scenarios, such as the isolation of a packet-forwarding BPF program for the memcached database with an average throughput loss of 6%.
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022

Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this task aims at detecting and classifying subtle movements from sport videos. We focus on recordings of table tennis matches. Conducted since 2019, this task provides a classification challenge from untrimmed videos recorded under natural conditions with known temporal boundaries for each stroke. Since 2021, the task also provides a stroke detection challenge from unannotated, untrimmed videos. This year, the training, validation, and test sets are enhanced to ensure that all strokes are represented in each dataset. The dataset is now similar to the one used in [1, 2]. This research is intended to build tools for coaches and athletes who want to further evaluate their sport performances.
BALANCE: Bayesian Linear Attribution for Root Cause Localization

Root Cause Analysis (RCA) plays an indispensable role in distributed data system maintenance and operations, as it bridges the gap between fault detection and system recovery. Existing works mainly study multidimensional localization or graph-based root cause localization. This paper opens up the possibilities of exploiting the recently developed framework of explainable AI (XAI) for the purpose of RCA. In particular, we propose BALANCE (BAyesian Linear AttributioN for root CausE localization), which formulates the problem of RCA through the lens of attribution in XAI and seeks to explain the anomalies in the target KPIs by the behavior of the candidate root causes. BALANCE consists of three innovative components. First, we propose a Bayesian multicollinear feature selection (BMFS) model to predict the target KPIs given the candidate root causes in a forward manner while promoting sparsity and concurrently paying attention to the correlation between the candidate root causes. Second, we introduce attribution analysis to compute the attribution score for each candidate in a backward manner. Third, we merge the estimated root causes related to each KPI if there are multiple KPIs. We extensively evaluate the proposed BALANCE method on one synthesis dataset as well as three real-world RCA tasks, that is, bad SQL localization, container fault localization, and fault type diagnosis for Exathlon. Results show that BALANCE outperforms the state-of-the-art (SOTA) methods in terms of accuracy with the least amount of running time, and achieves at least 6% notably higher accuracy than SOTA methods for real tasks. BALANCE has been deployed to production to tackle real-world RCA problems, and the online results further advocate its usage for real-time diagnosis in distributed data systems.
Angular correlations in nuclear reactions

Experiments involving the measurement of angular distributions and correlations of nuclear radiations fall into three groups: those involving simply conservation laws, studies of nuclear interactions and investigations of the properties of nuclear states. Elementary theoretical considerations and typical or important experimental results are presented with particular emphasis on the third group. When all the states involved are `pure' states with defined angular momentum and parity the correlations are always polynomials in even powers of cos θ only whose coefficients depend on the angular momenta of the states and the nature of the radiations. This condition of pure states is satisfied for all radioactive processes and generally for nuclear reactions at a resonance. Consequently it has been possible to deduce the angular momentum quantum number of a number of excited states from observed angular correlations between gamma-rays and alpha-rays, protons, and gamma-rays respectively. In non-resonant induced reactions a compound nucleus is frequently formed with so high an excitation that overlapping states of opposite parities contribute to the process. In this case odd powers of the cosine may appear and the angular distributions are not necessarily symmetrical about 90°. This is particularly true in reactions of the type (d, p) or (d, n) and, probably, (d, t) in which no true compound nucleus is formed at all. The distribution in these reactions is usually strongly forward.
Behavioural predictors of math anxiety

Math anxiety (MA) is a highly prevalent problem in education that has consistently shown to lead to poorer math performance. This study sought to investigate whether certain behaviours are predictive of MA among students. The study involved elementary school students ( n=124 ) who were low-progressing in math and is part of an educational intervention program to improve their mathematical abilities through a neural-informed math game. Ten classification types of behavioural indicators were identified, such as counting out loud. A multiple linear regression was conducted where students’ anxiety scores were regressed on these behavioural observations, along with their gender, mood, and math profile. Three behavioural observations were positively and significantly associated with their math anxiety. Implications and limitations of the study are discussed.
The Parkes selected regions: powerful radio galaxies and quasars at high redshifts

