Miracle-dz/UniFAR-FTUs huggingface dataset:
Record 2.29 Tb/s GS-256QAM Transmission using a Single Receiver

8 x 26 GBd DP-GS256QAM super-channel with a net data rate of 2.29 Tb/s was received after 75 km transmission using a single 211-GHz optical receiver. The use of digital pre-distortion and tailored geometric constellation shaping led to an improvement of 1.2 bit/4D-sym in the achievable information rate.
Intelligibility prediction of enhanced speech using recognition accuracy of end-to-end ASR systems

We propose speech intelligibility (SI) prediction methods using the recognition accuracy of an end-to-end (E2E) automatic speech recognition (ASR) system whose ASR performance has become comparable to the human auditory system due to its recent significant progress. Such predictors will fuel the development of speech enhancement methods for human listeners. In this paper, we evaluate our proposed method's prediction performance of the intelligibility of enhanced noisy speech signals. Our experiments show that when ASR systems are trained with various noisy speech data, our proposed methods, which do not require clean reference signals, predict SI more accurately than the existing “intrusive” methods: short-time objective intelligibility (STOI), extended-STOI (eSTOI), and our previously proposed methods, which were based on deep neural network-hidden Markov model hybrid ASR systems. Our experiments also show that our method, which additionally uses clean speech for determining the speech region of evaluation signals, further improves the prediction accuracy more than the existing methods.
A Network Dimensioning Algorithm for Exploiting the Capabilities of Subcarrier-based Point-to-Multipoint Coherent Optics

We present the first generalized dimensioning algorithm for optical networks with subcarrier-based Point-to-Multipoint (P2MP) coherent transceivers, which covers hub-spoke determination, transceiver allocation, along with light-tree routing and spectrum assignment, in arbitrary topologies. The benefits of P2MP optics in a metro-network case study are evaluated.
Forward-backward LMS adaptive line enhancer

A new LMS (least-mean-square) adaptive line enhancer algorithm is presented. It makes use of forward and backward prediction errors to update the coefficient values. For a given feedback factor, the algorithm converges to the optimal Wiener solution with the same speed as for the LMS algorithm, but requires about twice the number of multiplications and additions. However, in the situation when the order of the enhancer is at least a few times larger than the number of sinusoids to be enhanced, or when the frequencies of the sinusoids to be enhanced are not close to 0 or 0.5, the misadjustment of the new algorithm is approximately half that of the LMS algorithm. >
Lip Sync Matters: A Novel Multimodal Forgery Detector

Deepfake technology has advanced a lot, but it is a double-sided sword for the community. One can use it for beneficial purposes, such as restoring vintage content in old movies, or for nefarious purposes, such as creating fake footage to manipulate the public and distribute non-consensual pornography. A lot of work has been done to combat its improper use by detecting fake footage with good performance thanks to the availability of numerous public datasets and unimodal deep learning-based models. However, these methods are insufficient to detect multimodal manipulations, such as both visual and acoustic. This work proposes a novel lip-reading-based multi-modal Deepfake detection method called “Lip Sync Matters. ” It targets high-level semantic features to exploit the mismatch between the lip sequence extracted from the video and the synthetic lip sequence generated from the audio by the Wav2lip model to detect forged videos. Experimental results show that the proposed method outperforms several existing unimodal, ensemble, and multimodal methods on the publicly available multimodal FakeAVCeleb dataset.
Eyes Recognition for Inner Canthus Temperature Detection using YOLOv5 Algorithm

Viral infections severely attack the physically frail elderly population, resulting in fatal drawbacks. The fact of having massive elderly population growth in Europe gives high priority to the detection of physical frailty and infectious diseases. This paper presents a safe, accurate, fast temperature detection system that could be integrated into homes or assisted living residences. The presented work aims to detect one of the symptoms of contagious diseases: elevated body temperature. In order to do so, we worked on recognizing eyes in thermal face images followed by scanning the detected eyes region for inner canthus temperature. Eyes detection was done by training four different sizes of You Only Look Once 5th version (YOLOv5) object detection algorithm: nano, small, medium and large. A total of 4,255 thermal images were implemented for the training process after merging two different datasets and applying data augmentation techniques. Results show a similar mAP score (99.5%) for the different trained models. The large YOLOv5 model was the fastest, working at 115 FPS.
Radio Propagation Measurements and Channel Characterization in High-Voltage Substation Scenarios at 3.35 GHz

