Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling

This paper presents a novel high-fidelity and low-latency universal neural vocoder framework based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling (MWDLP). MWDLP employs a coarse-fine bit WaveRNN architecture for 10-bit mu-law waveform modeling. A sparse gated recurrent unit with a relatively large size of hidden units is utilized, while the multiband modeling is deployed to achieve real-time low-latency usage. A novel technique for data-driven linear prediction (LP) with discrete waveform modeling is proposed, where the LP coefficients are estimated in a data-driven manner. Moreover, a novel loss function using short-time Fourier transform (STFT) for discrete waveform modeling with Gumbel approximation is also proposed. The experimental results demonstrate that the proposed MWDLP framework generates high-fidelity synthetic speech for seen and unseen speakers and/or language on 300 speakers training data including clean and noisy/reverberant conditions, where the number of training utterances is limited to 60 per speaker, while allowing for real-time low-latency processing using a single core of $\sim\! $ 2.1--2.7 GHz CPU with $\sim\! $ 0.57--0.64 real-time factor including input/output and feature extraction.
Nonlocal fully nonlinear double obstacle problems

We prove the existence and C regularity of solutions to nonlocal fully nonlinear elliptic double obstacle problems. We also obtain boundary regularity for these problems. The obstacles are assumed to be Lipschitz semi-concave/semi-convex functions, and we do not require them to be C. Our approach is to adapt a penalization method to be applicable to the setting of nonlocal equations and their viscosity solutions. Mathematics Subject Classification. 35R35, 47G20, 35B65.
Photocatalytic CO2 Reduction Under Continuous Flow High‐Purity Conditions: Quantitative Evaluation of CH4 Formation in the Steady‐State

In this study, the photocatalytic CO2 reduction on TiO2 P25 was investigated for the first time under high‐purity continuous flow conditions with gas chromatographic (GC) online detection of CH4 as the main product. The thorough photocatalytic cleaning procedure in humid helium prior to all measurements was conducted under continuous flow too and we were able to monitor the decrease of carbonaceous contaminant concentration. On addition of CO2 to the feed under illumination, an increase in CH4 concentration was observed, which clearly follows the increase in CO2 concentration in the reactor. It was also demonstrated that CH4 formation ceases as soon as the lamp is switched off, providing clear evidence that the formation of CH4 from CO2 is a photoinduced process. It was shown that higher CO2 concentration accelerated CH4 formation under steady‐state conditions up to a certain optimum. Higher CO2 concentrations lead to a decrease in CH4 formation. This observation is tentatively assigned to a limited availability of photogenerated charge carriers at the TiO2 surface, or a lack of suitable catalytically active sites. Traces of O2 in the reactor completely hinder CH4 formation, implying that the lack of concomitant oxygen evolution observed in previous measurements on TiO2 is likely beneficial for the overall process. This study represents a first step towards performing true steady‐state kinetic studies of photocatalytic CO2 reduction.
Probabilistic and Variational Recommendation Denoising

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendation. In this work, we propose probabilistic and variational recommendation denoising for implicit feedback. Through an empirical study, we find that different models make relatively similar predictions on clean examples which denote the real user preference, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose denoising with probabilistic inference (DPI) which aims to minimize the KL-divergence between the real user preference distributions parameterized by two recommendation models while maximize the likelihood of data observation. We then show that DPI recovers the evidence lower bound of an variational auto-encoder when the real user preference is considered as the latent variables. This leads to our second learning framework de-noising with variational auto-encoder (DVAE). We employ the
Low-Latency Real-Time Non-Parallel Voice Conversion based on Cyclic Variational Autoencoder and Multiband WaveRNN with Data-Driven Linear Prediction

