Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Spectral and Spatial Features for Deep Learning Based Blind Speaker Separation

This study tightly integrates complementary spectral and spatial features for deep learning based multi-channel speaker separation in reverberant environments. The key idea is to localize individual speakers so that an enhancement network can be trained on spatial as well as spectral features to extract the speaker from an estimated direction and with specific spectral structures. The spatial and spectral features are designed in a way such that the trained models are blind to the number of microphones and microphone geometry. To determine the direction of the speaker of interest, we identify time-frequency (T-F) units dominated by that speaker and only use them for direction estimation. The T-F unit level speaker dominance is determined by a two-channel chimera++ network, which combines deep clustering and permutation invariant training at the objective function level, and integrates spectral and interchannel phase patterns at the input feature level. In addition, T-F masking based beamforming is tightly integrated in the system by leveraging the magnitudes and phases produced by beamforming. Strong separation performance has been observed on reverberant talker-independent speaker separation, which separates reverberant speaker mixtures based on a random number of microphones arranged in arbitrary linear-array geometry.
Vestibular Functions and Parkinson's Disease

For decades it has been speculated that Parkinson's Disease (PD) is associated with dysfunction of the vestibular system, especially given that postural instability is one of the major symptoms of the disorder. Nonetheless, clear evidence of such a connection has been slow to emerge. There are still relatively few studies of the vestibulo-ocular reflexes (VORs) in PD. However, substantial evidence of vestibulo-spinal reflex deficits, in the form of abnormal vestibular-evoked myogenic potentials (VEMPs), now exists. The evidence for abnormalities in the subjective visual vertical is less consistent. However, some studies suggest that the integration of visual and vestibular information may be abnormal in PD. In the last few years, a number of studies have been published which demonstrate that the neuropathology associated with PD, such as Lewy bodies, is present in the central vestibular system. Increasingly, stochastic or noisy galvanic vestibular stimulation (nGVS) is being investigated as a potential treatment for PD, and a number of studies have presented evidence in support of this idea. The aim of this review is to summarize and critically evaluate the human and animal evidence relating to the connection between the vestibular system and PD.
Information Dissemination Analysis Using a Time-Weight Null Model: A Case Study of Sina Micro-Blog

Information dissemination in a social network is constrained by the underlying structure of the network and interaction between the users, and different dissemination patterns are exhibited over time. In this paper, first, information dissemination trees (IDTs) are constructed by tracking the Sina micro-blog information dissemination path (i.e., message forwarding and @), and an empirical analysis of their time characteristics is conducted. Second, the time-weight shuffle null model is established based on the null model in statistics, including the equal time-weight edge shuffle null model and the node time-weight shuffle null model, which are utilized to analyze the temporal properties and the corresponding information dissemination pattern. Thus, we further analyze the dynamics of the IDTs, which contain the burstiness of the information dissemination, the dissemination scale, and the effect of the network structure on the information dissemination. Finally, the experimental results indicate that the information dissemination in Sina micro-blog has a 5-h-effect and the network structure has a positive effect on the dissemination of information. The latter contrasts sharply with the scenarios of the European mobile phone network and e-mail communication network, and the burstiness of the information dissemination is suppressed while the network structure is destroyed.
Pressure Gain Characteristic of Continuously Rotating Detonation Combustion and its Influence on Gas Turbine Cycle Performance

Considering the pressure gain characteristic of continuously rotating detonation (CRD) combustion, a rotating detonation gas turbine cycle was established using methane as fuel. The variation of pressure gain characteristic and its influence on cycle performance at different compressor pressure ratios and air distribution proportions were investigated combined with a series of two-dimensional numerical simulations. The results were demonstrated that compared with the conventional approximate iso-pressure combustion, rotating detonation combustion could significantly improve the thermodynamic performance of gas turbine in various conditions. Due to the difference of entropy generation between rotating detonation and iso-pressure combustion, the pressure gain characteristic of the CRD combustor was provided a higher cycle efficiency. The efficiency gain was higher at lower compressor pressure ratio and air proportion entering CRD combustor. In the calculated cases, the pressurization ratio of the CRD combustor was higher than 1.37, cycle efficiencies of rotating detonation gas turbine were reached 0.395, cycle efficiency increasing rates were more than 22.5%, and power generation enhancements were greater than 1500 kW.
The Impact of MQTT-based Sensor Network Architecture on Delivery Delay Time

The purpose of this study is to present two new architectures of the complexes of embedded systems in the context of the Internet of Things. The proposed new schemes use the MQTT protocol but provide both an autonomous operation and an online mode. This is achieved by adding one additional layer, which consists of its own MQTT-broker and critical messages handler so that in case of the Internet access disappearance, the system could continue to respond to changes. The research of message delivery delays has been carried out for the proposed models. The comparative analysis was conducted to identify the advantages and disadvantages of both solutions.
Intensive Glucose Control Reduces the Risk Effect of TRIB3, SMARCD3, and ATF6 Genetic Variation on Diabetic Vascular Complications