An in-depth and accurate knowledge of the radio channel is vital to support the application of wireless communication networks in power Internet of Things (PIoT). In this paper, we investigate the radio propagation measurements and channel characterization for typical substation scenarios. The propagation measurements at 3.35 GHz have been performed in three high-voltage substation scenarios, involving a 10 kV switch room, a 110 kV gas insulated substation (GIS) room and an outdoor 220 kV substation. Based on the measured data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss and shadow fading are proposed, and results of Ricean K-factor and root-mean-square (RMS) delay spread are presented. These results will provide a valuable reference for the deployment and evolution of wireless communication networks in high-voltage substation scenarios for PIoT.
Load Balancing Potentials in 5G NR FR2

With the use of millimeter waves (mmWaves) in high carrier frequencies, massive bandwidth is available in the fifth-generation (5G) of cellular networks and upcoming 6G. However, at such high frequencies, the radio propagation suffers from higher free space path loss, which is compensated by the use of beamforming at the transmitter and the receiver sides. With a high number of beams at the base station, the user equipment (UE) has multiple candidate serving cells to connect to, which in turn offers new opportunities to achieve high load balancing gains. In this paper we introduce an optimal load balancing approach with respect to conventional initial access (IA) based on maximum reference signal received power (RSRP). It is followed by simulation for urban micro (UMi) hexagonal scenario with multi-beam at next generation node base station (gNB). It is shown that on average 8.5% gain, not to mention over 40% gain for some realizations, is achievable for New Radio (NR) (single radio frequency (RF)-chain) while this gain is on average less than 0.4% for Long-Term Evolution (LTE).
Obstructive Sleep Apnea Classification Using Snore Sounds Based on Deep Learning

Early screening for the Obstructive Sleep Apnea (OSA), especially the first grade of Apnea-Hypopnea Index (AHI), can reduce risk and improve the effectiveness of timely treatment. The current gold standard technique for OSA diagnosis is Polysomnography (PSG), but the technique must be performed in a specialized laboratory with an expert and requires many sensors attached to a patient. Hence, it is costly and may not be convenient for a self-test by the patient. The characteristic of snore sounds has recently been used to screen the OSA and more likely to identify the abnormality of breathing conditions. Therefore, this study proposes a deep learning model to classify the OSA based on snore sounds. The snore sound data of 5 OSA patients were selected from the opened-source PSG- Audio data by the Sleep Study Unit of the Sismanoglio-Amalia Fleming General Hospital of Athens [1]. 2,439 snoring and breathing-related sound segments were extracted and divided into 3 groups of 1,020 normal snore sounds, 1,185 apnea or hypopnea snore sounds, and 234 non-snore sounds. All sound segments were separated into 60% training, 20% validation, and 20% test sets, respectively. The mean of Mel-Frequency Cepstral Coefficients (MFCC) of a sound segment were computed as the feature inputs of the deep learning model. Three fully connected layers were used in this deep learning model to classify into three groups as (1) normal snore sounds, (2) abnormal (apnea or hypopnea) snore sounds, and (3) non-snore sounds. The result showed that the model was able to correctly classify 85.2459%. Therefore, the model is promising to use snore sounds for screening OSA.
On the stability of plane shock waves

