Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Self-Supervised Speech Representations for Speech Emotion Recognition

Self-supervised learning has recently been implemented widely in speech processing areas, replacing conventional acoustic feature extraction to extract meaningful information from speech. One of the challenging applications of speech processing is to extract affective information from speech, commonly called speech emotion recognition. Until now, it is not clear the position of these speech representations compared to the classical acoustic feature. This paper evaluates nineteen self-supervised speech representations and one classical acoustic feature for five distinct speech emotion recognition datasets on the same classifier. We calculate the effect size among twenty speech representations to show the magnitude of relative differences from the top to the lowest performance. The top three are WavLM Large, UniSpeech-SAT Large, and HuBERT Large, with negligible effect sizes among them. The significance test supports the difference among self-supervised speech representations. The best prediction for each dataset is shown in the form of a confusion matrix to gain insights into the best performance of speech representations for each emotion category based on the training data from balanced vs. unbalanced datasets, English vs. Japanese corpus, and five vs. six emotion categories. Despite showing their competitiveness, this exploration of self-supervised learning for speech emotion recognition also shows their limitations on models pre-trained on small data and trained on unbalanced datasets.
Observations of the Surface Boundary Structure within the 23 May 2007 Perryton, Texas, Supercell

AbstractIn situ data collected within a weakly tornadic, high-precipitation supercell occurring on 23 May 2007 near Perryton, Texas, are presented. Data were collected using a recently developed fleet of 22 durable, rapidly deployable probes dubbed “StickNet” as well as four mobile mesonet probes. Kinematic and thermodynamic observations of boundaries within the supercell are described in tandem with an analysis of data from the Shared Mobile Atmospheric Research and Teaching Radar.Observations within the rear-flank downdraft of the storm exhibit large deficits of both virtual potential temperature and equivalent potential temperature, with a secondary rear-flank downdraft gust front trailing the mesocyclone. A primarily thermodynamic boundary resided across the forward-flank reflectivity gradient of the supercell. This boundary is characterized by small deficits in virtual potential temperature coupled with positive perturbations of equivalent potential temperature. The opposing thermodynamic perturbatio.
Brain-inspired Hierarchical Attention Recurrent CNN for Image Classification

So far convolutional neural network (CNN) is one of the best-known deep learning models and is pervasively employed in computer vision tasks. CNN models the forward connectivity of the visual cortex, however, there are also a huge amount of recurrent connections and the unique attention mechanism in the biological visual system. Inspired by this fact, we take the most important elements from the structural and functional characteristics of the visual cortex and propose hierarchical attention recurrent CNN (HARCNN) to model the process of the ventral visual pathway. Four blocks are mapped to ventral visual regions V1, V2, V4, and IT. Multi-scale feature fusion is used in the V1 block to increase the receptive field, and the recurrent circuitries in V2, V4, and IT areas are employed to explore the neural dynamics. The attention module is applied to enable the model to concentrate on the important information. HARCNN is evaluated by Top-l accuracy on three benchmark datasets of image classification, namely, CIFAR-10, CIFAR-100, and MNIST. Experimental findings prove that HARCNN is effective for modeling visual information processing and its performance is comparable to the current deep convolutional networks.
Two-particle interferometry.

An exposition is given of the fundamental ideas of the recently opened field of two-particle interferometry, which employs spatially separated, quantum mechanically entangled two-particle states. These ideas are illustrated by a realizable arrangement, in which four beams are selected from the output of a laser-pumped down-converting crystal, with two beams interferometrically combined at one locus and two at another. When phase shifters are placed in these beams, the coincident count rates at the two loci will oscillate as the phases are varied, but the single count rates will not.
Motion Correction Using Deep Learning Neural Networks - Effects of Data Representation

