Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully Connected Reproducible SE-UResNet for Multiorgan Chest Radiographs Segmentation

Deep learning (DL) models are a popular choice for resolving intricate issues in medical imaging, such as the classification of diseases, detection of anomalies, and segmentation of tissues in real-world scenarios. To be useful in these contexts, the models must be able to provide accurate results for new, previously untrained data. Existing methods not only fail to consider the intrinsic features of small target lesions but are also not evaluated on separate datasets. To solve these problems we propose a novel architecture, SE-UResNet, capable of segmenting multiple organs having different size and shapes from Chest X-Ray (CXR) images. The proposed architecture introduces a residual module in between the encoding and decoding modules of an attention U-Net architecture for better feature representation of high-level features. The architecture also replaces the attention gates in the decoder module of attention U-Net with Squeeze and Excite (S&E) modules. SE-UResNet is experimented on benchmark CXR datasets such as NIH CXR for lungs, heart, trachea and collarbone segmentation as well as VinDr-RibCXR for ribs segmentation tasks with respect to other state-of-the-art segmentation models. The proposed model achieves an average DSC of 95.9%, 76.8%, 78.7%, 78.8%, and 86.0% for lungs, trachea, heart, collarbone and ribs segmentation for the aforementioned datasets. Furthermore, the proposed model has only been tested on two benchmark CXR datasets: Shenzen and JSRT to establish the reproducibility and robustness of the model. The performance of SE-UResNet on several benchmark CXR datasets demonstrates the model’s ability to generalize, making it a reliable baseline for medical image segmentation. Furthermore, it can also be used for assessing the reproducibility of DL models based on their performance on different datasets.
An Approach to Testing Banking Software Using Metamorphic Relations

Software systems used for banking are crucial for daily operations and are considered to be part of critical infrastructure; however, testing the functions of these highly reusable systems can be difficult due to the project’s complexity and the absence of a reliable oracle. In software testing, the Oracle problem directs to the difficulty of deciding whether the software’s observed behavior is correct. To address this issue, we suggest utilizing metamorphic testing (MT), which tests the banking system’s functionalities based on their properties. Metamorphic testing is a software testing technique where multiple inputs are generated for a program, then those inputs are transformed based on a pre-defined set of rules. The resulting outputs are then compared to the original outputs to verify that the program works correctly. Metamorphic relations (MRs) are a fundamental concept in metamorphic testing. They define the relationships between the input and output of a system under test and specify how they should change in response to input transformations. Through a case study, we introduce new metamorphic relations to test banking functions and demonstrate the effectiveness of using these MRs. The study results indicate that this is a feasible and efficient approach using an alternative to a test oracle when testing complex E-type (i.e., real-world) software.
Weakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network

Most anatomical information and anomalies are provided by chest X-ray (CXR) images and are sometimes adequate for the early diagnosis. However, by observing the radiographs it is a challenging task to recognize multiple occurring thorax diseases. As a result, there is a trend toward developing deep learning systems to assist radiologists. Motivated by this, we propose a generalized weakly supervised Confidence-Aware Probabilistic Class Activation Map (CAPCAM) classification model that localizes anomalies for 13 different thoracic disease. The CAPCAM used CX-Ultranet as the backbone with the combination of Confidence Aware Network (CAN) and Anomaly Detection Network (ADN) which helps the model to utilise all components of feature extracted therefore eliminating the necessity to train them individually and time taken. We experimentally shown that our proposed CAPCAM method sets a new state-of-the-art benchmark by achieving accuracy in terms of Intersection of Bounding Box (IoBB) in the range of 85% - 94%, and F1 scores in the range of 88% - 90% for all thirteen diseases on publicly available large-scale CXR NIH dataset. The proposed CAPCAM pooling also achieves better results than other state of the art (SOTA) pooling methods.
Human-object Behavior Analysis Based on Interaction Feature Generation Algorithm

—Aiming at the problem of insufficient utilization of interactive feature information between human and object, this paper proposes a two-stream human-object behavior analysis network based on interaction feature generation algorithm. The network extracts human-object’s feature information and interactive feature information respectively. When extracting human-object features information, considering that ResNeXt has powerful feature expression ability, the network is used to extract human-object features from images. When extracting interactive features information between human and object, an interaction feature generation algorithm is proposed, which uses the feature reasoning ability of graph convolutional neural networks. A graph model is constructed by taking human and objects as nodes and the interaction between them as edges. According to the interactive feature generation algorithm, the graph model is updated by traversing nodes, and new interactive features are generated during this process. Finally, the humans’ and objects’ features information and the human-object interaction feature information are fused and sent to the classification network for behavior recognition, so as to fully utilize the humans’ and objects’ feature information and the interaction feature information of human-objects. The human-object behavior analysis network is experimentally verified. The results show that the accuracy of the network has been significantly improved on HICO-DET and V-COCO datasets.
EFFECTS OF DORSIFLEXION ON ENERGY EXPENDITURE DURING CROSS·COUNTRY SKIING USING THE V1 SKATE TECHNIQUE

