Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-View Millimeter-Wave Imaging Over Wireless Cellular Network

Millimeter-wave (mmWave) imaging over wireless networks is one of the potential technologies in the design of integrated sensing and communication (ISAC) systems. To achieve complete and accurate sensing of the large-scale complex environment, multiple views from different user equipments (UEs) and base stations (BSs) in a wireless network should be fully and cooperatively exploited. In this paper, based on the uplink channels of the wireless cellular network, we propose a multi-view mmWave imaging architecture. In the proposed architecture, a single BS centrally or multiple BSs jointly process the transmitted data of UEs. Taking into account the complex physical propagation characteristics of mmWave in the environment, especially the occlusion effect, we exploit the multi-view sensing of the environment from various UEs and BSs. To solve the multi-view sensing problem for the considered model, we propose a generalized-approximate-message-passing-based multi-view sparse vector reconstruction (GAMP-MVSVR) algorithm to obtain the imaging results. In the proposed algorithm, a multi-layer factor graph is proposed to describe the data receiving and sending relationship, as well as the occlusion effect of mmWave propagation. The sum-product algorithm (SPA) is used to iteratively solve the imaging result. Specifically in each iteration, the occlusion relationship between the target object in the environment is recalculated according to the proposed occlusion detection rule, and in turn, used to estimate the scattering coefficients of the target objects. Simulation results verify the effectiveness of the proposed algorithm.
Neural Band-to-Piano Score Arrangement with Stepless Difficulty Control

This paper describes a music arrangement method of popular music that can convert a band score into a piano score with a steplessly-specified level of performance difficulty. The basic strategy of band-to-piano score arrangement is to select notes from an augmented band score obtained by up- and down-shifting the notes of an original band score by one octave. Given band scores and the corresponding piano scores with elementary- and advanced-levels, one can train a deep neural network (DNN) that estimates note masks conditioned by the difficulty levels. Conditioned by an intermediate level at runtime, however, the DNN tends to generate an advanced-level score. To solve this problem, assuming that an easier piano score is a subset of harder one, we estimate the basic importance of each note with a difficulty-agnostic DNN and then warp it with a power function depending on a specified difficulty level. To achieve the fine controllability of the difficulty level, we propose a training method that subjects the DNN to generating piano scores with various intermediate levels, where the note-level loss for those scores is evaluated using only the ground-truth elementary- and advanced-level scores. Considering the non-uniqueness of piano arrangement, the statistic-level loss with respect to the note density and polyphony level is also computed according to the given levels. The experimental results showed that the proposed method attained both the performance gain and the stepless difficulty control.
Lightweight Annotation and Class Weight Training for Automatic Estimation of Alarm Audibility in Noise

In an effort to improve occupational health and safety, we recently proposed an approach to assess the audibility of acoustic danger signals. It is based on the use of a binary classifier trained on perceptual data to predict the audibility of acoustic alarms in audio clips. In the present article, we first investigate the impact of label noise in the training data induced by a flexible annotation procedure on the model performance. We show that a lighter annotation procedure at training still allows for reaching close to human performance at test time. Besides, threshold selection is a crucial aspect in our application as it can have a direct impact on user safety. We thus explore class weight to train a model that allows for a more robust decision threshold selection, ensuring a low false positive rate.
Robust and Globally Sparse Pca via Majorization-Minimization and Variable Splitting

This paper addresses the problem of robust and sparse PCA. We consider a formulation combining a M-estimation type robust subspace recovery term and a mixed norm that promotes structured sparsity in the basis vectors, which is especially interesting for joint dimension reduction and variable selection. To solve it, we propose to leverage variable splitting methods, with the crucial step then lying on the Stiefel manifold. The resolution of this subproblem, involving the orthonormality constraint, is achieved through a tailored majorization-minimization (MM) step. Numerical experiments on gene expression measurements illustrate the interest of the proposal.
Exploring the Role of Fricatives in Classifying Healthy Subjects and Patients with Amyotrophic Lateral Sclerosis and Parkinson’s Disease