An in-silico investigation of the effects of ultrasound data representation on the accuracy of the motion prediction made using deep learning neural networks was carried out. The representations studied include: linear (‘envelope’), log compressed, linear with phase and log compressed with phase. A UNet model was trained to predict non-rigid deformation field using a fixed and a moving image pair as the input. The results illustrate that the choice of the representation plays an important role on the accuracy of motion estimation. Specifically, representations with phase information outperform the representations without phase. Furthermore, log-compressed data yielded predictions with higher accuracy than the linear data.
Photoresponses and Memory Effects in Optoelectronic Synaptic Devices Based on CdSe Quantum Dots and Poly(3-hexylthiophene)

The optical responses and memory effects of photoelectric synaptic devices based on CdSe quantum dots (QDs) and poly(3-hexylthiophene) (P3HT) are studied in this work. Compared with devices only incorporating CdSe QDs, the devices based on CdSe QDs and P3HT exhibit higher photocurrents because the heterojunction formed by CdSe QDs and P3HT enhances the separation of photogenerated excitons, and the loss of excitons in the QDs reduces. In addition, due to the effect of the surface defect trapping charge of CdSe QDs, the photocurrent of the device can still be maintained for more than 100 seconds under the condition of zero gate voltage. Finally, the device can perform each synaptic activity with a low power consumption of 12.9 pJ by adjusting the concentration of QDs.
Ring Array Passive Acoustic Mapping Using Hybrid Heterogeneous Angular Spectrum Method

Passive acoustic mapping (PAM) using a ring array facilitates high-resolution panoramic monitoring of cavitation activities in human breast. While the heterogeneous angular spectrum method has been developed for fast PAM reconstruction using planar arrays, it is not directly applicable in ring arrays. In this work, we combine the Rayleigh-Sommerfeld diffraction integral and the angular spectrum method for fast panoramic PAM with high resolution in heterogeneous medium. We evaluated the spatial resolution of the hybrid method with ring array in simulated experiments. Bubble clouds localization ability of the proposed method was assessed by imaging 9 tubes containing randomly distributed bubbles in the tumor of a human breast phantom with heterogeneous speed of sound (SoS) and compared with time-domain (TD-) and frequency-domain (FD-) “delay and sum” (DAS) methods. Results show that the proposed method can provide high resolution panoramic PAM images in the heterogeneous human breast phantom with more than 8 times speed-up compared to TD-DAS and FD-DAS algorithms.
On the Nucleosynthetic Origin of Presolar Silicon Carbide X-Grains

In this paper we present an extension of our nucleosynthesis parameter study within the classical neutrino-driven wind scenario of core-collapse supernovae (ccSNe). The principal aim of this decade-old study was to shine light on the production of the historical ‘p-only’ isotopes of the light trans-Fe elements in the Solar System (S.S.). One of our earliest key findings was the co-production of neighbouring classical ‘s-only’ and ‘r-only’ isotopes between Zn (Z = 30) and Ru (Z = 44), alongside the synthesis of light p-isotopes, under similar conditions of a moderately neutron-rich, low-entropy, charged-particle component of Type II SNe wind ejecta. We begin this analysis by expressing the need for nuclear-structure input from detailed spectroscopic experiments and microscopic models in the relevant shape-transition mass region between N = 50 and N = 60. Then, we focus on the unique nucleosynthetic origin of the anomalous isotopic compositions of Zr (Z = 40), Mo (Z = 42) and Ru (Z = 44) in presolar silicon carbide X-grains. In contrast to the interpretation of other studies, we show that these grains do not reflect the signature of a ‘clean’ stellar scenario but are mixtures of an exotic rapid (r-process like) nucleosynthesis component and different fractions of S.S. material. Thus, the synthesis of these light isotopes through a ‘primary’ production mode provides further means to revise the abundance estimates of the light trans-Fe elements in the S.S. , reducing our dependence on still favoured ‘secondary’ scenarios like Type Ia SNe or neutron-bursts in exploding massive stars.
Exponential stability and instability of stochastic neural networks 1

