Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Structural Representations via Dynamic Object Landmarks Discovery for Sketch Recognition and Retrieval

State-of-the-art methods on sketch classification and retrieval are based on deep convolutional neural network to learn representations. Although deep neural networks have the ability to model images with hierarchical representations by convolution kernels, they cannot automatically extract the structural representations of object categories in a human-perceptible way. Furthermore, sketch images usually have large-scale visual variations caused by the styles of drawing or viewpoints, which make it difficult to develop generalized representations using the fixed computational mode of convolutional kernel. In this paper, our aim is to address the problem of fixed computational mode in feature extraction process without extra supervision. We propose a novel architecture to dynamically discover the object landmarks and learn the discriminative structural representations. Our model is composed of two components: a representative landmark discovering module that localizes the key points on the object and a category-aware representation learning module that develops the category-specific features. Specifically, we develop a structure-aware offset layer to dynamically localize the representative landmarks, which is optimized based on the category labels without extra supervision. After that, a diversity branch is introduced to extract the global discriminative features for each category. Finally, we employ a multi-task loss function to develop an end-to-end trainable architecture. At testing time, we fuse all the predictions with different number of landmarks to achieve the final results. Through extensive experiments, we compare our model with several state-of-the-art methods on two challenging datasets, TU-Berlin and Sketchy, for sketch classification and retrieval, and the experimental results demonstrate the effectiveness of our proposed model.
Proteomics; applications in familial Parkinson's disease

Our understanding of the biological basis of Parkinson's disease (PD) has been greatly improved in recent years by the identification of mutations that lead to inherited PD. One of the strengths of using genetics to try to understand disease biology is that it is inherently unbiased and can be applied at a genome‐wide scale. More recently, many studies have used another set of unbiased approaches, proteomics, to query the function of familial PD genes in a variety of contexts. We will discuss some specific examples, including; elucidation of protein‐protein interaction networks for two dominantly inherited genes, α‐synuclein and leucine rich‐repeat kinase 2 (LRRK2); the identification of substrates for three genes for familial PD that are also enzymes, namely LRRK2, pink1, and parkin; and changes in protein abundance that arise downstream to introduction of mutations associated with familial PD. We will also discuss those situations where we can integrate multiple proteomics approaches to nominate deeper networks of inter‐related events that outline pathways relevant to inherited PD.
BART: A Modular Toolkit for Coreference Resolution

Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort, yet there is very limited availability of off-the shelf tools for researchers whose interests are not in coreference, or for researchers who want to concentrate on a specific aspect of the problem. We present BART, a highly modular toolkit for developing coreference applications. In the Johns Hopkins workshop on using lexical and encyclopedic knowledge for entity disambiguation, the toolkit was used to extend a reimplementation of the Soon et al. (2001) proposal with a variety of additional syntactic and knowledge-based features, and experiment with alternative resolution processes, preprocessing tools, and classifiers.
FORECAST-CLSTM: A New Convolutional LSTM Network for Cloudage Nowcasting

With the highly demand of large-scale and real-time weather service for public, a refinement of short-time cloudage prediction has become an essential part of the weather forecast productions. To provide a weather-service-compliant cloudage nowcasting, in this paper, we propose a novel hierarchical Convolutional Long-Short-Term Memory network based deep learning model, which we term as FORECAST-CLSTM, with a new Forecaster loss function to predict the future satellite cloud images. The model is designed to fuse multi-scale features in the hierarchical network structure to predict the pixel value and the morphological movement of the cloudage simultaneously. We also collect about 40K infrared satellite nephograms and create a large-scale Satellite Cloudage Map Dataset(SCMD). The proposed FORECAST-CLSTM model is shown to achieve better prediction performance compared with the state-of-the-art ConvLSTM model and the proposed Forecaster Loss Function is also demonstrated to retain the uncertainty of the real atmosphere condition better than conventional loss function.
Effects of Reverberation on the Relation Between Compression Speed and Working Memory for Speech-in-Noise Perception.

OBJECTIVES Previous study has suggested that when listening in modulated noise, individuals benefit from different wide dynamic range compression (WDRC) speeds depending on their working memory ability. Reverberation reduces the modulation depth of signals and may impact the relation between WDRC speed and working memory. The purpose of this study was to examine this relation across a range of reverberant conditions. DESIGN Twenty-eight older listeners with mild-to-moderate sensorineural hearing impairment were recruited in the present study. Individual working memory was measured using a Reading Span test. Sentences were combined with noise at two signal to noise ratios (2 and 5 dB SNR), and reverberation was simulated at a range of reverberation times (0.00, 0.75, 1.50, and 3.00 sec). Speech intelligibility was measured in listeners when listening to the sentences processed with simulated fast-acting and slow-acting WDRC conditions. RESULTS There was a significant relation between WDRC speed and working memory with minimal or no reverberation. Consistent with previous research, this relation was such that individuals with high working memory had higher speech intelligibility with fast-acting WDRC, and individuals with low working memory performed better with slow-acting WDRC. However, at longer reverberation times, there was no relation between WDRC speed and working memory. CONCLUSIONS Consistent with previous studies, results suggest that there is an advantage of tailoring WDRC speed based on an individual's working memory under anechoic conditions. However, the present results further suggest that there may not be such a benefit in reverberant listening environments due to reduction in signal modulation.
A fuzzy decision based WSN localization algorithm for wise healthcare

