Miracle-dz/UniFAR-FTUs huggingface dataset:
Results of the WMT17 Metrics Shared Task

This paper presents the results of the WMT17 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT17 news translation task and Neural MT training task. We collected scores of 14 metrics from 8 research groups. In addition to that, we computed scores of 7 standard metrics (BLEU, SentBLEU, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system-level correlation (how well each metric’s scores correlate with WMT17 official manual ranking of systems) and in terms of segment level correlation (how often a metric agrees with humans in judging the quality of a particular sentence). This year, we build upon two types of manual judgements: direct assessment (DA) and HUME manual semantic judgements.
Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism

Parkinson’s disease (PD) is characterized by motor impairments caused by degeneration of dopamine neurons in the substantia nigra pars compacta. In addition to these symptoms, PD patients often suffer from non-motor co-morbidities including sleep and psychiatric disturbances, which are thought to depend on concomitant alterations of serotonergic and noradrenergic transmission. A primary locus of serotonergic neurons is the dorsal raphe nucleus (DRN), providing brain-wide serotonergic input. Here, we identified electrophysiological and morphological parameters to classify serotonergic and dopaminergic neurons in the murine DRN under control conditions and in a PD model, following striatal injection of the catecholamine toxin, 6-hydroxydopamine (6-OHDA). Electrical and morphological properties of both neuronal populations were altered by 6-OHDA. In serotonergic neurons, most changes were reversed when 6-OHDA was injected in combination with desipramine, a noradrenaline reuptake inhibitor, protecting the noradrenergic terminals. Our results show that the depletion of both noradrenaline and dopamine in the 6-OHDA mouse model causes changes in the DRN neural circuitry.
House dust mite allergy: complete removal of the provoking allergen is a primary therapeutic approach *

Recently, Gøtzsche and Johansen published an updated systematic review on the effect of house dust mite avoidance measures in mite-sensitive individuals with asthma (1, 2). In their two meta-analyses of randomized controlled trials, peak flow measurements, asthma symptom scores, and medication usage showed no significant improvement in the intervention group. The authors concluded that chemical and physical methods aimed at reducing exposure to house dust mite allergens cannot be recommended. Unfortunately, they rely on the intervention as such, irrespective of a proven allergen-diminishing effect. As Gøtzsche and Johansen stated, in only 18 trials a reduction of house dust mite exposure was achieved, while 29 studies failed to demonstrate a reduced mite allergen load and a further 10 reports did not comment on this point. The fact that a stratified analysis of trials with and without effective house dust mite allergen reduction is not included is surely an omission on the part of this review, which cannot really be justified by the lack of a gold standard for measuring mite allergen exposure. Three further aspects arise when evaluating the included studies with large populations:
A Review of Alzheimer’s Disease and Emerging Patient Support Systems

Alzheimer's disease (AD) is the main and most common source of brain ill health and neurocognitive disorders. Dementia accounts for 80% of AD cases. This disease is considered one of the biggest challenges facing social organizations and medical care providers at present, whether they are within these organizations or at home. The most worrying problems facing the world are the significant rise in the prevalence of dementia, especially for those over the age of 65, with no treatment for this disease. Consequently, healthcare costs have increased steadily, which puts a burden on healthcare systems and puts pressure on families. Researchers have estimated that by 2050, there would be 152 million individuals worldwide living with dementia. The cost of dementia care is expected to exceed $1 trillion. Therefore, it is important for these patients to try to do what they can do for themselves, such as going out without a company and taking medicine on time for as long as possible. The aim of this study is to assist Alzheimer's disease patients to slow cognitive degradation and reduce the burden of care and psychological pressure on families and caregivers. The research reviews the existing studies into AD prevention, progression, planning, and relevant factors for risk reduction. Additionally, the study investigates assistance systems through computerized cognitive assessment, using technology tools and applications dealing with health information recording, drug monitoring, location, and behavior tracking.
Detection of the Soluble Solid Contents from Fresh Jujubes during Different Maturation Periods Using NIR Hyperspectral Imaging and an Artificial Bee Colony