In this paper we shall discuss stochastic effects to the stability property of a neural network Suppose the stochastically perturbed neural network is described by an Ito equation [ILM002]The Geral theory on the almost sure exponential stability and instability of the stochastically perturbed neural network is first established. The theory is then applied to investigate the stochastic stabilization and destabilization of the neural network. Several interesting examples are also given for illustration
Accelerating multi-echo MRI in k-space with complex-valued diffusion probabilistic model

Compressed sensing (CS) is an interesting technique for effectively accelerating multi-echo gradient-recalled-echo (ME-GRE) magnetic resonance imaging (MRI). However, how to reconstruct high-quality MRI from undersampled k-space data is still a challenge issue. Considering the superiority of complex-valued convolutional neural network and the image generation ability of denoising diffusion probabilistic model (DDPM), in this work, we proposed a complex diffusion probabilistic model (CDPM) to realize the CS-MRI reconstruction in k-space. Specifically, we randomly generated a mask to under-sample the k-space data; those k-space data not acquired were taken as the input of forward diffusion model. We recover these unobserved k-space data through an inverse diffusion process, which was realized by a complex-valued UNet-like network. By comparing the complexvalued CDPM and real-valued DDPM models on the reconstruction of ME-GRE MRI, we validated that our proposed CDPM model outperforms the real-valued DDPM methods. It can effectively restore the image details with a sampling ratio of 25%.
Analysis of adjacency effects for two Lambertian half‐spaces

An analysis of one‐dimensional adjacency effects is presented. This classical problem is shown to be amenable to closed form error analysis when one‐dimensional adjacency correction schemes are used. In particular the error made when surface reflectance is retrieved using an infinite target assumption is given in closed form. This allows deductions for the behaviour of the error as a function of wavelength and optical thickness. Typical length‐scales of the adjacency effects are deduced and the range and magnitude of the error are also given in closed form.
Influence of Three-dimensional Boron Nitride Foam on Thermal Conductivity and Charge Transport Behavior for Polypropylene/Elastomer Blends

The application of polypropylene (PP)/elastomer blend in high voltage direct current (HVDC) cable insulation is up against two major problems, the low thermal conductivity and the degraded insulation properties, which threatens the safe operation of power cable. In this paper, a self-support and pressure reinforced three-dimensional nano boron nitride foam (3D-n-BN) was introduced into PP/propylene-based elastomer (PBE) blend to synergistically improve the thermal conductivity and electrical properties. Microstructures, thermal conductivity, charge transport behaviors and mechanical properties were estimated. It was found that 3D-n-BN micro skeleton structure was formed in PP/PBE blend matrix. The thermal conductivity was improved and more deep traps were introduced with the addition of 3D-n-BN, while the elongation at break (EAB) of PP/PBE/3D-n-BN composite was a little higher than that of PP/PBE blend. It is proposed that the introduction of 3D-n-BN micro skeleton structures can synergistically improve the thermal conductivity and electrical properties of PP/elastomer blend without causing the degradation of mechanical performance.
ReLI: Real-Time Lightweight Byzantine Consensus in Low-Power IoT-Systems

IoT/WSN assisted smart-systems are making our living easier and more comfortable in various aspects. However, there is always a chance of malfunctioning in such massive decentralized systems in crucial moments because of one or more components of the system getting compromised. For instance, monitoring systems installed to watch the status of a bridge may unknowingly suppress the recent deterioration in the status because of some compromised sensing devices. Byzantine fault tolerance support is highly essential in combating the presence of such smart devices with malicious intentions. However, existing solutions for consensus or data aggregation in IoT/WSN systems either assume non-Byzantine node failures or use only simulation/theoretical models to address the existence of Byzantine nodes. Theoretically, a decentralized system can effectively tolerate Byzantine characteristics of up to a certain fraction of the nodes. However, to achieve even that, the nodes need to interact extensively and share data with each other which makes it challenging for such solutions to get practically realized and produce outcomes in real-time, especially in resource-constrained IoT systems. In this work, we adopt Synchronous-Transmission based mechanisms and propose a framework ReLI to efficiently achieve Byzantine consensus in low-power IoT systems. We show that ReLI can operate up to 80% faster and consume up to 78% lesser radio-on time compared to the traditional implementation of the strategy in a publicly available IoT/WSN testbed containing 45 nodes.
Quantitative Evaluation of Application Layer Protocols for Edge-Centric Real-Time IoVT Applications

