Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Training of Vector Quantized Bottleneck Models

In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs). Discrete latent variable models have been shown to learn nontrivial representations of speech, applicable to unsupervised voice conversion and reaching state-of-the-art performance on unit discovery tasks. For unsupervised representation learning, they became viable alternatives to continuous latent variable models such as the Variational Auto-Encoder (VAE). However, training deep discrete variable models is challenging, due to the inherent non-differentiability of the discretization operation. In this paper we focus on VQ-VAE, a state-of-the-art discrete bottleneck model shown to perform on par with its continuous counterparts. It quantizes encoder outputs with on-line k-means clustering. We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs. We demonstrate that these can be successfully overcome by increasing the learning rate for the codebook and periodic date-dependent codeword re-initialization. As a result, we achieve more robust training across different tasks, and significantly increase the usage of latent codewords even for large codebooks. This has practical benefit, for instance, in unsupervised representation learning, where large codebooks may lead to disentanglement of latent representations.
Psychophysical identity and free energy

An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind–brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.
Low-Complexity and Low-Overhead Receiver for OTFS via Large-Scale Antenna Array

Orthogonal time frequency space (OTFS) is a modulation technique that is dedicated to the high-speed mobility scenario. However, its transmission involves a two-dimensional convolution of the symbols of interest and the multipath fading channel, and it complicates the equalization. In addition to the high-complexity issue, the existing pilot pattern to estimate the unknown channel accurately requires large overhead to avoid the pilot being contaminated, which is spectrally inefficient. In this paper, we propose a receiver design by the marriage of the OTFS and a large-scale antenna array, which allows low-complexity detection and low-overhead pilot pattern design. The receiver is briefly summarized as follows. First, the received signal from each path of the multipath fading channel is identified by a high-resolution spatial matched filter beamformer facilitated by a large-scale antenna array. Then, the derivation shows that the received signal from an angle of arrival can be regarded as a flat-faded signal with rotations in both the delay and the Doppler coordinates. In addition, the identified signal from each beamforming branch in the delay-Doppler domain can be simply equalized using the channel information estimated by our pilot pattern. We further provide the estimator of the channel fading and the rotations of delay and Doppler in each identified branch. With these estimates, the delay and the Doppler shift in each identified branch can be compensated, and then, the signals from all angles of arrival are combined as different diversity versions. Furthermore, the equalization complexity is reduced to 5.0% of the message passing detection and our pilot pattern with only around 25% overhead of the existing pilot pattern ensures the same protection of the pilot. Moreover, the carrier frequency offset is considered and be compensated. The significance of the proposed receiver is its practicality, and it achieves better error performance with lower receiver complexity and lower overhead compared to the existing approaches, at the cost of a linear beamforming antenna array. The price is quite affordable, since the linear antenna array has moderate computational complexity, and it is deployed widely in current and future wireless communication systems. Eventually, the efficiency, the reliability, and the low complicacy of the proposed receiver approach are further validated by the numerical results.
Assessment of Unconsciousness for Memory Consolidation Using EEG Signals

The assessment of consciousness and unconsciousness is a challenging issue in modern neuroscience. Consciousness is closely related to memory consolidation in that memory is a critical component of conscious experience. So far, many studies have been reported on memory consolidation during consciousness, but there is little research on memory consolidation during unconsciousness. Therefore, we aim to assess the unconsciousness in terms of memory consolidation using electroencephalogram signals. In particular, we used unconscious state during the nap; because sleep is the only state in which consciousness disappears under normal physiological conditions. Seven participants performed two memory tasks (word-pairs and visuo-spatial) before and after the nap to assess the memory consolidation during unconsciousness. As a result, spindle power in central, parietal, occipital regions during unconsciousness was positively correlated with the difference in location memory performance. With the difference in memory performance, there was also a negative correlation between delta connectivity and word-pairs memory, alpha connectivity and location memory, and spindle connectivity and word-pairs memory. Additionally, brain activity and connectivity for differences according to nap and unconsciousness during memory recall were explored. These findings could help present new insights into the assessment of unconsciousness by exploring the relationship with memory consolidation.
The Generalized Rainbow Turán Problem for Cycles