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) and Parkinson’s Disease (PD) impairs sustained phoneme productions. Vowels and fricatives get affected differently owing to the differences in their production mechanisms. This paper examines three sustained voiceless fricatives - /s/, /sh/ and /f/, as compared to three sustained vowels - /a/, /i/ and /o/, for classifying patients with ALS/PD and Healthy Controls (HC). Fricatives are found to achieve higher classification accuracies than /a/ and /o/, though /i/ outperforms all. Patients seem to find it difficult to form constrictions while producing fricatives, or to proximally position the tongue and palate while uttering /i/, due to dysarthria. Unwanted voicing added to voiceless fricatives by the patient population further contributes towards the discrimination. Both source (related to vocal cord) and filter (related to vocal tract) cues of fricatives, on average, outperform those of vowels. Lastly, decision-level fusion of /i/-/s/-/sh/, with a pooled classifier for these three phonemes, achieves the highest mean ALS vs. HC classification accuracy of 83.35%, although in PD vs. HC case, fusion of multiple /i/ utterances performs the best with an accuracy of 80.03%.
Some models for interaction of renewal processes related to neuron firing

One area of science in which probabilistic models are beginning to play a role is in neuron firing. The firing process may be thought of in terms of electrical potentials: there is a potential difference across the membrane surrounding a neuron; this potential difference is determined by the concentrations of sodium, potassium, and chlorine ions on the two sides of the membrane. When electrical impulses arrive along the many input fibers, the potential difference changes; when it crosses a certain threshold, the neuron “fires”, that is, an impulse is discharged along the axon. Arriving impulses are of two types, those which raise and those which lower the potential inside the neuron; these are called stimuli and inhibitors, respectively. The mechanism by which stimuli and inhibitors interact in changing the neuron potential is not well understood; however, it appears that some of the stimuli cause the neuron to respond, or fire, and the inhibitors play a role in preventing the other stimuli from causing a response. The ability of stimuli to cause responses is further weakened by the decay, in the absence of arriving impulses, of the transmembrane potential toward a “resting level”, the level to which the potential is reset after a firing. While several models for the firing process have been proposed ([3], [4], [5], [6], [7], [11]), the present paper is devoted to introducing some modifications into a model proposed by Ten Hoopen and Reuver ([9], [10]). In particular, we place a time limit on the ability of inhibitors to prevent a stimulus from producing a response. Hopefully, these modifications bring their model closer to physiological reality.
Spectroscopic measurement of temperatures in pulsed TIG welding arcs

Time resolved plasma temperatures in a pulsed tungsten-inert-gas (TIG) welding arc have been measured using optical emission spectroscopy. The peak and base pulse-averaged plasma temperatures both decrease with time after the arc ignition, and the plasma temperature decreases during the peak pulse period and increases during the base pulse period when the arc reaches the steady state. The decrease in the plasma temperature is associated with the increase in the cathode surface temperature and the decrease in the arc voltage and vice versa. The importance of the cathode surface temperature on the arc properties has been discussed.
Beyond Neural-on-Neural Approaches to Speaker Gender Protection

Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker’s gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection.
Exploiting Sparse Recovery Algorithms for Semi-Supervised Training of Deep Neural Networks for Direction-of-Arrival Estimation

This paper proposes a semi-supervised training approach for a direction-of-arrival (DoA) estimation based on a convolutional neural network (CNN). We apply a sparse recovery algorithm called optMGD-ℓ1-SVD on the training dataset consisting of only unlabeled observed data to obtain binarized pseudo-spectra regarded as the CNN training targets (labels). The estimated DoAs are obtained at test time by performing peak picking on the CNN outputs. optMGD-ℓ1-SVD has been shown to perform well with a few sensors under low signal-to-noise ratio (SNR) conditions (up to −6 dB) by optimally reweighting the pseudo-spectra of ℓ1-SVD based on the application of group delay function on the pseudo-spectra of MUSIC. Since its hyperparameters are noise-sensitive, we assume that the SNR levels of the training dataset are known such that we can use the optimal ones. We also consider multi-condition training using data of multiple SNR levels to improve the robustness towards different noisy environments. We evaluated the trained networks, named optMGD-ℓ1-SVD-CNN and MGD-ℓ1-SVD-CNN, in terms of the average root-mean-square error and the resolution probability under low SNR conditions (up to −20 dB). We demonstrated that it performed well with a few sensors and snapshots, including at SNR levels unseen in the training data.
Mahjong Solitaire Computing the number of unique and solvable arrangements

In this paper we will provide denitions on various aspects of the game Mahjong Solitaire. We will show in how many ways n tiles can be arranged and provide a recurrence relation on this sequence. We will also provide a recurrence relation on the number of ways we can divide these tiles over a various number of sets, and even a closed formula when we apply the constraint that each set may only contain two tiles. We will show that the product of these two sequences is not equal to the number of arrangements that can be created. We will provide algorithms for generating all arrangements and calculating the number of arrangements that can be won.
Improved Stability and Catalytic Efficiency of ω-Transaminase in Aqueous Mixture of Deep Eutectic Solvents