Type 2 diabetes mellitus is a complex disease. Our previous study revealed that TRIB3 genetic variations were strongly associated with diabetic vascular complications, although TRIB3 regulation pathways remain poorly understood. We used two extreme treatment groups from a 2 × 2 factorial randomized controlled trial to identify a positive association, which was further validated in patients receiving cross treatment to test the effect of genetic polymorphisms among the different treatment groups. A gene-centric score (GS)-weighted model including the three associated genetic variations TRIB3 rs2295490, ATF6 rs12086247, and SMARCD3 rs58125572 was used. The results of the GS model indicated a 46% reduction in the risk of primary vascular complications in patients bearing more than two risk alleles [hazard ratio (HR) 0.54, 95% confidence interval (CI) 0.38–0.76, p < 0.001], following intensive glucose control treatment when compared with patients who received standard glucose control treatment. Furthermore, these patients benefited from active blood pressure-lowering treatment (HR 0.39, 95% CI 0.24–0.64, p < 0.001). However, no significant difference was observed between the two interventions in patients with fewer than two risk alleles (HR 1.09, 95% CI 0.86–1.39, p = 0.47). These results indicate that genetic variants in these three genes may be useful biomarkers for individualized drug therapy in diabetic patients.
3D Ground Point Classification for Automotive Scenarios

Autonomous driving applications must be provided with information about other road users and road side infrastructure by object detection modules. These modules often process point clouds sensed by light detection and ranging (LiDAR) sensors. Within the captured point cloud a large amount of points correspond to physical locations on the ground. These points do not hold information about road users, obstacles or road side infrastructure. Thus an important preprocessing step is identifying ground points to allow the object detection focusing on relevant measurements only. Within this paper we propose a ground point classification which relies on simple but effective geometric features. We evaluate the accuracy of the proposed algorithm on simulated data of different traffic scenarios. In addition, we evaluate the effectiveness of this preprocessing step based on the achieved speed up of an object detection algorithm on real world data.
Analysis and Procedures for Water Pipeline Leakage Using Three-Axis Accelerometer Sensors: ADXL335 and MMA7361

Previous studies have proposed vibration of the water pipeline using accelerometer sensors as a mechanism to detect leaks. Nevertheless, they only relied on a single x-axis data, no single study investigates all the axes of the sensors. Therefore, this paper aims to investigate a vibration technique to detect leakage on plastic water pipeline using wireless accelerometer sensors, namely, 6DOF accelerometer sensor (i.e., MPU6050) and breakout accelerometer sensors (i.e., ADXL335 and MMA7361) across the x-, y-, and z-axis. A 25-mm diameter of acrylonitrile butadiene styrene (ABS) pipe with a length of approximately 10 m was developed as the water pipeline testbed. All of the accelerometer sensors measured the vibration on the water pipeline across the x-, y-, and z-axis, over ZigBee networks. The vibration signals were then compared between the three sensors and analyzed by extracting the signal features in time and frequency domains. The sensors were examined based on three different cases, which are no pipe leakage, a 1-mm leak, and a 3-mm leak. All three sensors demonstrated a significant difference between no leak and leak conditions when the water pressure is in the range of 0.6– $1.2~kgf/cm^{2}$ for both time and frequency domains. For different leak size cases, ADXL335 can distinguish the 1-mm and 3-mm holes from the three-axis data. On the contrary, MPU6050 can identify similar leak size cases only from the y- and z-axis data. Overall, ADXL335 has the best performance compared with MPU6050 and MMA7361 in detecting water pipeline leakage, which includes the sizes of the leaks. Based on the empirical results, this paper finally proposes a procedure analysis for each sensor on improving the accuracy of water pipeline leakage detection.
Learning to Drive from Simulation without Real World Labels

Simulation can be a powerful tool for under-standing machine learning systems and designing methods to solve real-world problems. Training and evaluating methods purely in simulation is often “doomed to succeed” at the desired task in a simulated environment, but the resulting models are incapable of operation in the real world. Here we present and evaluate a method for transferring a vision-based lane following driving policy from simulation to operation on a rural road without any real-world labels. Our approach leverages recent advances in image-to-image translation to achieve domain transfer while jointly learning a single-camera control policy from simulation control labels. We assess the driving performance of this method using both open-loop regression metrics, and closed-loop performance operating an autonomous vehicle on rural and urban roads.
Non-linear Input Capacitance Determination of WBG Power FETs using Gate Charge Measurements