Given an edge-coloured graph, we say that a subgraph is rainbow if all of its edges have different colours. Let $\operatorname{ex}(n,H,$rainbow-$F)$ denote the maximal number of copies of $H$ that a properly edge-coloured graph on $n$ vertices can contain if it has no rainbow subgraph isomorphic to $F$. We determine the order of magnitude of $\operatorname{ex}(n,C_s,$rainbow-$C_t)$ for all $s,t$ with $s\not =3$. In particular, we answer a question of Gerbner, Meszaros, Methuku and Palmer by showing that $\operatorname{ex}(n,C_{2k},$rainbow-$C_{2k})$ is $\Theta(n^{k-1})$ if $k\geq 3$ and $\Theta(n^2)$ if $k=2$. We also determine the order of magnitude of $\operatorname{ex}(n,P_\ell,$rainbow-$C_{2k})$ for all $k,\ell\geq 2$, where $P_\ell$ denotes the path with $\ell$ edges.
Neural network interpretation using descrambler groups

Significance Artificial neural networks are famously opaque—it is often unclear how they work. In this communication, we propose a group-theoretical way of finding out. It reveals considerable internal sophistication, even in simple neural networks: our nets apparently invented an elegant digital filter, a regularized integral transform, and even Chebyshev polynomials. This is a step toward saving reductionism. For centuries, the philosophical approach to science has been to find fundamental laws that govern reality, to test those laws, and to use their predictive power. Black-box neural networks amount to blasphemy within that school, but they are irresistible because they “just work. ” Explaining how they work is a notoriously difficult problem, to which this paper offers a partial solution. The lack of interpretability and trust is a much-criticized feature of deep neural networks. In fully connected nets, the signaling between inner layers is scrambled because backpropagation training does not require perceptrons to be arranged in any particular order. The result is a black box; this problem is particularly severe in scientific computing and digital signal processing (DSP), where neural nets perform abstract mathematical transformations that do not reduce to features or concepts. We present here a group-theoretical procedure that attempts to bring inner-layer signaling into a human-readable form, the assumption being that this form exists and has identifiable and quantifiable features—for example, smoothness or locality. We applied the proposed method to DEERNet (a DSP network used in electron spin resonance) and managed to descramble it. We found considerable internal sophistication: the network spontaneously invents a bandpass filter, a notch filter, a frequency axis rescaling transformation, frequency-division multiplexing, group embedding, spectral filtering regularization, and a map from harmonic functions into Chebyshev polynomials—in 10 min of unattended training from a random initial guess.
Effect of Ionizing Radiation on the Quantification of Genetically Modified Foods

The rapid spread of Genetically Modified (GM) crops globally and the mandatory labeling of GM food and feed imposed by many countries has led to the development of relevant detection techniques. Polymerase Chain Reaction (PCR) – based methods are presently the most effective and reliable for GM detection even in processed food products. This study evaluated the effect of electron beam irradiation, used for food pasteurization, on the detection and quantification of dry GM soyabean and maize products; qualitative and quantitative (real-time TaqManTM probe) PCR analysis showed that electron beam irradiation treatment, even at a high dose (10 kGy), did not affect the traceability of transgenes.
Neutrino oscillations as many-particle induced interference between distinguishable particles

We investigate the causes of the curious property of neutrino oscillations of looking as an interference between fully distinguishable particles. The sources of this effect are identified as to be determined by the many particle nature of space of states of the quantum field theory. It is firstly underlined that in order to explain the observed oscillations, the neutrino subspace of states should not be interpreted as the direct product of the three neutrinos Fock spaces, which is equivalent to imposing a superselection rule. Further, it is argued that the linear completion of such a direct product space of states permits to describe the measured oscillations. Thus, the space of states of te Standard Model (SM) should be interpreted as the linear completion of the direct product of all the Fock spaces (boson or fermions ones) associated to the distinguishable particles. Then, it follows that the neutrino oscillations are determined as interference between independent particles, which are generated by states being outside the direct product of the neutrino Fock spaces. This interpretation seems to imply a large amount of interference observations between distinguishable particles in Particle as well as in Condensed Matter Physics. For seeking clearness, the discussion is done in the framework of a simple Quantum Field Theory (QFT) model of two relativistic free massive neutrinos.
Latency Analysis of Multiple Classes of AVB Traffic in TSN With Standard Credit Behavior Using Network Calculus