The decay of small perturbations on a plane shock wave propagating along a two-dimensional channel into a fluid at rest is investigated mathematically. The perturbations arise from small departures of the walls from uniform parallel shape or, physically, by placing small obstacles on the otherwise plane parallel walls. An expression for the pressure on a shock wave entering a uniformly, but slowly, diverging channel already exists (given by Chester 1953) as a deduction from the Lighthill (1949) linearized small disturbance theory of flow behind nearly plane shock waves. Using this result, an expression for the pressure distribution produced by the obstacles upon the shock wave is built up as an integral of Fourier type. From this, the shock shape, ξ, is deduced and the decay of the perturbations obtained from an expansion (valid after the disturbances have been reflected many times between the walls) for ξ in descending power of the distance, ζ, travelled by the shock wave. It is shown that the stability properties of the shock wave are qualitatively similar to those discussed in a previous paper (Freeman 1955); the perturbations dying out in an oscillatory manner like ζ−3/2. As before, a Mach number of maximum stability (1·15) exists, the disturbances to the shock wave decaying most rapidly at this Mach number. A modified, but more complicated, expansion for the perturbations, for use when the shock wave Mach number is large, is given in §4. In particular, the results are derived for the case of symmetrical ‘roof top’ obstacles. These predictions are compared with data obtained from experiments with similar obstacles on the walls of a shock tube.
Calculation and Analysis of Transient and Sub-Transient Processes in Homopolar Inductor Machine

Homopolar inductor machine (HIM) belongs to the electrical excitation synchronous machine (EESM), but compared with the conventional wound-field synchronous machine (WFSM), it has static field winding and static armature winding, whose rotor is solid structure without any winding or permanent magnetic on it. In this paper, it is found that an obvious difference of the transient and sub-transient (T&ST) characteristics between HIM and WFSM due to the special three-dimensional homopolar magnetic structure of HIM. Firstly, in this paper, before analyzing the T&ST processes, it is necessary to sort, derive and develop the steady-state parameter formulae of HIM. Secondly, for the transient process, the variables are calculated, the special transient characteristics are explained and the reasons are revealed. Thirdly, the subtransient processes caused by the eddy currents in HIM are discussed qualitatively, whose parameters calculation method is the same as that of the transient process and not be repeated.
Cloud-Resolving Simulations of Deep Convection over a Heated Mountain

Abstract Cloud-resolving numerical simulations of airflow over a diurnally heated mountain ridge are conducted to explore the mechanisms and sensitivities of convective initiation under high pressure conditions. The simulations are based on a well-observed convection event from the Convective and Orographically Induced Precipitation Study (COPS) during summer 2007, where an isolated afternoon thunderstorm developed over the Black Forest mountains of central Europe, but they are idealized to facilitate understanding and reduce computational expense. In the conditionally unstable but strongly inhibited flow under consideration, sharp horizontal convergence over the mountain acts to locally weaken the inhibition and moisten the dry midtroposphere through shallow cumulus detrainment. The onset of deep convection occurs not through the deep ascent of a single updraft but rather through a rapid succession of thermals that are vented through the mountain convergence zone into the deepening cloud mass. Emerging t.
H ∞ control of linear time-varying systems: a state-space approach

In this paper the standard problem of $H^\infty $ control theory for finite-dimensional linear time-varying continuous-time plants is considered. The problem is: given a real number $\gamma > 0$, find (if one exists) an internally stabilizing controller such that the closed-loop operator norm is less than $\gamma $. Under rather weak assumptions on the plant model, it is shown that a solution to this problem exists if and only if a pair of matrix Riccati differential equations admits positive semidefinite stabilizing solutions. State-space formulae for one solution to the problem are also given.
An Ensemble Learning-based Short-Term Load Forecasting on Small Datasets

Short-term load forecasting (STLF) is an important foundation for the electrical network automation and intelligentization. Classic time series methods such as autoregressive integrated moving average (ARIMA)-based methods, and fashionable data mining technologies such as deep learning are not suitable for STLF on the small datasets with a number of attributes. Therefore, in order to tackle this problem, a novel ensemble formulation is proposed to model the load series in this paper. Specifically, the ensemble formulation decomposes the load consumption into two parts, i.e., the extrinsic-variational component (E-com) described by the external factors, and the intrinsic-stationary component (I-com) representing the internal structure of the series itself. Thereafter, a boosting regression learner consists of several simple learners, is further developed to model the E-com and I-com. By localizing the global attributes, the extraction of E-com is achieved by mining the relationships between load patterns and the corresponding local attributes, while I-com is naturally learned by the classic time series methods. Experimental results show that the proposed method improves the accuracy of STLF on small datasets, as compared to the existing approaches in the literature.
Fixed points of asymptotically regular multivalued mappings