To perform accurate and synchronous detection of the soluble solid contents (SSC) in fresh jujubes at different stages of maturity, hyperspectral imaging was used to establish robust models. The combined data constituting four maturation stages were used to build the grid-search least squares support vector machine (GS-LS-SVM) model. The determination coefficient (Rp2), the root-mean-square error (RMSEP), and the residual predictive deviation (RPD) of the prediction set for samples of the overall stages were 0.98, 1.10%, and 7.85, respectively. Furthermore, a successive projections algorithm (SPA) was used to extract the characteristic wavelengths of the combined data. An artificial bee colony (ABC) algorithm (for the prediction set, Rp2 = 0.98, RMSEP = 1.19%, RPD = 7.25) was used to improve the SPA-LS-SVM model, which was better than the SPA-GS-LS-SVM model (for the prediction set, Rp2 = 0.98, RMSEP = 1.24%, RPD = 6.96). Lastly, visualization of the SSC distribution map was performed based on the SPA-ABC-LS-SVM model, which clearly showed that the SSC gradually increased during maturation. The results indicated that it was realistic to construct a detection model of the multimaturity stage. This research also demonstrated that the combination of hyperspectral imaging and the ABC had good application values in the testing of agricultural products.
Holocenske paleoekološke in paleohidrološke razmere na Ljubljanskem barju ― prispevek k diskusiji

The Ljubljansko barje is a remarkable landscape. Due to wet hydrological conditions the palaeoecological record and archaeological sites in the area are very well preserved. Yet, it is also a very demanding landscape for studying the past environment. The environmental changes and vegetation development throughout the Holocene were very dynamic, which led to changeable taphonomic conditions and, because of the complex hydrological network and human impact (peat cutting), the palaeoecological record is often fragmented. Past hydrological conditions at Ljubljansko barje are a very important topic for archaeologists and palaeoecologists alike and in the last few years there has been an intensive debate about whether Ljubljansko barje was covered by a floodplain or a lake. However, despite intensive research in the area in the last decades, no detailed study of palaeohydrology was carried out. With this paper I would like to join the discussion and present my opinion regarding past hydrological conditions in the area. In my opinion this question is too complex to limit ourselves to the "lake or floodplain? " dilemma. In most studies to date of the past hydrology, the interpretation that has been suggested was too ambitious, because it was based on insufficient data, no matter whether researchers were presenting their own data or interpreting other researcher's results.
Motor Vehicle Crash Detection Using Yolov8 Algorithm

In recent years, the number of motor vehicle accidents has been increasing, causing significant morbidity and mortality worldwide. This study proposes a machine learning model for detecting motor vehicle accidents using the YOLOv8 algorithm to address this issue. The proposed model analyzes video footage captured by cameras installed on roadside infrastructure to detect vehicle accidents. The study uses a publicly available dataset of vehicle accident videos to train and evaluate the model based on the four classifications: 2wheel accident, 4-wheel accident, and large vehicle accident. The model’s performance is evaluated using evaluation metrics such as precision, recall, and mean average precision. The developed model, with a precision of 92.4% and a recall of 78.1%, demonstrates notable potential for real-world applications. The model also achieved a Mean Average Precision (mAP) of 87.6% at IoU 0.5, ensuring a high level of accuracy in identifying accidents promptly.
Study on Transfer Characteristic Hysteresis of Carbon Nanotube Thin Film Transistor