Competitive cross-country skiing involves race events of different distances conducted over natural terrain. The primary objective of the skier is to cover the race distance as quickly as possible. This requires the athlete to achieve a high speed to energy expenditure ratio while maintaining physiological strain within tolerable limits. Thus, the influence of various mechanical techniques of skiing on energy expenditure and physiological strain is of interest to skiers. There has been interest concerning the possible effects of various joint angles on skiing techniques (Smith, 1992). More specifically, ankle flexion seems to play some role in reducing the abovementioned speed to energy expenditure ratio. An increase in dorsiflexion may be reflective of a more forward center of mass position and a degree of force oriented downward and rearward onto the ski. A portion of this force may be propulsive. As a result, increased dorsiflexion may provide lower oxygen demands for a given velocity of movement. The purpose of this study was to examine whether oxygen uptake and energy expenditure vary with different degrees of ankle dorsiflexion during the V1 skating technique. A second objective was to determine if a degree exists where dorsiflexion does not influence energy expenditure during the V1 skating technique.
Fire disasters in a wetlands area monitored by remote sensing data.

ABSTRACT: The aim of the project is to use remote sensing data in order to assess damage caused by fire in a wetland area. There are many satellite systems, which provide information on different fire characteristics: location of burned area, duration of active fire, aerosol emissions, etc. Remote sensing data play a significant role in detection and monitoring fires of wetlands and could be the only tool in getting information about burned area when the access to it is difficult. In autumn 2002, fires occurred at the area of the Biebrza National Park, the largest wetlands in Europe. The fire spread to various sites and affected in total almost 3 000 ha. The largest burn scar was located in the central part of the Biebrza National Park in Biele Suchowolskie. In this area the fire destroyed 1 600 ha of wetlands. Fires of peat bogs are dangerous and difficult to stop as they spread under the surface. The fire destroyed natural vegetation on the surface and the thick layer of peat bog. Images from MODIS/Terra satellite were used to estimate the extent of the disaster and the changes in the environment caused by the fire.
Machine Learning, Human Experts, and the Valuation of Real Assets

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique—neural networks—to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers’ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts’ systematic biases in expectations formation—and identify ex ante situations in which such biases are likely to arise.
Interactions magma-roche, déformation à haute température et anisotropie sismique dans le manteau de la transition continent-océan et dans la lithosphère océanique

Cette these regroupe deux etudes distinctes, qui documentent le controle des microstructures sur les proprietes sismiques des roches. La premiere partie traite du developpement des orientations preferentielles cristallographiques (OPC) dans le manteau superieur, associe aux interactions liquide/magma-roche, enregistre dans des xenolites de peridotites du bassin d'arriere-arc de la mer du Japon. Les caracteristiques microstucturales et geochimiques des echantillons etudies montrent que l'ouverture arriere-arc active est associee a une deformation du manteau superieure similaire a celle observee dans l'ophiolite d'Oman. L'initiation de l'extension d'arriere-arc n'est pas associee a de fortes interactions entre percolation magmatique et deformation, en comparaison avec les zones de rifting continentales, probablement en raison des taille et duree relativement petites de l'episode d'ouverture. La seconde partie presente une base de donnees unique d'OPC de plagioclase de roches mafiques plus ou moins deformees. Les OPC sont classees en 3 types principaux; leurs caracteristiques en fonction du regime de deformation (magmatique ou plastique) sont presentees et discutees. Les proprietes sismiques calculees des roches gabbroiques montrent que l'anisotropie tend a croitre avec l'intensite des fabriques, bien qu'elle soit generalement faible, en raison des effets opposes des olivines/clinopyroxenes et du plagioclase.
Variation in sexual communication and its role in divergence of two host strains of the noctuid moth Spodoptera frugiperda