The efficient biosynthesis of chiral amines at an industrial scale to meet the high demand from industries that require chiral amines as precursors is challenging due to the poor stability and low catalytic efficiency of ω-transaminases (ω-TAs). Herein, this study adopted a green and efficient solvent engineering method to explore the effects of various aqueous solutions of deep eutectic solvents (DESs) as cosolvents on the catalytic efficiency and stability of ω-TA. Binary- and ternary-based DESs were used as cosolvents in enhancing the catalytic activity and stability of a ω-TA variant from Aspergillus terreus (E133A). The enzyme exhibited a higher catalytic activity in a ternary-based DES that was 2.4-fold higher than in conventional buffer. Moreover, the thermal stability was enhanced by a magnitude of 2.7, with an improvement in storage stability. Molecular docking studies illustrated that the most potent DES established strong hydrogen bond interactions with the enzyme’s amino acid, which enhanced the catalytic efficiency and improved the stability of the ω-TA. Molecular docking is essential in designing DESs for a specific enzyme.
Utilization of Bessel Beams in Wideband Sub Terahertz Communication Systems to Mitigate Beamsplit Effects in the Near-field

Exploring near-field propagation is increasingly relevant for terahertz(THz)-band communications, as very large antenna arrays (VLAA) are required to overcome the large propagation losses. In the near field, the efficiency of conventional far-field beamforming is reduced, while state-of-the-art near-field beamfocusing requires perfect positioning and channel state information to work efficiently. In this paper, first, experimental measurements of wireless data transmission above 100 GHz in indoor scenarios are presented to highlight the need for VLAAs that will likely operate in the near field. Next, diffraction-free Bessel beams are described as a promising near-field contender, with propagation characteristics in the near field similar to those of beamforming in the far field. Numerical results and their interpretations highlight the impact of prospective wideband THz communications during system implementations. It is particularly observed that THz Bessel beams can be optimized for the given environment, potentially improving the performance characteristics of the system.
Determining the thermal-physical characteristics of a coke foam layer in the fire protection of cable articles with foaming coating

An issue related to using cable products for building structures is to ensure their stability and durability when operating within wide limits. Therefore, the object of research was a change in the properties of the polymer sheath of the cable during the formation of a swollen coating layer under the influence of high temperature. It is proved that in the process of thermal action on the flame retardant coating, the process of thermal insulation of the cable involves the formation of particulate products on the surface of the sample. Under the action of the burner flame, a temperature was reached on the surface of the sample, which led to a swelling of the coating of more than 16 mm. The measured temperature on the inverse surface of the sample was no more than 160 °C, which indicates the formation of a barrier for temperature. In this regard, a calculation and experimental method for determining thermal conductivity when using a flame retardant as a coating has been developed, which makes it possible to estimate the coefficients of temperature conductivity and thermal conductivity under high-temperature action. According to the experimental data and established dependences, the coefficients of temperature conductivity and thermal conductivity of wood were calculated, which are 214.4·10–6 m2/s and 0.62 W/(m∙K), respectively, due to the formation of a heat-insulating swollen layer. The maximum possible temperature penetration through the thickness of the coating was assessed. A temperature was created on the surface of the sample, which significantly exceeds the ignition temperature of the polymer sheath of the cable, and, on a non-heated surface, does not exceed 160 °C. Thus, there is reason to argue about the possibility of directed adjustment of the fire protection processes of an electrical cable by using coatings capable of forming a protective layer on the surface of the material, which inhibits the rate of heat transfer.
A template for mapping emotion expression within hashtag publics

Current literature on networked publics lacks research that examines how emotions are mobilised around specific actors, and quantitative analysis of affective phenomena is limited to vanity metrics. We address this issue by developing a network analytic routine, which guides the attribution of emotions contained in hashtagged tweets to their sources and targets. The proposed template enables identification of networked inconsequentiality (i.e., inability to trigger dialogue), reply targets (i.e., individuals targeted in replies) and voice agents (i.e., senders of replicated utterances). We demonstrate this approach with two data sets based on the hashtags #Newzealand (n = 131,523) and #SriLanka (n = 145,868) covering two major incidents of terrorism related to opposing extremist ideologies. In addition to the methodological contribution, the study demonstrates that user-driven emergence of networked leadership takes place based on conventional structures of power in which individuals with high power and social status are likely to emerge as targets as well as sources of emotions.
Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models