On a compile un echantillon complet de radiosources extragalactiques de densite de flux S≥0,1 Jy a 2,7 GHz pour tester la fonction de luminosite radio jusqu'aux deplacements vers le rouge z≃3-4. On donne les cartes VLA des 188 sources dans ce domaine des densites de flux
CMLCompiler: A Unified Compiler for Classical Machine Learning

Classical machine learning (CML) occupies nearly half of machine learning pipelines in production applications. Unfortunately, it fails to utilize the state-of-the-practice devices fully and performs poorly. Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues. This paper presents the design of a unified compiler, called CMLCompiler, for CML inference. We propose two unified abstractions: operator representations and extended computational graphs. The CMLCompiler framework performs the conversion and graph optimization based on two unified abstractions, then outputs an optimized computational graph to DL compilers or frameworks. We implement CMLCompiler on TVM. The evaluation shows CMLCompiler's portability and superior performance. It achieves up to 4.38× speedup on CPU, 3.31× speedup on GPU, and 5.09× speedup on IoT devices, compared to the state-of-the-art solutions --- scikit-learn, intel sklearn, and hummingbird. Our performance of CML and DL mixed pipelines achieves up to 3.04x speedup compared with cross-framework implementations. The project documents and source code are available at https://www.computercouncil. org/cmlcompiler.
Ion-Wave Instabilities in Mercury-Vapor Plasma

The Landau dispersion equation $2{k}^{2}={{k}_{\mathrm{De}}}^{2}{Z}^{\ensuremath{'}}({\ensuremath{\zeta}}_{e})+{{k}_{\mathrm{Di}}}^{2}{Z}^{\ensuremath{'}}({\ensuremath{\zeta}}_{i})$ is studied experimentally for $\ensuremath{\omega}={\ensuremath{\omega}}_{r}+i{\ensuremath{\omega}}_{i}$, where ${\ensuremath{\omega}}_{i}=0$ determines the stable-unstable boundary of ion waves, ${k}_{\mathrm{De}}$ and ${k}_{\mathrm{Di}}$ are the electron and ion Debye wave numbers, and $Z(\ensuremath{\zeta})$ is the plasma dispersion function. The decrease of the phase velocity with the frequency predicted by dispersion relation is observed for the spontaneously excited ion waves in the mercury-vapor discharge. The cutoff frequency beyond which no ion waves appear is also observed. The cutoff frequency increases with the electron drift velocity in the plasma. The dependence of the cutoff frequency on the electron drift is explained by the ion-wave instability in a two-Maxwellian-component plasma. One-way propagation of externally excited ion waves is also shown.
Near-perfect Reachability of Variational Quantum Search with Depth-1 Ansatz

Grover’s search algorithm is renowned for its dramatic speedup in solving many important scientific problems. The recently proposed Variational Quantum Search (VQS) algorithm has shown an exponential advantage over Grover’s algorithm for up to 26 qubits. However, its advantage for larger numbers of qubits has not yet been proven. Here we show that the exponentially deep circuit required by Grover’s algorithm can be replaced by a multi-controlled NOT gate together with either a single layer of R y gates or two layers of circuits consisting of Hadamard and NOT gates, which is valid for any number of qubits greater than five. We prove that the VQS, with a single layer of R y gates as its Ansatz, has near-perfect reachability in finding the good element of an arbitrarily large unstructured data set, and its reachability exponentially improves with the number of qubits, where the reachability is defined to quantify the ability of a given Ansatz to generate an optimal quantum state. Numerical studies further validate the excellent reachability of the VQS. Proving the near-perfect reachability of the VQS, with a depth-1 Ansatz, for any number of qubits completes an essential step in proving its exponential advantage over Grover’s algorithm for any number of qubits, and the latter proving is significant as it means that the VQS can efficiently solve NP-complete problems.
Improving Open-Domain Dialogue Evaluation with a Causal Inference Model