This paper presents a low-latency real-time (LLRT) non-parallel voice conversion (VC) framework based on cyclic variational autoencoder (CycleVAE) and multiband WaveRNN with data-driven linear prediction (MWDLP). CycleVAE is a robust non-parallel multispeaker spectral model, which utilizes a speaker-independent latent space and a speaker-dependent code to generate reconstructed/converted spectral features given the spectral features of an input speaker. On the other hand, MWDLP is an efficient and a high-quality neural vocoder that can handle multispeaker data and generate speech waveform for LLRT applications with CPU. To accommodate LLRT constraint with CPU, we propose a novel CycleVAE framework that utilizes mel-spectrogram as spectral features and is built with a sparse network architecture. Further, to improve the modeling performance, we also propose a novel fine-tuning procedure that refines the frame-rate CycleVAE network by utilizing the waveform loss from the MWDLP network. The experimental results demonstrate that the proposed framework achieves high-performance VC, while allowing for LLRT usage with a single-core of $2.1$--$2.7$ GHz CPU on a real-time factor of $0.87$--$0.95$, including input/output, feature extraction, on a frame shift of $10$ ms, a window length of $27.5$ ms, and $2$ lookup frames.
KLUE: Korean Language Understanding Evaluation

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. We build all of the tasks from scratch from diverse source corpora while respecting copyrights, to ensure accessibility for anyone without any restrictions. With ethical considerations in mind, we carefully design annotation protocols. Along with the benchmark tasks and data, we provide suitable evaluation metrics and fine-tuning recipes for pretrained language models for each task. We furthermore release the pretrained language models (PLM), KLUE-BERT and KLUE-RoBERTa, to help reproducing baseline models on KLUE and thereby facilitate future research. We make a few interesting observations from the preliminary experiments using the proposed KLUE benchmark suite, already demonstrating the usefulness of this new benchmark suite. First, we find KLUE-RoBERTa-large outperforms other baselines, including multilingual PLMs and existing open-source Korean PLMs. Second, we see minimal degradation in performance even when we replace personally identifiable information from the pretraining corpus, suggesting that privacy and NLU capability are not at odds with each other. Lastly, we find that using BPE tokenization in combination with morpheme-level pre-tokenization is effective in tasks involving morpheme-level tagging, detection and generation. In addition to accelerating Korean NLP research, our comprehensive documentation on creating KLUE will facilitate creating similar resources for other languages in the future. KLUE is available at https://klue-benchmark.com.
Towards an Arrow-native Storage System

With the ever-increasing dataset sizes, several file formats like Parquet, ORC, and Avro have been developed to store data efficiently and to save network and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25-100 Gb/s and storage devices delivering 1, 000, 000 reqs/sec the CPU has become the bottleneck, trying to keep up feeding data in and out of these fast devices. The result is that data access libraries executed on single clients are often CPU-bound and cannot utilize the scale-out benefits of distributed storage systems. One attractive solution to this problem is to offload data-reducing processing and filtering tasks to the storage layer. However, modifying legacy storage systems to support compute offloading is often tedious and requires extensive understanding of the internals. Previous approaches re-implemented functionality of data processing frameworks and access library for a particular storage system, a duplication of effort that might have to be repeated for different storage systems. In this paper, we introduce a new design paradigm that allows extending programmable object storage systems to embed existing, widely used data processing frameworks and access libraries into the storage layer with minimal modifications. In this approach data processing frameworks and access libraries can evolve independently from storage systems while leveraging the scale-out and availability properties of distributed storage systems. We present one example implementation of our design paradigm using Ceph, Apache Arrow, and Parquet. We provide a brief performance evaluation of our implementation and discuss key results.
A Terabit Hybrid FPGA-ASIC Platform for Switch Virtualization

The roll-out of technologies like 5G and the need for multi-terabit bandwidth in backbone networks requires networking companies to make significant investments to keep up with growing service demands. For lower capital expenditure and faster time-to-market, companies can resort to anything-as-a-service providers to lease virtual resources. Nevertheless, existing virtualization technologies are still lagging behind next-generation networks’ requirements. This paper breaks the terabit barrier by introducing a hybrid FPGA-ASIC architecture to virtualize programmable forwarding planes. In contrast to existing solutions, our architecture involves an ASIC that multiplexes network flows between programmable virtual switches running in an FPGA capable of full and partial reconfiguration, enabling virtual switch hot-swapping. Our evaluation shows the feasibility of a switch virtualization architecture capable of achieving a combined throughput of 3.2 Tbps by having up to 26 virtual switch instances in parallel with low resource occupation overhead.
A User-Centered Interface for Enhanced Conjoined Human-Robot Actions in Industrial Tasks