Carbon nanotube thin film transistor (CNT TFT) fabricated by solution-processed method shows hysteresis effect in the transfer characteristic test. In order to investigate the cause of hysteresis, a back-gated CNT TFT was fabricated by using a drop coating of the CNT toluene solution with a normal aromatic hydrocarbon polymer(PFO-BPy). Although there have been slight changes in the experimental materials and environmental conditions, it does not affect the test results of the electrical characteristics of carbon nanotube thin film transistors. We carried out measurement of current-voltage characteristic at various test conditions, including scanning order, scanning range, scanning speed, and vacuum environment. Moreover, device fabrication followed by surface passivation was conducted to isolate CNT network from the ambient. The results strongly suggest that, the hysteresis of solution-processed CNT TFT is generally caused by absorption of water molecules on the surface of CNT. Furthermore, surface passivation is a prospective method for suppressing hysteresis of CNT TFT made by using solution process.
Angler: Dark Pool Resource Allocation

Demand for distributed computational infrastructure is growing in order to offer low latency connections to end users. The fragmenting infrastructure complicates the resource allocation process. As the number of infrastructure providers grows, points of presence are resource constrained compared to the cloud, they have diverse availability profiles, and diverse connectivity properties. Existing resource allocation approaches require providers share intimate details about their infrastructure to support the placement process, or rely on third party aggregators. Such solutions introduce strong assumptions of trust and collaboration. In this work we present Angler, the first system to allocate resources from dark pools, meaning the capacity and requests of the distributed pool of resources are unknown. Angler leverages cryptographic protocols for secure function evaluation, namely the WRK secure multiparty computation (MPC) protocol [76]. While MPC protocols can have large overheads compared to plaintext function evaluation, an end-to-end approach to the system design subverts the expensive overheads. Specifically, Angler combines a tuned implementation of a maliciously secure MPC protocol, a tailored distributed hash table, and a systematic effort to make the best allocation decision within a response time envelope. Angler is only 2x slower than resource allocation with no privacy when arbitrating among 8 providers, taking less than a second.
Employment and Investigation of Various CNN Models and Datasets for Facial Expression Recognition and Classification

Facial expression recognition (FER) has become a popular research topic serving enhancement of human-computer interaction. Facial expressions are valuable sources of information, especially for identifying emotions. Therefore, classification of emotions is crucial for computers to interpret our reactions. Machine learning algorithms can be employed for solving facial expression classification problems. Among the deep learning algorithms, the convolutional neural network (CNN) architectures are known to be especially suitable for such tasks. In this study, we trained CNN architectures of VGG16, VGG19, and ResNet50 by employing FER2013, FER2013plus datasets, and a subset of AffectNet dataset composed of images of facial expressions. We have used transfer learning to incorporate network models previously trained via ImageNet dataset. We have investigated the impact of various datasets, network models, and parameters on the performance of a multi-class facial emotion classification problem. Due to dataset imbalances, certain data augmentation techniques have also been utilized to increase accuracy.
The effect of purification on Snail (Achatina fulica) cellulase enzyme characteristic

Snail is an organism that able to produce cellulase enzyme due to its ability to use cellulose for energy source. The purposes of this study were to obtain best concentration of ammonium sulphate in increasing cellulase enzyme activity, optimum temperature, and cellulase enzyme specific substrate from snail. The snail cellulase enzyme was concentrated using ammonium sulphate. The study was designed as Completely Randomized Design consisting seven treatments (30-90% of ammonium sulphate) and three replicates. The result showed that the activity of crude cellulase enzyme was 0,123 U/ml. The optimum cellulose enzyme activity was at 50% ammonium sulphate as 0,609 U/ml and the specific activity was 0,970 U/mg. Optimum temperature of the concentrated cellulose enzyme was at 50°C and the highest activity was in CMC substrate at 0,396 U/ml.
ESD-Ship: An Fast and Accurate SAR Ship Detection Method

To address the issue of low accuracy and lengthy processing time in synthetic aperture radar (SAR) ship detection based on deep learning, this paper proposes an improved ship detection method based on YOLOv7 (You Only Look Once). Building upon the YOLOv7 algorithm, the overall feature learning architecture is simplified by removing unnecessary large target feature processing structures. Subsequently, a downsampling module with enhanced feature extraction capability is constructed by integrating the RepVGGBlock. Finally, the traditional NMS time-consuming and inaccurate positioning issues are optimized by combining Weighted Non Maximum Suppression (NMS) with Cluster-NMS. Compared to the original algorithm, the proposed method achieves a substantial improvement on the HRSID dataset, with the mean average precision (mAP) increasing from 91.8% to 94.9% and FPS rising from 38.3 to 46.3, outperforming the control significantly.
Learn-to-Respond: Sequence-Predictive Recovery from Sensor Attacks in Cyber-Physical Systems