The fall armyworm, Spodoptera frugiperda , consists of two distinct strains, the corn-and the rice-strain, which exhibit different prezygotic isolation mechanisms and seem to be in the process of ecological speciation in sympatry. Recent studies found that females of both strains showed significant differences in their sex pheromone composition and strain-specific sexual communication is considered as possible prezygotic isolation barrier between both strains. In this study, we investigated the inheritance of strain-specific sex pheromone blends by comparing the sex pheromone of pure strain, hybrid and backcross females. Furthermore, we conducted a QTL analysis and mapped different candidate genes to QTLs involved in the strain-specific production of ( Z )-9-tetradecenyl acetate (Z9-14:OAc), ( Z )-11-hexadecenyl acetate (Z11-16:OAc), ( Z )-7-dodecenyl acetate (Z7-12:OAc), and ( Z )-9-dodecenyl acetate (Z9-12:OAc). We found that all four pheromone compounds were differentially inherited and multiple factors influenced their inheritance. The QTL analysis showed that multiple genomic regions on 9 different chromosomes determined the strain-specific female sex pheromone. For three pheromone compounds, i.e. Z9-14:OAc, Z7-12:OAc and Z9-12:OAc, we found the involvement of one minor QTL each, whereas a total of seven different QTLs were significantly correlated with differential relative amounts of Z11-16:OAc. A delta-11-desaturase ( Sf LPAQ) mapped to one chromosome that explained a significant proportion of the variance in Z9-14:OAc and Z11-16:OAc, and showed the opposite-to-expected phenotypic pattern for both components. Interestingly, the circadian clock gene vrille ( Sf VRI), which appears to be responsible for strain-specific differences in the onset time of mating in the night, mapped to another chromosome that was involved in the
Accurate and Eﬀicient Filtering using Anisotropic Filter Decomposition

: E(cid:30)cient (cid:28)ltering remains an important challenge in computer graphics, particularly when (cid:28)lters are spatially-varying, have large extent, and/or exhibit complex anisotropic pro(cid:28)les. We present an e(cid:30)cient (cid:28)ltering approach for these di(cid:30)cult cases based on an isotropic (cid:28)lter decomposition (IFD). By decomposing complex (cid:28)lters into linear combinations of b simpler, displaced isotropic kernels, and precomputing a compact pre(cid:28)ltered dataset, we are able to interactively apply any number of(cid:22)potentially transformed(cid:22)(cid:28)lters to a signal. Our performance scales linearly with the size of the decomposition s (cid:28) n (i.e. O ( s ) time), not the size n nor the dimensionality of the (cid:28)lter, and our pre(cid:28)ltered data requires O ( bn ) storage, comparing favorably to the state-of-the-art. We apply IFD to interesting problems in image processing and realistic rendering.
Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

—This study directly and thoroughly investigates the practicalities of utilizing sentence embeddings, derived from the foundations of deep learning, for textual entailment recognition, with a specific emphasis on the robust BERT model. As a cornerstone of our research, we incorporated the Stanford Natural Language Inference (SNLI) dataset. Our study emphasizes a meticulous analysis of BERT’s variable layers to ascertain the optimal layer for generating sentence embeddings that can effectively identify entailment. Our approach deviates from traditional methodologies, as we base our evaluation of entailment on the direct and simple comparison of sentence norms, subsequently highlighting the geometrical attributes of the embeddings. Experimental results revealed that the L 2 norm of sentence embeddings, drawn specifically from BERT’s 7th layer, emerged superior in entailment detection compared to other setups.
SOCIO-DEMOGRAPHIC CHARACTERISTICS AND PREVALENCE OF UNDER NUTRITION AMONG ADULT SABARS OF BANKURA DISTRICT, WEST BENGAL, INDIA

Nutritional status is an important index of economic growth and development. The status of nutrition of a given population has positively or negatively influenced nation’s economic growth. Our study was designed to find out the relationship between socio-demographic characteristics and nutritional status among adult Sabars. The present cross-sectional study was conducted at six villages under Ranibandh block of Bankura District, West Bengal. A total 226 adults (males = 111 and females = 115) Sabars, aged above 18 years in these village were included in the study. The mean (SD) values of age, family members, family income per month, and per-capita income were 37.32 (16.62) years, 4.63 (1.85) members, 2725.2 (1424.3) Rupees, 691.58 (545.32) Rupees, respectively. The prevalence of illiteracy was 45.0% and 82.6%, respectively among males and females. There was a statistically significant (Chi-square = 39.67, p < 0.001) difference between two sexes. Among males more than 65.8% were daily wage laborers. Among females collection of products was the main occupation (28.7%). The frequency of use of drinking water from tube well, tap and well were 79.2%, 11.1% and 4.4%, respectively. The mean (SD) height, weight, mid-upper arm circumference (MUAC) and (body mass index (BMI) of the subjects were 159.88 (5.93) cm, 49.6 (7.42) kg, 28.38 (7.83) cm and 19.4 (2.59) kg/m2, respectively, among males. The mean (SD) height, weight, MUAC and BMI among females were 1148.9 (5.04) cm, 40.1 (5.88) kg, 22.9 (2.34) cm and 18.0 (2.30) kg/m2, respectively. All mean values were higher among men. The rates of under nutrition were higher (60.0%) among the illiterate group than the literate group (37.0%) based on BMI. Likewise, based on MUAC, the rates of under nutrition were higher (41.4%) among illiterates compared to literates. The prevalence of under nutrition based on BMI and MUAC was high according to the World Health Organization (WHO) guideline, indicating critical nutritional situation. To reduce the nutritional stress among this ethnic group, immediate appropriate nutritional intervention programes should be initiated. Serious efforts are needed to increase their educational status and per-capita income.
An overview of University of Alaska Anchorage, ENRI research on the spruce bark beetle infestation, Kenai Peninsula, Alaska, 1997-2002