Abstract Some results on fixed point of asymptotically regular multivalued mapping are obtained in metric spaces. The structure of common fixed points and coincidence points of a pair of compatible multivalued mappings is also discussed. Our work generalizes known results of Aubin and Siegel, Dube, Dube and Singh, Hardy and Rogers, Hu, Iseki, Jungck, Kaneko, Nadler, Ray and Shiau, Tan and Wong.
Ultraluminous Infrared Galaxies: Mergers of Sub-L* Galaxies?

A sample of 27 low-redshift, mostly cool, ultraluminous infrared galaxies (ULIRGs) has been imaged at 1.6 μm with the Hubble Space Telescope (HST) Near-Infrared Camera and Multi-Object Spectrometer (NICMOS). The majority (67%) of the sample's galaxies are multiple-nucleus galaxies with projected separations of up to 17 kpc, and the rest of the sample (33%) are single-nucleus galaxies, as determined by the NICMOS angular resolution limit. The average observed, integrated (host+nucleus) H magnitude of our HST H sample ULIRGs is -24.3, slightly above that of an L* galaxy (MH = -24.2), and 52% of the sample's galaxies have sub-L* luminosities. The ULIRGs in the HST H sample are not generated as a result of the merging of two luminous (i.e., ≥L*) spiral galaxies. Instead, the interactions and mergers occur in general between two, or in some cases more, less massive sub-L* (0.3-0.5L*) galaxies. Only one out of the 49 nuclei identified in the entire HST H sample has the properties of a bright quasar-like nucleus. On average, the brightest nuclei in the HST H sample galaxies (i.e., cool ULIRGs) are 1.2 mag fainter than warm ULIRGs and low-luminosity Bright Quasar Survey quasars (BQS QSOs) and 2.6 mag fainter than high-luminosity BQS QSOs. Since the progenitor galaxies involved in the merger are sub-L* galaxies, the mass of the central black hole in these ULIRGs would be only about (1-2) × 107 M☉, if the bulge-to-black hole mass ratio of nearby galaxies holds for ULIRGs. The estimated mass of the central black hole is similar to that of nearby Seyfert 2 galaxies but at least 1 order of magnitude lower than the massive black holes thought to be located at the center of high-luminosity QSOs. Massive nuclear starbursts with constant star formation rates of 10-40 M☉ yr-1 could contribute significantly to the nuclear H-band flux and are consistent with the observed nuclear H-band magnitudes of the ULIRGs in the HST H sample. An evolutionary merging scenario is proposed for the generation of the different types of ULIRGs and QSOs on the basis of the masses of the progenitors involved in the merging process. According to this scenario, cool ULIRGs would be the end product of the merging of two or more low-mass (0.3L*-0.5L*) disk galaxies. Warm ULIRGs and low-luminosity QSOs would be generated by a merger involving intermediate-mass (0.5 < L < L*) spirals, or one L* spiral with a less massive companion. High-luminosity QSOs would be the end point in the merging process of massive (>L*) disk galaxies. Under this scenario, warm ULIRGs could still be the dust-enshrouded phases of UV-bright low-luminosity QSOs, but cool ULIRGs, which are most ULIRGs, would not evolve into QSOs.
Detecting Diabetic Autonomic Neuropathy from Electronic Health Records Using Machine Learning