While many research efforts on Cyber-Physical System (CPS) security are devoted to attack detection, how to respond to the detected attacks receives little attention. Attack response is essential since serious consequences can be caused if CPS continues to act on the compromised data by the attacks. In this work, we aim at the response to sensor attacks and adapt machine learning techniques to recover CPSs from such attacks. There are, however, several major challenges. i) Cumulative error. Recovery needs to estimate the current state of a physical system (e.g., the speed of a vehicle) in order to know if the system has been driven to a certain state. However, the estimation error accumulates over time in presence of compromised sensors. ii) Timely response. A fast response is needed since slow recovery not only comes with large estimation errors but also may be too late to avoid irreparable consequences. To address these challenges, we propose a novel learning-based solution, named sequence-predictive recovery (or SeqRec). To reduce the estimation error, SeqRec designs the first sequence-to-sequence (Seq2Seq) model to uncover the temporal and spatial dependencies among sensors and control demands, and then uses the model to estimate system states using the trustworthy data logged in history. To achieve an adequate and fast recovery, SeqRec designs the second Seq2Seq model that considers both the current time step using the remaining intact sensors and the future time steps based on a given target state, and embeds the model into a novel recovery control algorithm to drive a physical system back to that state. Experimental results demonstrate that SeqRec can effectively and efficiently recover CPSs from sensor attacks.
Cryptococcal Meningoencephalitis in a Patient with Hyper IgM Syndrome Due to CD40 Deficiency: Case Report and Literature Review

Hyper-IgM syndrome (HIGM) due to CD40 deficiency is a very rare form of combined immunodeficiency with increased susceptibility to opportunistic infections. Cryptococcus is an opportunistic infection usually affecting immunocompromised individuals. This is the first report to describe a patient with HIGM due to CD40 deficiency presenting with meningoencephalitis secondary to Cryptococcus infection.
Simulation of Tip Defect Discharge Process under the Influence of Multiple Physical Fields

Partial discharge can pose a serious threat to electrical device’s insulation effectiveness. Monitoring partial discharge is crucial for the operation and maintenance of electrical equipment, and streamer discharge is a predominant form of gas discharge is observed within the high-voltage field. and a key issue in partial discharge research. Meanwhile, due to the complex and diverse operating environment of power equipment, they will be exposed to different external environments such as humidity and air pressure during long-term operation. Therefore, exploring the impact of different external conditions on the insulation defect discharge process is of great significance for accurately evaluating partial discharge data. This paper examines the research status regarding the impact of external parameters on the discharge process both domestically and internationally. Based on the hydrodynamic model, a streamer discharge simulation model with tip defects is built in the COMSOL environment. Using the above hydrodynamic model, how external parameters influence the streamer discharge process with tip gap of 3mm is studied, and the method of changing the physical fields such as air pressure and humidity in the hydrodynamic model is studied. The simulation results show that a decrease in air pressure will accelerate the propagation speed of the streamer, slightly decrease the maximum electric field intensity at the streamer head, and exacerbate the ionization degree of the streamer discharge; An increase in humidity will accelerate the propagation speed of the streamer, and the impact on the maximum electric field intensity of the streamer head is not significant, which will exacerbate the ionization degree of the streamer discharge.
Rainfall Variability and Trend Analysis of Annual Rainfall in North Africa