Wise healthcare is a typical application of wireless sensor network (WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center, and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced, which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength (RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.
The GNOME project: a case study of open source, global software development

Many successful free/open source software (FOSS) projects start with the premise that their contributors are rarely colocated, and as a consequence, these projects are cases of global software development (GSD). This article describes how the GNOME Project, a large FOSS project, has tried to overcome the disadvantages of GSD. The main goal of GNOME is to create a GUI desktop for Unix systems, and encompasses close to two million lines of code. More than 500 individuals (distributed across the world) have contributed to the project. This article also describes the software development methods and practices used by the members of the project, and its organizational structure. The article ends by proposing a list of practices that could benefit other global software development projects, both FOSS and commercial. Copyright © 2004 John Wiley & Sons, Ltd.
Adaptive Neuro-Fuzzy Inference System Models for Force Prediction of a Mechatronic Flexible Structure

This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped with acceleration transducers at each degree of freedom and a force transducer for validation and training. Two types of models were developed; the first type uses current accelerations only while the second type considers both the current accelerations and the historical values. The models are trained using data obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error. The frequency domain is considered to analyze the similarity of the frequencies in the predicted and the original force signal. For a selection of the best models, a more advanced performance analysis is carried out. This includes application of the trained models to deterministic and non- deterministic excitation forces with different excitation frequencies and amplitudes. Additionally, the influence of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that ANFIS models can be used to set up reliable force predictors for dynamical loaded flexible structures, when a certain degree of inaccuracy is accepted. Furthermore, the comparison study points out that the transducer location is crucial for the model performance. However, there exists no general solution for the final selection of models. The findings showed that the model type employing historical values gives better predictions when operating in their trained regions while the models using only current values have generally higher prediction errors in trained regions but are less sensitive to changes of the system dynamics history.
Exploration of Compact Sequence Diagram Using Hyperlink

The objective of this work is about the activation of a hyperlink, which is in a compacted portion, that is accessed and enters a node by a single click of any user. This is achieved only by a tool called JIVE. It provides the visualization of any big program in a most understandable and simple way for any program execution. JIVE automatically generates two diagrams called object and sequence diagram which gives the current execution state and history respectively. In a compact sequence diagram, one may want to explore compact portions of sequence diagram which is controlled yet. Hence this paper proposes exploration of a compact portions using hyperlinks. A hyperlink diagram is required for zooming into a compact portion. Exploration in these compact portions would make the user understand about the method calls used inside the loop.
A Configurable Memristor-based Finite Impulse Response Filter

There are two main methods to implement FIR filters: software and hardware. In the software method, an FIR filter can be implemented within the processor by programming; it uses too much memory and it is extremely time-consuming while it gives the design more configurability. In most hardware-based implementations of FIR filters, Analog-to-Digital (A/D) and Digital-to-Analog (D/A) converters are mandatory and increase the cost. The most important advantage of hardware implementation of a FIR filter is its higher speed compared to its software counterpart. In this work, considering the advantages of software and hardware approaches, a method to implement direct form FIR filters using analog components and memristors is proposed. Not only the A/D and D/A converters are omitted, but also using memristors avails configurability. A new circuit is presented to handle negative coefficients of the filter and memristance values are calculated using a heuristic method in order to achieve a better accuracy in setting coefficients. Moreover, an appropriate sample and delay topology is employed which overcomes the limitations of the previous research in implementation of high-order filters. Proper operation and usefulness of the proposed structures are all validated via simulation in Cadence.
DNA Barcoding as a Diagnostic Tool of a Rare Human Parasitosis: The First Case of Lagochilascaris minor in Quintana Roo, Mexico

ABSTRACT Recently, DNA barcoding based on the mitochondrial gene cytochrome c oxidase subunit 1 (COI) has become a widespread tool to identify animals. Its use with parasites of humans has been limited with some groups of nematodes where the amplification of this gene has been difficult. In this study, we present the first COI barcode sequence of a rare parasite from tropical regions, Lagochilascaris minor, which parasitized a human host from Quintana Roo, southern Yucatán Peninsula, Mexico. Destruction of the mastoid apophysis in the lateral sinus and cerebellar involvement were observed at the site of infection. After a radical mastoidectomy and a treatment with 200 mg oral albendazole for 63 days, the patient completely recovered. Lagochilascaris minor was identified based on the ratio between length of spicules and ejaculatory duct, shape of eggs, and host, as well as comparison with its congeners. The mode of infection is unknown, although it could be after direct exposure to eggs or consumption of uncooked wild meat. Morphology of adults is demonstrated using scanning electron microscopy, and high-quality sequences of COI barcode are presented from amplifications using semi-degenerate primers designed for micro-crustaceans. DNA barcoding proved to be a reliable identification method for L. minor. A comparison of the sequences for this species with 81 ascaridoids obtained from the Barcode of Life Database places it in a unique clade most closely related to Baylisascaris procyonis. Future diagnosis of larval and adult stages of L. minor using DNA barcoding will allow the recognition of its infection parameters, transmission, and precise epidemiology. Reports of lagochilascarosis in the Yucatán Peninsula have been occurred over the last decade, suggesting it is an emerging zoonotic disease in the region.
Development of Micro Weather Station Based on Long Range Radio Using Automatic Packet Reporting System Protocol