—This paper presents a user–centered physical interface for collaborative mobile manipulators in industrial manufacturing and logistics applications. The proposed work builds on our earlier MOCA-MAN interface, through which a mobile manipulator could be physically coupled to the operators to assist them in performing daily activities. The new interface instead presents the following additions: i) A simplistic, industrial-like design that allows the worker to couple/decouple easily and to operate mobile manipulators locally; ii) Enhanced loco-manipulation capabilities that do not compromise the worker mobility. Besides, an experimental evaluation with six human subjects is carried out to analyze the enhanced locomotion and ﬂexibility of the proposed interface in terms of mobility constraint, usability, and physical load reduction.
Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine

herein, we have compared the performance of SVM and MLP in emotion recognition using speech and song channels of the RAVDESS dataset. We have undertaken a journey to extract various audio features, identify optimal scaling strategy and hyperparameter for our models. To increase sample size, we have performed audio data augmentation and addressed data imbalance using SMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of 82 compared to 75%. Following data augmentation, the performance of both algorithms was identical at ~79%, however, overfitting was evident for the SVM. Our final exploration indicated that the performance of both SVM and MLP were similar in which both resulted in lower accuracy for the speech channel compared to the song channel. Our findings suggest that both SVM and MLP are powerful classifiers for emotion recognition in a vocal-dependent manner.
Classifying concepts via visual properties

We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification. Based on this distinction, we introduce a general methodology for building lexico-semantic hierarchies of substance concepts, where nodes are annotated with the media, e.g.,videos or photos, from which substance concepts are extracted, and are associated with the corresponding classification concepts. The methodology is based on Ranganathan's original faceted approach, contextualized to the problem of classifying substance concepts. The key novelty is that the hierarchy is built exploiting the visual properties of substance concepts, while the linguistically defined properties of classification concepts are only used to describe substance concepts. The validity of the approach is exemplified by providing some highlights of an ongoing project whose goal is to build a large scale multimedia multilingual concept hierarchy.
Superpixel-based Domain-Knowledge Infusion in Computer Vision

Superpixels are higher-order perceptual groups of pixels in an image, often carrying much more information than raw pixels. There is an inherent relational structure to the relationship among different superpixels of an image. This relational information can convey some form of domain information about the image, e.g. relationship between superpixels representing two eyes in a cat image. Our interest in this paper is to construct computer vision models, specifically those based on Deep Neural Networks (DNNs) to incorporate these superpixels information. We propose a methodology to construct a hybrid model that leverages (a) Convolutional Neural Network (CNN) to deal with spatial information in an image, and (b) Graph Neural Network (GNN) to deal with relational superpixel information in the image. The proposed deep model is learned using a generic hybrid loss function that we call a `hybrid' loss. We evaluate the predictive performance of our proposed hybrid vision model on four popular image classification datasets: MNIST, FMNIST, CIFAR-10 and CIFAR-100. Moreover, we evaluate our method on three real-world classification tasks: COVID-19 X-Ray Detection, LFW Face Recognition, and SOCOFing Fingerprint Identification. The results demonstrate that the relational superpixel information provided via a GNN could improve the performance of standard CNN-based vision systems.
Determinants of some pentadiagonal matrices

In this paper we consider pentadiagonal (n+1)× (n+1)matrices with two subdiagonals and two superdiagonals at distances k and 2k from the main diagonal where 1 ≤ k < 2k ≤ n. We give an explicit formula for their determinants and also consider the Toeplitz and “imperfect” Toeplitz versions of such matrices. Imperfectness means that the first and last k elements of the main diagonal differ from the elements in the middle. Using the rearrangement due to Egerváry and Szász we also show how these determinants can be factorized.
M4Depth: A motion-based approach for monocular depth estimation on video sequences