Time-sensitive networking (TSN) is a set of amendments that extend Ethernet to support distributed safety-critical and real-time applications in the industrial automation, aerospace, and automotive areas. TSN integrates multiple traffic types and supports interactions in several combinations. In this article, we consider the configuration supporting scheduled traffic (ST) based on gate-control lists, audio–video bridging (AVB) traffic according to IEEE 802.1BA that has bounded latencies, and best-effort traffic, for which no guarantees are provided. This article extends the timing analysis method to multiple AVB classes and proofs the credit bounds for multiple classes of AVB traffic, respectively, under frozen and nonfrozen behaviors of credit during guard band (GB). They are prerequisites for nonoverflow credits of credit-based shaper (CBS) and preventing starvation of AVB traffic. Moreover, this article proposes an improved timing analysis method reducing the pessimism for the worst-case end-to-end delays of AVB traffic by considering the limitations from the physical link rate and the output of CBS. Finally, we evaluate the improved analysis method on both synthetic and real-world test cases, showing the significant reduction of pessimism on latency bounds compared to related work and presenting the correctness validation compared with simulation results. We also compare the AVB latency bounds in the case of frozen and nonfrozen credit during GB. Additionally, we evaluate the scalability of our method with variation of the load of ST flows and of the bandwidth reservation for AVB traffic.
Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization

In a recent paper, we have presented a generative adversarial network (GAN)-based model for unconditional generation of the mel-spectrograms of singing voices. As the generator of the model is designed to take a variable-length sequence of noise vectors as input, it can generate mel-spectrograms of variable length. However, our previous listening test shows that the quality of the generated audio leaves room for improvement. The present paper extends and expands that previous work in the following aspects. First, we employ a hierarchical architecture in the generator to induce some structure in the temporal dimension. Second, we introduce a cycle regularization mechanism to the generator to avoid mode collapse. Third, we evaluate the performance of the new model not only for generating singing voices, but also for generating speech voices. Evaluation result shows that new model outperforms the prior one both objectively and subjectively. We also employ the model to unconditionally generate sequences of piano and violin music and find the result promising. Audio examples, as well as the code for implementing our model, will be publicly available online upon paper publication.
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework. To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent). In the search stage, the Child-Parent model uses an indicator generated by the child and parent model accuracy to evaluate the performance and abandon operations with less potential. In the training stage, a kernel-level CP loss is introduced to optimize the binarized network. Extensive experiments demonstrate that the proposed CP-NAS achieves a comparable accuracy with traditional NAS on both the CIFAR and ImageNet databases. It achieves the accuracy of $95.27\%$ on CIFAR-10, $64.3\%$ on ImageNet with binarized weights and activations, and a $30\%$ faster search than prior arts.
Attention-based Transducer for Online Speech Recognition

Recent studies reveal the potential of recurrent neural network transducer (RNN-T) for end-to-end (E2E) speech recognition. Among some most popular E2E systems including RNN-T, Attention Encoder-Decoder (AED), and Connectionist Temporal Classification (CTC), RNN-T has some clear advantages given that it supports streaming recognition and does not have frame-independency assumption. Although significant progresses have been made for RNN-T research, it is still facing performance challenges in terms of training speed and accuracy. We propose attention-based transducer with modification over RNN-T in two aspects. First, we introduce chunk-wise attention in the joint network. Second, self-attention is introduced in the encoder. Our proposed model outperforms RNN-T for both training speed and accuracy. For training, we achieves over 1.7x speedup. With 500 hours LAIX non-native English training data, attention-based transducer yields ~10.6% WER reduction over baseline RNN-T. Trained with full set of over 10K hours data, our final system achieves ~5.5% WER reduction over that trained with the best Kaldi TDNN-f recipe. After 8-bit weight quantization without WER degradation, RTF and latency drop to 0.34~0.36 and 268~409 milliseconds respectively on a single CPU core of a production server.
Information geometric inequalities of chemical thermodynamics