Automatic Weather Station is an integrated system of measuring instruments, interfaces, and data processing and transmission capable of collecting weather data. Weather stations data can be crowd-sourced through Citizen Weather Observer Program (CWOP), powered by Automatic Packet Reporting System (APRS). To further increase the performance of the weather station, a LoRa based modem is used as the transceiver modem. This paper proposes an automatic weather station network using a LoRa-based APRS network. Preliminary results show that the prototyped weather station data can be easily integrated to APRS-IS based network infrastructure.
Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.
Attraction-Repulsion clustering with applications to fairness

In the framework of fair learning, we consider clustering methods that avoid or limit the influence of a set of protected attributes, $S$, (race, sex, etc) over the resulting clusters, with the goal of producing a fair clustering. For this, we introduce perturbations to the Euclidean distance that take into account $S$ in a way that resembles attraction-repulsion in charged particles in Physics and results in dissimilarities with an easy interpretation. Cluster analysis based on these dissimilarities penalizes homogeneity of the clusters in the attributes $S$, and leads to an improvement in fairness. We illustrate the use of our procedures with both synthetic and real data.
Enhancing Time-Series Momentum Strategies Using Deep Neural Networks

Although time-series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this article, the authors introduce deep momentum networks—a hybrid approach that injects deep learning–based trading rules into the volatility scaling framework of time-series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimizing the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, the authors demonstrate that the Sharpe-optimized long short-term memory improved traditional methods by more than two times in the absence of transactions costs and continued outperforming when considering transaction costs up to 2–3 bps. To account for more illiquid assets, the authors also propose a turnover regularization term that trains the network to factor in costs at run-time. TOPICS: Statistical methods, simulations, big data/machine learning Key Findings • While time-series momentum strategies have been extensively studied in f inance, common strategies require the explicit specification of a trend estimator and position sizing rule. • In this article, the authors introduce deep momentum networks —a hybrid approach that injects deep learning–based trading rules into the volatility scaling framework of timeseries momentum. • Backtesting on a portfolio of continuous futures contracts, Deep Momentum Networks were shown to outperform traditional methods for transaction costs of up to 2–3 bps, with a turnover regularisation term proposed for more illiquid assets.
Effects of Summer Transhumance of Dairy Cows to Alpine Pastures on Body Condition, Milk Yield and Composition, and Cheese Making Efficiency

Simple Summary Summer transhumance of dairy cows is a seasonal pastoral system practiced in many European countries from ancient times. This practice provides additional forage supply for mountain dairy farms and plays a role in the preservation of landscape, biodiversity, and natural habitats and conservation of local traditional dairy products, but it may affect cows’ physiological and nutritional status. This study aimed to investigate the effects of transhumance of Brown Swiss cows to summer pastures on the yield, composition, and coagulation properties of milk, and on cheese yield. For this study, twelve multiparous cows from a mountain lowland permanent farm were divided into two groups of six cows: One group stayed at the permanent farm while the other moved to the alpine pasture (1860 m above sea level). Cows at the alpine pasture had reduced milk yield and body condition, and greater fat and lower protein contents in milk compared to cows at the permanent farm. Conversely, neither milk coagulation properties nor cheese yield were affected by summer transhumance. In conclusion, summer transhumance did not affect the cheese making efficiency of milk compared to permanent farm, but the negative effect on milk yield depressed daily cheese yield, which was 2 kg/d lower in cows moved to Alpine pasture. Abstract Summer transhumance to alpine pastures (ALP) is widespread in dairy systems of alpine regions. This study aimed to investigate the effects of transhumance of Brown Swiss cows to ALP on the yield, composition, and coagulation properties of milk (MCP), and on cheese yield (CY). The study involved 12 multiparous cows kept at a mountain lowland permanent farm (PF), which were divided into two equal groups: One remained at the PF, the other was moved to the ALP (1860 m above sea level) from July to September. Every month (June to October), daily milk yield (MY) and body condition score (BCS) were recorded, and individual milk samples (n = 60, 2000 mL each) were collected to assess milk composition, MCP, and CY. Compared with PF, ALP cows had a reduced MY and BCS, which was maintained on return to the PF, greater fat and lower protein contents of milk. Neither MCP nor CY were affected by summer transhumance. In conclusion, summer transhumance did not affect the cheese making efficiency of milk but depressed MY and consequently daily cheese yield, which was nearly 2 kg/d lower for the ALP than the PF cows and was only partially recovered after returning to the PF in autumn.