Diabetes is a disease that affects a large number of people worldwide, and diabetic neuropathy is one of its most common and serious complications. Diabetic autonomic neuropathy (DAN) is a type of diabetic neuropathy that is defined as a disorder of the autonomous nervous system and can affect various organs in the body, including the heart and kidney. DAN is widely under-diagnosed due to reasons such as the cost and unavailability of testing equipment, the difficulty of performing cardiovascular tests, and the oftentimes asymptomatic state of the disease in its early stages. However, a late diagnosis can lead to dangerous health complications in the long run. As such, this paper aims to use machine learning to detect DAN in the kidney and heart in diabetic patients by retrieving their information from electronic health records. For this purpose, a dataset of 1275 patient records was used with a variety of traditional machine learning and deep learning algorithms. The best performing model was TabNet with an F1 score of 85.82 for the heart and 73.37 for the kidney.
Improving the Performance of Mutation-based Fault Localization via Mutant Bias Practical Experience Report

Mutation-Based Fault Localization (MBFL) is one of the most widely studied techniques. MBFL adopts mutation analysis to generate mutants for revealing potential faults in the program. Previous studies proposed approaches to optimize MBFL in terms of efficiency and accuracy. However, these approaches ignored the difference of mutants on correct entities (such as statements) and faulty entities, which we refer this kind of difference as mutant bias. In this study, we identify and analyze the impact of mutant bias on MBFL. We find that the mutant bias may introduce effects to statement suspiciousness and negatively influence the fault localization accuracy of MBFL. To mitigate the mutant bias, we propose Delta4Ms, a model that captures the mutant bias from the mutants of the same statements. Then the real suspiciousness is obtained by removing the bias from the practical suspiciousness. To evaluate the performance of our proposed method, we conduct experimental studies on 320 real-world programs from Codeflaws. The experimental results show that Delta4Ms improves the fault localization accuracy of MBFL. Besides, Delta4Ms outperforms the state-of-the-art SBFL and three MBFL techniques significantly. Moreover, Delta4Ms ranks 94 and 161 of the target faults within the top-5 suspicious statements in single-fault and multiple-fault programs, respectively.
Design and Evaluation of Instrument Sound Identification Difficulty for the Deaf and Hard-of Hearing

This study investigates the level of difficulty in identifying listening to 16 different instrumental sounds in moderate to severe deaf and hard-of-hearing (DHH) subjects. Previous studies have suggested that DHH listeners' instrument identification difficulty level depends on the instrument group. Based on these findings, we hypothesized that timbre identification for similar instruments (e.g., two plucked string instruments) would be more difficult than that between dissimilar instruments (e.g., a plucked string instrument and a woodwind instrument). Based on this hypothesis, we designed the ISID, which is the difficulty of combining the sounds of four alternative instruments that comprise a group of instruments based on timbre similarity. In this study, we report the results of an instrumental timbre identification test developed using the ISID, which was administered to 20 patients with moderate to severe DHH, compare correct responses and the response times for each level of the ISID, and analyze and verify whether the identification of instrumental timbres exhibited any differences. The results show significant differences in the three levels of ISID based on the correct response rate and response time, thereby supporting the hypothesis.
Parasitic Capacitance Calculation by Electric Field Decomposition Method for Electric Motor

This study deals with calculating parasitic capacitances of electric motors by considering the fringing effects of the electric fields. The parasitic capacitance of the electric motor is an undesired factor, and in fact, the parasitic capacitance causes problems such as shaft voltage. The conformal mapping was used to facilitate the calculation of parasitic capacitances between active parts inside the electric motor, and parasitic capacitances were calculated using the electric field decomposition method. The results of these studies were analyzed and verified through numerical simulations.
Going through the Life Cycle of Faults in Clouds: Guidelines on Fault Handling

Faults are the primary culprits of breaking the high availability of cloud systems, even leading to costly outages. As the scale and complexity of clouds increase, it becomes extraordinarily difficult to understand, detect and diagnose faults. During outages, engineers record the detailed information of the whole life cycle of faults (i.e., fault occurrence, fault detection, fault identification, and fault mitigation) in the form of postmortems. In this paper, we conduct a quantitative and qualitative study on 354 public post-mortems collected in three popular large-scale clouds, 97.7% of which spans from 2015 to 2021. By reviewing and analyzing post-mortems, we go through the life cycle of faults in clouds and obtain 10 major findings. Based on these findings, we further reach a series of actionable guidelines for better fault handling.