We study a connection between chemical thermodynamics and information geometry. We clarify a relation between the Gibbs free energy of an ideal dilute solution and an information-geometric quantity called an $f$-divergence. From this relation, we derive information-geometric inequalities that give a speed limit for a changing rate of the Gibbs free energy and a general bound of chemical fluctuations. These information-geometric inequalities can be regarded as generalizations of the Cramer--Rao inequality for chemical reaction networks described by rate equations, where unnormalized concentration distributions are of importance rather than probability distributions. They hold true for damped oscillatory reaction networks and systems where the total concentration is not conserved so that the distribution cannot be normalized. We also formulate a trade-off relation between speed and time on a manifold of concentration distribution by using the geometrical structure induced by the $f$-divergence. Our results apply to both closed and open chemical reaction networks, thus they are widely useful for thermodynamic analysis of chemical systems from the viewpoint of information geometry.
On Loss Functions and Regret Bounds for Multi-Category Classification

We develop new approaches in multi-class settings for constructing loss functions and establishing corresponding regret bounds with respect to the zero-one or cost-weighted classification loss. We provide new general representations of losses by deriving inverse mappings from a concave generalized entropy to a loss through a convex dissimilarity function related to the multi-distribution $f$ -divergence. This approach is then applied to study both hinge-like losses and proper scoring rules. In the first case, we derive new hinge-like convex losses, which are tighter extensions outside the probability simplex than related hinge-like losses and geometrically simpler with fewer non-differentiable edges. We also establish a classification regret bound in general for all losses with the same generalized entropy as the zero-one loss, thereby substantially extending and improving existing results. In the second case, we identify new sets of multi-class proper scoring rules through different types of dissimilarity functions and reveal interesting relationships between various composite losses currently in use. We also establish new classification regret bounds in general for multi-class proper scoring rules and, as applications, provide simple meaningful regret bounds for two specific sets of proper scoring rules. These results generalize, for the first time, previous two-class regret bounds to multi-class settings.
Forecasting Electricity Prices With Machine Learning: Predictor Sensitivity

Purpose Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts. Design/methodology/approach This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Research limitations/implications The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting. Practical implications When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors. Originality/value The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.
A Cyclical Post-filtering Approach to Mismatch Refinement of Neural Vocoder for Text-to-speech Systems

Recently, the effectiveness of text-to-speech (TTS) systems combined with neural vocoders to generate high-fidelity speech has been shown. However, collecting the required training data and building these advanced systems from scratch are time and resource consuming. An economical approach is to develop a neural vocoder to enhance the speech generated by existing or low-cost TTS systems. Nonetheless, this approach usually suffers from two issues: 1) temporal mismatches between TTS and natural waveforms and 2) acoustic mismatches between training and testing data. To address these issues, we adopt a cyclic voice conversion (VC) model to generate temporally matched pseudo-VC data for training and acoustically matched enhanced data for testing the neural vocoders. Because of the generality, this framework can be applied to arbitrary TTS systems and neural vocoders. In this paper, we apply the proposed method with a state-of-the-art WaveNet vocoder for two different basic TTS systems, and both objective and subjective experimental results confirm the effectiveness of the proposed framework.
Lost and found: stopping bluetooth finders from leaking private information

A Bluetooth finder is a small battery-powered device that can be attached to important items such as bags, keychains, or bikes. The finder maintains a Bluetooth connection with the user's phone, and the user is notified immediately on connection loss. We provide the first comprehensive security and privacy analysis of current commercial Bluetooth finders. Our analysis reveals several significant security vulnerabilities in those products concerning mobile applications and the corresponding backend services in the cloud. We also show that all analyzed cloud-based products leak more private data than required for their respective cloud services. Overall, there is a big market for Bluetooth finders, but none of the existing products is privacy-friendly. We close this gap by designing and implementing PrivateFind, which ensures locations of the user are never leaked to third parties. It is designed to run on similar hardware as existing finders, allowing vendors to update their systems using PrivateFind.