This paper discusses the measurement of the input capacitances C<inf>gs</inf> and C<inf>gd</inf> of SiC and GaN power FETs in order to implement simulation models with an improved mapping of the drain-source voltage V<inf>ds</inf> and drain current id during the switching operation. Based on the gate charge characteristic measured at an inductive load condition using different drain current values and temperature settings, the gate current is allocated to the charging of the gate-drain capacitance C<inf>gd</inf>and the gate-source capacitance C<inf>gs</inf>. With this approach the capacitance characteristics C<inf>gs</inf>(v<inf>gs</inf>) and C<inf>gd</inf>(V<inf>gd</inf>) are determined in the full operating range of the gate-source voltage V<inf>gs</inf> and gate-drain voltage V<inf>gd. </inf>
Learning Embedding Adaptation for Few-Shot Learning

Learning with limited data is a key challenge for visual recognition. Few-shot learning methods address this challenge by learning an instance embedding function from seen classes, and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task. In this paper, we propose a novel approach to adapt the embedding model to the target classiﬁcation task, yielding embeddings that are task-speciﬁc and are discriminative. To this end, we employ a type of self-attention mechanism called Trans-former to transform the embeddings from task-agnostic to task-speciﬁc by focusing on relating instances from the test instances to the training instances in both seen and unseen classes. Our approach also extends to both transductive and generalized few-shot classiﬁcation, two important settings that have essential use cases. We verify the effectiveness of our model on two standard benchmark few-shot classiﬁcation datasets — MiniImageNet and CUB, where our approach demonstrates state-of-the-art empirical performance.
The effect of three lining materials on microleakage of packable composite resin restorations in young premolars with cavity margins located on enamel and dentin/cementum - An In vitro study

Objectives The objective of our in vitro investigation was to assess the effect of a layer of three lining materials; flowable composite, flowable compomer, and light-curing glass ionomer cement as a liner on microleakage with Class V packable composite restorations in young premolar teeth. Materials and Methods A total of 40 premolars were assigned randomly into four groups of 10 teeth each (Groups 1, 2, 3, and 4). Class V cavities (3 mm × 2 mm × 1.5 mm) were prepared in a standardized technique on the buccal surface of every tooth with the cervical margin extending 0.5 mm below the cementoenamel junction, into the dentin. Cavities in the first group (control group) were filled with packable composite (Heliomolar HB) without liners. Cavities in Groups 2, 3, and 4 were restored with packable composite after placing flowable composite (Heliomolar Flow), light-curing glass ionomer cement (Vivaglass), and flowable compomer (Compoglass Flow) as liners, respectively. The 40 restored teeth were put in thermocycling machine, then immersed in 2% methylene blue solution for a period of 24 h to permit penetration of methylene blue into potential microgaps that might have been created between the restorative material and the tooth. Each tooth was then cut buccolingually into two halves through the center of the restoration parallel to their long axes. Photomicrographs of each group were captured, then examined using the ImageJ an analysis software. Results Control group (packable composite), Group 2 (flowable composite), and Group 3 (light-curing glass ionomer cement) showed no statistical significance between them. The difference between control group (packable composite) and Group 4 (flowable compomer) was statistically significant. Conclusions Flowable compomer as intermediate lining material can significantly reduce microleakage under packable composite.
Detecting Adversarial Examples in Convolutional Neural Networks

The great success of convolutional neural networks has caused a massive spread of the use of such models in a large variety of Computer Vision applications. However, these models are vulnerable to certain inputs, the adversarial examples, which although are not easily perceived by humans, they can lead a neural network to produce faulty results. This paper focuses on the detection of adversarial examples, which are created for convolutional neural networks that perform image classification. We propose three methods for detecting possible adversarial examples and after we analyze and compare their performance, we combine their best aspects to develop an even more robust approach. The first proposed method is based on the regularization of the feature vector that the neural network produces as output. The second method detects adversarial examples by using histograms, which are created from the outputs of the hidden layers of the neural network. These histograms create a feature vector which is used as the input of an SVM classifier, which classifies the original input either as an adversarial or as a real input. Finally, for the third method we introduce the concept of the residual image, which contains information about the parts of the input pattern that are ignored by the neural network. This method aims at the detection of possible adversarial examples, by using the residual image and reinforcing the parts of the input pattern that are ignored by the neural network. Each one of these methods has some novelties and by combining them we can further improve the detection results. For the proposed methods and their combination, we present the results of detecting adversarial examples on the MNIST dataset. The combination of the proposed methods offers some improvements over similar state of the art approaches.
Non-Guided Depth Completion with Adversarial Networks