Effective evaluation methods remain a significant challenge for research on open-domain conversational dialogue systems. Explicit satisfaction ratings can be elicited from users, but users often do not provide ratings when asked, and those they give can be highly subjective. Post-hoc ratings by experts are an alternative, but these can be both expensive and complex to collect. Here, we explore the creation of automated methods for predicting both expert and user ratings of open-domain dialogues. We compare four different approaches. First, we train a baseline model using an end-to-end transformer to predict ratings directly from the raw dialogue text. The other three methods are variants of a two-stage approach in which we first extract interpretable features at the turn level that capture, among other aspects, user dialogue behaviors indicating contradiction, repetition, disinterest, compliments, or criticism. We project these features to the dialogue level and train a dialogue-level MLP regression model, a dialogue-level LSTM, and a novel causal inference model called counterfactual-LSTM (CF-LSTM) to predict ratings. The proposed CF-LSTM is a sequential model over turn-level features which predicts ratings using multiple regressors depending on hypotheses derived from the turn-level features. As a causal inference model, CF-LSTM aims to learn the underlying causes of a specific event, such as a low rating. We also bin the user ratings and perform classification experiments with all four models. In evaluation experiments on conversational data from the Alexa Prize SocialBot, we show that the CF-LSTM achieves the best performance for predicting dialogue ratings and classification.
Fisheye traffic data set of point center markers

This study presents an open data-market platform and a dataset containing 160,000 markers and 18,000 images. We hope that this dataset will bring more new data value and applications In this paper, we introduce the format and usage of the dataset, and we show a demonstration of deep learning vehicle detection trained by this dataset.
Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles

Modern-day autonomous vehicles are increasingly becoming complex multidisciplinary systems composed of mechanical, electrical, electronic, computing and information subsystems. Furthermore, the individual constituent technologies employed for developing autonomous vehicles have started maturing up to a point, where it seems beneficial to start looking at the synergistic integration of these components into sub-systems, systems, and potentially, system-of-systems. Hence, this work applies the principles of mechatronics approach of system design, verification and validation for the development of autonomous vehicles. Particularly, we discuss leveraging multidisciplinary co-design practices along with virtual, hybrid and physical prototyping and testing within a concurrent engineering framework to develop and validate a scaled autonomous vehicle using the AutoDRIVE Ecosystem. We also describe a case-study of autonomous parking application using a modular probabilistic framework to illustrate the benefits of the proposed approach.
Non-convex sampling for a mixture of locally smooth potentials

The purpose of this paper is to examine the sampling problem through Euler discretization, where the potential function is assumed to be a mixture of locally smooth distributions and weakly dissipative. We introduce α G -mixture locally smooth and α H -mixture locally Hessian smooth, which are novel and typically satisﬁed with a mixture of distributions. Under our conditions, we prove the convergence in Kullback-Leibler (KL) divergence with the number of iterations to reach ε -neighborhood of a target distribution in only polynomial dependence on the dimension. The convergence rate is improved when the potential is 1-smooth and α H -mixture locally Hessian smooth. Our result for the non-strongly convex outside the ball of radius R is obtained by convexifying the non-convex domains. In addition, we provide some nice theoretical properties of p -generalized Gaussian smoothing and prove the convergence in the L β -Wasserstein distance for stochastic gradients in a general setting.
SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search

Despite the popularity of policy gradient methods, they are known to suffer from large variance and high sample complexity. To mitigate this, we introduce SoftTreeMax – a generalization of softmax that takes planning into account. In SoftTreeMax, we extend the traditional logits with the multi-step discounted cumulative reward, topped with the logits of future states. We consider two variants of SoftTreeMax, one for cumulative reward and one for exponentiated reward. For both, we analyze the gradient variance and reveal for the ﬁrst time the role of a tree expansion policy in mitigating this variance. We prove that the resulting variance decays exponentially with the planning horizon as a function of the expansion policy. Speciﬁcally, we show that the closer the resulting state transitions are to uniform, the faster the decay. In a practical implementation, we utilize a parallelized GPU-based simulator for fast and efﬁcient tree search. Our differentiable tree-based policy leverages all gradients at the tree leaves in each environment step instead of the traditional single-sample-based gradient. We then show in simulation how the variance of the gradient is reduced by three orders of magnitude, leading to better sample complexity compared to the standard policy gradient. On Atari, SoftTreeMax demonstrates up to 5x better performance in a faster run time compared to distributed PPO. Lastly, we demonstrate that high reward correlates with lower variance.