We present a new method for imposing parameter restrictions in Markov-Switching Vector Autoregression (MS-VAR) models. Our method is more flexible than competing methodologies and easily handles a range of parameter restrictions over different equations, regimes and parameter types. We also expand the range of priors used in the MS-VAR literature. We demonstrate the versatility of our approach using three appropriate examples.
Spatial Attention Modulates Neuronal Interactions between Simple and Complex Cells in V1

Visual perception is profoundly modulated by spatial attention, which can selectively prioritize goal-related information. Previous studies found spatial attention facilitated the efficacy of neuronal communication between visual cortices with hierarchical organizations. In the primary visual cortex (V1), there is also a hierarchical connection between simple (S) and complex (C) cells. We wonder whether and how spatial attention modulates neuronal communication within V1, especially for neuronal pairs with heterogeneous visual input. We simultaneously recorded the pairs’ activity from macaque monkeys when they performed a spatial-attention-involved task, then applied likelihood-based Granger causality analysis to explore attentional modulation of neuronal interactions. First, a significant attention-related decrease in Granger causality was found in S-C pairs, which primarily displayed in the S-to-C feedforward connection. Second, the interaction strength of the feedforward connection was significantly higher than that of the feedback under attend toward (AT) conditions. Although information flow did not alter as the attentional focus shifted, the strength of communications between target- and distractor-stimuli-covered neurons differed only when attending to complex cells’ receptive fields (RFs). Furthermore, pairs’ communications depended on the attentional modulation of neurons’ firing rates. Our findings demonstrate spatial attention does not induce specific information flow but rather amplifies directed communication within V1.
A Compensated Shrinkage Affine Projection Algorithm for Debiased Sparse Adaptive Filtering

In this paper, we propose a novel sparse adaptive filtering algorithm termed compensated shrinkage affine projection algorithm (CS-APA). Our cost function is the sum of a time-varying data fidelity term and a difference-of-convex (DC) type nonconvex sparse regularizer. The regularizer includes the well known MC and SCAD penalty as special instances, thus leading to sparse estimation with small bias. Leveraging the DC structure of the regularizer, the nonconvex forward-backward splitting algorithm can be applied to the cost function, whereby the proposed CS-APA is derived. We present several favourable properties of CS-APA, including its mean stability analysis. Numerical examples demonstrate the superiority of CS-APA with comparisons to existing methods.
MRNET: Multi-Refinement Network for Dual-Pixel Images Defocus Deblurring

Defocus blurring is an inevitable phenomenon in cameras. Though many methods have been proposed, the problem is still challenging because of their low deblurring performance and long processing time. To solve this problem, we propose an efficient Multi-Refinement Network (MRNet) for dual-pixel images defocus deblurring. The MRNet contains two core modules that are alignment module and reconstruction module, respectively. We design a Siamese Pyramid Network (SPN) as alignment module to alleviate the misalignment problem of left and right views. At the same time, a Multi-Scale Residuals Group Module (MSRGM) is proposed in the reconstruction module, which can extract and fuse features from different scales to obtain better deblurring performance. Specifically, the reconstruction module is composed of multiple MSRGM modules, and each MSRGM is a refinement of the previous one, which is our leitmotif - Multi-Refinement. Experimental results on the popular benchmarks show that the proposed method can significantly improve the performance of defocus deblurring.
TEFISTA-NET: GTD Parameter Estimation of Low-Frequency Ultra- Wideband Radar via Model-Based Deep Learning

The geometrical theory of diffraction (GTD) has been widely investigated to describe the target scattering behaviors with the low-frequency ultra-wideband (LFW) radar. In this paper, we propose a new model-based deep learning method for GTD parameter estimation. The proposed method is designed by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) into a deep neural network. Unlike existing methods based on compressed sensing (CS), the key parameters in our algorithm are fully learnable, avoiding nontrivial parameter tuning procedures. Our network with simple convolution operations is more computationally efficient than existing methods, which require matrix inversions or quadratic programming and have low convergence speed. A novel loss function is designed for the new network to improve the capacity of target enhancement. Experiments on simulation data show that the new method achieves higher computational efficiency while maintaining or improving the precision of GTD parameter estimation compared with existing methods.