Depth completion, which interpolates dense depth maps based on sparse inputs acquired from 3D LiDAR sensors, enhances perception capabilities of autonomous driving using object detection and 3D mapping. Recent studies on depth completion have leveraged deep learning approaches applying traditional convolutional neural networks to prediction of invisible information in sparse and irregular inputs. Due to the lack of local and global structures such as object boundary cues, however, the predicted information results in unstructured and noisy depth maps. This paper presents a supervised depth completion method using an adversarial network based only on sparse inputs. In the presented method, a fully convolutional depth completion network, along with the adversarial network, is designed to find and correct inconsistencies between ground truth distributions and the resulting depth maps interpolated by the depth completion network. This leads to more realistic and structured depth images without compromising runtime performance of inference. Experimental results based on the KITTI depth completion benchmark show that the presented adversarial network method achieves about 60% improvements for the accuracy of inference and increases the rate of convergence during training.
Divided Presence: Improving Group Decision-Making via Pseudo-Population Increase

During group decision-making, a group with an imbalanced minority and majority typically suffers normative bias. This bias creates socio-emotional conflict and decreases group member consent and decision power. Thus, we propose a novel video-chat system, "Divided Presence," which aims to reduce this bias by equalizing the apparent number of majority and minority participants using a pseudo-population increase. Members of a discussion use computer graphics avatars on monitors instead of their actual video appearances during communication. The apparent number of discussion members increases when two avatars are assigned to an arbitrary minority member; the avatars then speak on behalf of him or her. We evaluate the system with a three-person consensus game, where a minority member is assigned two avatars. The results show that our system increases the degree of consent among the majority participants when they finally agree to the minority opinion.
Botulinum Toxin A: A Novel Therapeutic Modality for Upper Extremity Chronic Regional Pain Syndrome

Background: Complex regional pain syndromes (CRPS) is a disease that is poorly understood. It is a chronic pain syndrome characterized by sympathetic disruptions as well as CNS sensitization. Botulinum Toxin-A has been shown to have efficacy in Raynaud's as well as other neuropathic pain disorders. Perhaps BTX-A warrants experimentation in the treatment of CRPS. Methods: Patients with CRPS refractory to 2 or more regional sympathetic nerve blocks in 2007 were included in the study. Patient's were asked to rank their initial pain on a visual analog scale of 0 to 10 (10 being the worst pain). "Tenderness maps" were marked on patient's areas of most pain in 1 by 1 centimeter grids. Each box on the grid was injected with 10 IU of BTX-A after nerve blocks with 1% lidocaine. Treatment sessions occured on a monthly basis with VAS pain scores being re-assessed immediately before the new treatment. t Test, linear regression, and Cohen's D-test were used to analyze the correlation of the data. Results: Study sample was 20 patients. Etiology of CRPS was 6 amputations, 4 crush injuries, 4 penetrating injuries, and 2 lacerations. Average pain reduction on VAS scale achieved was 2.05 points. Average percentage pain reduction was 22.94%. Cohen's D Test also showed a meaningful difference with a score of 1.01. Linear regression R2 = 0.491. Maximum pain reduction, on average, was achieved by treatment 9. Conclusion: Despite the esoteric etiology of CRPS, BTX-A has a well-demonstrated mechanism of effect. BTX-A should be further explored as a treatment modality for CRPS.
Actin polymerization and cell motility are affected by NAA80-mediated posttranslational N-terminal acetylation of actin

ABSTRACT Actin is the most abundant protein in our cells, and also one of the most studied. Nevertheless, an important modifier of actin, the N-terminal acetyltransferase (NAT) for actin, remained unknown until now. The recent identification of the enzyme that catalyzes actin acetylation, has opened up for functional studies of unacetylated actin using knockout cells. This enzyme, called NAA80 (Nα-acetyltransferase 80) or NatH, belongs to the NAT family of enzymes, which together provides N-terminal acetylation for around 80 % of the human proteome. In many cases, N-terminal acetylation is essential. In the case of actin, the acetyl group that NAA80 attaches to actin plays an important role in actin’s polymerization properties as well as in actin’s function in cell migration.
Multitaper estimation on arbitrary domains

Multitaper estimators have enjoyed significant success in estimating spectral densities from finite samples using as tapers Slepian functions defined on the acquisition domain. Unfortunately, the numerical calculation of these Slepian tapers is only tractable for certain symmetric domains, such as rectangles or disks. In addition, no performance bounds are currently available for the mean squared error of the spectral density estimate. This situation is inadequate for applications such as cryo-electron microscopy, where noise models must be estimated from irregular domains with small sample sizes. We show that the multitaper estimator only depends on the linear space spanned by the tapers. As a result, Slepian tapers may be replaced by proxy tapers spanning the same subspace (validating the common practice of using partially converged solutions to the Slepian eigenproblem as tapers). These proxies may consequently be calculated using standard numerical algorithms for block diagonalization. We also prove a set of performance bounds for multitaper estimators on arbitrary domains. The method is demonstrated on synthetic and experimental datasets from cryo-electron microscopy, where it reduces mean squared error by a factor of two or more compared to traditional methods.