The IPCC climate models predict, for the Maghreb countries, lower rainfall and increased aridity. Current observations in the three countries of central Maghreb (Morocco, Algeria, and Tunisia) are not consistent with these predictions. To demonstrate this new trend, a detailed regional analysis of rainfall evolution is conducted. This investigation is based on the calculation of the reduced centered index and the chronological graphical method of processing information (MGCTI) of “Bertin matrix” type. The results show extreme variability of this parameter and the severe past drought (more intense for Morocco, in which the drastic conditions from the seventies are observed). The results also show the beginning of a gradual return to wetter conditions since the early 2000s in Algeria and Tunisia and from 2008 for Morocco (this trend is confirmed by recent agricultural production data in 2011/2012 and 2012/2013).
Pyramid Feature Iterative Fusion: A Cross-Scale Fusion Algorithm for Enhanced Analysis of H&E Images in HER2-Positive Breast Cancer

For HER2-positive breast cancer patients, the presence of distant metastasis is a crucial prognostic element. Early detection can notably assist in devising treatment strategies, thereby improving patient prognosis. Pathological Hematoxylin and Eosin (H&E) whole-slide images (WSIs) provide a rich array of prognosis-related histological features. By conducting an exhaustive multi-scale analysis, a hierarchical model embodying core tumor trait can be established. Currently, prevalent techniques in multi-scale H&E image analysis entail concatenating feature vectors and applying attention mechanisms to calculate feature correlations. Nonetheless, due to insufficient cross-scale correlation constraints, these methods might generate redundant information. This paper introduces Pyramid Feature Iterative Fusion (PFIF), a groundbreaking cross-scale fusion algorithm for H&E images. PFIF resolves the issues of redundancy and reduces noise following fusion. By integrating a backward feature fusion module, it emulates human evaluation of H&E images across scales. It amalgamates structural semantic features from a low-magnification view with texture features from a high-magnification view, thereby conveying key complementary details across resolutions. Training this model with H&E image data from Chongqing University’s Cancer Hospital resulted in an AUC rate improvement of approximately 3.69.
Microwave Photonic Interferometric System for Monitoring Delay and Temperature of Optical Cables deployed in Radio Telescope Arrays

The on-field validation of a fiber delay monitoring system (FDMS) based on a microwave Michelson’s interferometer operating over optical fiber is presented. The FDMS has been designed for monitoring the impact of optical cables installed in radioastronomy applications. The system was tested in the second version of the Aperture Array Verification System (AAVS), which represents the prototype for the upcoming low frequency radioastronomy plant Square Kilometre Array (SKA-Low), installed on site at the Murchison Radio Observatory, in Western Australia. The FDMS has been used to monitor two different typologies of cable installation: Surface Laid and Underground Buried. This work shows that the behavior of the delay measurement agrees with the measured average temperature of the cable, which depends on several aspects other than the air temperature. The FDMS can be used also to estimate the average temperature effects of the fiber optic cables.
The MFS for Electromagnetic Scattering by a Layered Chiral Object

The scattering of time-harmonic electromagnetic waves by a layered chiral obstacle is considered. The direct scattering problem is formulated. Surface vector potentials in terms of the dyadic fundamental solutions of the differential operators governing the problem are defined. The Method of Fundamental Solutions (MFS) is applied in order to approximate the sought for solution by finite linear combinations in terms of the dyadic fundamental solutions for singularities placed on auxiliary surfaces outside the domain of the solution and for unknown vector coefficients determined by the conditions on the scatterer’s surfaces. A system of vector functions containing the tangential components of the columns of the dyadic fundamental solutions is constructed. Completeness and linear independence of the constructed system is proven on the surfaces of the scatterer using the defined surface vector potentials. It is concluded that the approximation of the solution by the finite linear combinations is reduced to the approximation of the boundary data. The corresponding MFS approximations of the far-field data are constructed. Imposing the transmission conditions to a finite number of collocation points on the surfaces of the scatterer transforms the problem to an algebraic linear system whose solution determines the unknown vector coefficients. Numerical results obtained by the implementation of the method are presented for the case of a two-layered chiral sphere embedded in a chiral medium.