Many applications are moving to edge computing in order to benefit from its enhanced performance and saved bandwidth. The overall performance and quality of data transmissions in these edge-based applications highly depend on the choice of application layer protocols. This is especially crucial for those dealing with continuous and high volumes of data transmissions such as Internet of Video Things (IoVT) applications. While previous works have investigated various trade-offs involved in choosing application layer protocols for IoT applications, their trade-offs for IoVT are overlooked. So the question still remains as to whether the same protocols observed to be well-suited for IoT would be appropriate choices for IoVT especially those with timing constraints in edge computing environments. In this paper, we present a quantitative evaluation of the performance, throughput, resource, and energy consumption of various protocols including HTTP (both HTTP/1 and HTTP/3 over QUIC), CoAP, WebSockets, AMQP, MQTT, DDS, and XMPP for real-time video data transmissions in an edge computing environment. We perform the comparisons while analyzing the sensitivity of the results with regard to video resolutions and transmission rates. Our comparisons reveal that promising protocols in IoT such as MQTT and CoAP, are not necessarily the best choices for real-time edge-based IoVT applications. The trade-offs discussed in this paper based on various metrics provide valuable insights for system architects dealing with latency sensitive edge-centric IoVT applications to choose the best protocol according to their system needs and constraints.
NGC 454: unveiling a new ‘changing look’ active galactic nucleus

We present a detailed analysis of the X-ray spectrum of the Seyfert 2 galaxy NGC 454E, belonging to the interacting system NGC 454. Observations performed with Suzaku, XMM–Newton and Swift allowed us to detect a dramatic change in the curvature of the 2–10 keV spectrum, revealing a significant variation of the absorbing column density along the line of sight (from ∼ 1 × 1024 cm−2 to ∼ 1 × 1023 cm−2). Consequently, we propose this source as a new member of the class of ‘changing look’ active galactic nuclei (AGN), i.e. AGN that have been observed both in Compton thin (NH= 1023 cm−2) and reflection-dominated states (Compton thick, NH > 1024 cm−2). Due to the quite long time lag (six months) between the Suzaku and XMM–Newton observations, we cannot infer the possible location of the obscuring material causing the observed variability. In the 6–7 keV range, the XMM–Newton observation also shows a clear signature of the presence of an ionized absorber. Since this feature is not detected during the Suzaku observation (despite its detectability), the simplest interpretation is that the ionized absorber is also variable; its location is estimated to be within ∼ 10−3 pc from the central black hole, probably much closer in than the rather neutral absorber.
All-Digital Full-Precision In-SRAM Computing with Reduction Tree for Energy-Efficient MAC Operations

This paper proposes an all-digital full-precision static random-access memory based computing-in-memory (SRAM-CIM) macro with compressor-based reduction tree (CRT) for energy-efficient multiplication-and-accumulation (MAC) operations. The proposed CRT composed of hybrid 28T/18T/14T 3–2 compressors (full adders, FAs) and 18T half adders (HAs) with lower supply voltage consumes lower power compared to conventional binary adder tree (BAT). The experimental results show that the power and area of the proposed CRT are reduced by up to 56.15% and 28.11%, respectively, as compared to BAT. The proposed SRAM-CIM macro with CRT achieves 78.07% higher energy efficiency per unit area, compared to previous all-digital full-precision SRAM-CIM macro with BAT.
Cooperative Networking Approach to Assisting Blinds in a Crowd Using Air Trackers