Getting the distance to objects is crucial for autonomous vehicles. In instances where depth sensors cannot be used, this distance has to be estimated from RGB cameras. As opposed to cars, the task of estimating depth from on-board mounted cameras is made complex on drones because of the lack of constrains on motion during ﬂights. In this paper, we present a method to estimate the distance of objects seen by an on-board mounted camera by using its RGB video stream and drone motion information. Our method is built upon a pyramidal convolutional neural network architecture and uses time recurrence in pair with geometric constraints imposed by motion to produce pixel-wise depth maps. In our architecture, each level of the pyramid is designed to produce its own depth estimate based on past observations and information provided by the previous level in the pyramid. We introduce a spatial reprojection layer to maintain the spatio-temporal consistency of the data be-tween the levels. We analyse the performance of our approach on Mid-Air, a public drone dataset featuring synthetic drone trajectories recorded in a wide variety of un-structured outdoor environments. Our experiments show that our network outperforms state-of-the-art depth estimation methods and that the use of motion information is the main contributing factor for this improvement. The code of our method is publicly available on GitHub; see https: //github.com/michael-fonder/M4Depth
AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection

Recently, the anchor-free object detection model has shown great potential for accuracy and speed to exceed anchor-based object detection. Therefore, two issues are mainly studied in this article: (1) How to let the backbone network in the anchor-free object detection model learn feature extraction? (2) How to make better use of the feature pyramid network? In order to solve the above problems, Experiments show that our model has a certain improvement in accuracy compared with the current popular detection models on the COCO dataset, the designed attention mechanism module can capture contextual information well, improve detection accuracy, and use sepc network to help balance abstract and detailed information, and reduce the problem of semantic gap in the feature pyramid network. Whether it is anchor-based network model YOLOv3, Faster RCNN, or anchor-free network model Foveabox, FSAF, FCOS. Our optimal model can get 39.5% COCO AP under the background of ResNet50.
DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks

Over the past decade, many approaches have been introduced for traffic speed prediction. However, providing fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network where the size of the network keeps increasing and new traffic detectors are constantly deployed has not been well studied. To address this issue, this paper presents DistTune based on long short-term memory (LSTM) and the Nelder-Mead method. When encountering an unprocessed detector, DistTune decides if it should customize an LSTM model for this detector by comparing the detector with other processed detectors in the normalized traffic speed patterns they have observed. If a similarity is found, DistTune directly shares an existing LSTM model with this detector to achieve time-efficient processing. Otherwise, DistTune customizes an LSTM model for the detector to achieve fine-grained prediction. To make DistTune even more time-efficient, DisTune performs on a cluster of computing nodes in parallel. To achieve adaptive traffic speed prediction, DistTune also provides LSTM re-customization for detectors that suffer from unsatisfactory prediction accuracy due to, for instance, changes in traffic speed patterns. Extensive experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistTune. The results demonstrate that DistTune provides fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network.
Effect of Biologics on Cardiovascular Inflammation: Mechanistic Insights and Risk Reduction

Abstract It is increasingly recognized that atherosclerosis and consequently cardiovascular disease (CVD) are closely linked with inflammatory processes. The latter is in the center of the pathogenic mechanism underlying autoimmune rheumatic diseases (ARD). It follows then, that optimal control of inflammation in ARDs may lead to a decrease of the accompanied CVD risk. Major trials (eg, CANTOS, CIRT), aimed at examining the possible benefits of immunomodulatory treatments in CVD, demonstrated conflicting results. On the other hand, substantial evidence is accumulating about the possible beneficial effects of biologic disease modifying antirheumatic drugs (bDMARDs) in patients with ARDs, particularly those with rheumatoid arthritis (RA). It seems that bDMARDs (some more than others) alter the lipid profile in RA patients but do not adversely affect, in most cases, the TC/HDL ratio. Favorable effects are noted for arterial stiffness and endothelial function. This is reflected in the lower risk for CVD events, seen in observational studies of RA patients treated with bDMARDs. It should be stressed that more data exist for the TNF-inhibitors than for other bDMARDs, such as tocilizumab, abatacept and rituximab. As regards the spondyloarthropathies (SpA), data are less robust. For TNF-inhibitors, effects appear to be on par with those seen in RA but no conclusions can be drawn for newer biologic drugs used in SpA (eg, IL-17 blockers). Finally, there is accumulating evidence for a beneficial effect of immunosuppressive treatment in cardiac inflammation and function in several ARDs. Introduction of newer therapeutic options in clinical practice seem to have a positive impact on CVD in the setting of ARD. This is probably due to better control of inflammation, but direct improvement in vascular pathology is also a valid hypothesis. Most data are derived from observational studies and, therefore, randomized controlled trials are needed to assess the possible favorable effect of bDMARDs on CVD outcomes.