Air trackers such as AirTags and AirFinders have emerged as an alternative to image-and GPS-based positioning systems. On the other hand, adaptive cooperative networks have been found as powerful multi-agent adaptive systems which incorporate the information from the neighboring nodes (a.k. a. agents) in the adaptation or modelling process. Here, diffusion adaptation strategy is effectively used to exploit the positioning information from a group of blind people within a neighborhood in the crowd, collected by Air trackers, to best support them reaching their destinations.
Solar wind Halo electrons from 1–4 AU

In this study we use observations from the Ulysses solar wind electron spectrometer to make a first examination of the evolution of the solar wind suprathermal or halo electron population as a function of heliocentric distance beyond 1 AU. Solar wind electron analyzers previously flown to distances >1 AU did not resolve the halo electron distribution so that the 3-D suprathermal solar wind electron observations from Ulysses represent a unique data set. As the core population cools with increasing heliocentric distance, no gap is formed between the core and halo populations. Rather, the halo electrons extend to increasingly lower energies. As predicted previously on theoretical grounds, the ratio of the core electron temperature to the low energy cutoff of the halo population appears to be roughly constant with a value of ∼7.5. The total integrated heat flux drops rapidly with increasing heliocentric distance; we find a best fit power law of R−2.36. In addition, we find that the ratio of the halo to core densities is roughly constant over heliocentric distance with the halo representing 4% of the total electron distribution. These results suggest that the halo population may not consist of truly non-interactive test particles over the heliocentric range of 1–4 AU.
AI-Based mechanism for the Predictive Resource Allocation of V2X related Network Services

5G architectures will utilize the virtualization of the network functions (VNF) and the use of Multi-access edge computing (MEC) to gain multiple benefits such as simpler service orchestration, while simultaneously covering diverse use cases even with strict performance requirements. 5G service orchestration mechanisms will need to allow more efficient and flexible network deployment and operations in a resource-efficient and delay-sensitive manner. A field that is expected to be greatly boosted by these advances, is Cellular Vehicle to Everything communications. 5G will enable cooperative, connected and automated mobility services, which are often are safety critical while also having stringent delay requirements. This paper, proposes a mechanism that predicts the future position of a vehicle moving in both urban and/or highway environments. Based on this knowledge, it decides on the optimal position of VNFs so that the allocation of network resources can be preemptively requested. The objective of this mechanism is to ensure the uninterrupted, continuous connections of the vehicles, resulting in minimal or no service interruption time while ensuring an optimal utilization of Edge Cloud and MEC resources.
Strain-regulated flexible molecular sensors enabled by 2D PtTe2

Two-dimensional transition metal dichalcogenide (TMDs) holds great promise for future wearable technologies. The large-scale synthesis of TMDs and the investigation of their molecular sensing properties are current research hotspots. Herein, we construct flexible PtTe2 molecular sensors directly on polyimide, followed by the exploration on ammonia sensing properties and stability under strain. The sensor shows an excellent ammonia response of 0.4% ppm-1surpassing most reported semimetal-based sensors. In-situ sensing testing reveals 500% enhancement in response after controlled strain engineering, which suggests the potential to expand limit of detection and linearity range.
Assessing Unsupervised Machine Learning solutions for Anomaly Detection in Cloud Gaming Sessions

Cloud gaming applications have gained great adoption on the Internet particularly benefiting from the wide availability of broadband access networks. However, they still fail to meet users’ quality requirements when accessed using cellular networks due to common wireless channel degradations. Machine Learning (ML) techniques can be leveraged to detect such anomalies during users’ cloud gaming sessions. In this respect, unsupervised ML approaches are particularly interesting since they do not require labeled datasets. In this work, we investigate these approaches to understand their performance and their robustness. Our dataset consists of game sessions played on the public Google Stadia Cloud Gaming servers. The game sessions are played using a 4G network emulation replicating the capacity variations sampled on a commercial 4G network. We compare different models ranging from traditional approaches to deep learning and we evaluate their default performance while varying the level of contamination in their training datasets. Our experiments show that Auto-Encoders models achieve the best performance without contamination while the OC-SVM and the Isolation Forest are the most robust to data contamination.