In the mid 1990s, one of the largest bark beetle infestations seen anywhere in the world occurred on the Kenai Peninsula of Alaska. In one year, the infestation affected over one million acres of spruce in Alaska. This paper presents a coalescence of several aspects of study on the problem, including tree inventory and mortality, regeneration, understory response, a phytomass study, an assessment of mortality inventory methodology, and an assessment of forest health after the bark beetle infestation declined. The basis for the data collection in these studies was a two-phase sampling design (aerial photos and ground plots), established by the author in 1997 and subsequently re-measured in 1998.
Mechatronics Design and Robotic Simulation of Serial Manipulators to Perform Automation Tasks in the Avocado Industry

—Peru is considered one of the principal agroindustrial avocado exporters worldwide. At the beginning of 2022, the volume exported was 8.3% higher than in 2021, so the design and simulation of a pick and place and palletizing cell for agro-exporting companies in the Region of La Libertad was proposed. A methodology was followed that presented a flow diagram of the design of the cell, considering the size of the avocado and the dimensions of the box-type packaging. The forward and inverse kinematics for the Scara T6 and UR10 robots were developed in Matlab according to the Denavit-Hartenberg algorithm, and 3D CAD, dynamic modeling, and trajectory calculation were performed in Solidworks using a "planner" algorithm developed in Matlab, which takes into account the start and end points, maximum speeds, and travel time of each robot. Then, in CoppeliaSim, the working environment of the cell and the robots with their respective configurations are created. Finally, the simulation of trajectories is performed, describing the expected movement, getting the time of the finished task was calculated, where the Scara T6 robot had a working time of 1.18 s and the UR10 of 2.32 s. For 2023 - 2025, its implementation is proposed in the Camposol Company located in the district of Chao - La Libertad, considering the dynamic control of the system.
Effect of Difference in Rainfall Patterns and Intensities on Runoff Simulation Results in Arid Land (THE 1st INTERNATIONAL CONFERENCE ON ARID LAND (ICAL 1) "DESERT TECHNOLOGY X (DT X)")

Currently, a sustainable carbon fixation system with large scale afforestation in arid land has been established for the mitigation of global warming. We have set a research site near Leonora (rainfall 200 mm: evapotranspiration 2,400-2,600 mm) in Western Australia. In arid land, the most important problem is water shortage for afforestation, because of the small amount of rainfall. In addition, large amount of runoff water flows out without being used by plants in this area. In this study, we developed an original runoff model for understanding water behavior to select appropriate afforestation sites. This model consists of three simulation processes which are penetration, evaporation and runoff as water behavior. This model has two fitting parameters. One is corrective coefficient of penetration (PR) and the other is equivalent roughness coefficient (N). To estimate these two parameters, a closed hydrographic basin (6 × 5 km) including a water collecting pond and its DEM (Digital Elevation Model) data with spatial resolution of 10 meters were used. Rainfall gauges with rainfall amount resolution and time resolution as 0.2 mm and 0.5 s, respectively, were set near the main creek flowing into the pond, and water level indicator was set in the pond. This model simulation was conducted using one hour cumulative data or one minute cumulative data calculated from these rainfall measurement data obtained on 24th March 2003. We also evaluated effects of various patterns of rainfall intensities on calculated results of runoff water behavior using other rain data.
Automatic Layout Algorithm for Graphic Language in Visual Communication Design

—As computer technology advances, people's capacity for visual perception grows better, and the demands placed on computerized layouts progressively rise. The simple style of graphics is no longer the only option for computer figure video creation; instead, there is a greater tendency to visually represent the effect and improve the aesthetics and expressiveness of visuals and images. Graphic language uses visual components, including shapes, colors, typographies, images, and icons, in a visual communication context to express messages, ideas, and emotions. Graphics language encounters greater chances and obstacles against the backdrop of this information era. Consequently, it is crucial to convert data into graphics language. Visual communication is evolving in some potential directions with the advancement of technological advances and cultural convergence. The graphic language has its distinct visual meaning, and each person's visual experience is extremely diverse and exists in life with various visual elements in different layouts. A hybridized Grid and Content-based Automatic Layout (HGC-AL) algorithm for graphic language in Visual Communication Design (VCD) has been developed to produce visually balanced layouts and establish a structured system for arranging content elements. The content-based layout uses design constraints for better alignment and avoids conflict loss. The hierarchical arrangement of graphic elements in a grid layout analyzes the types of visual elements like image, text, and color. Finally, graphic language enhances the visual score and gives flexibility by allowing changes and modifications within the grid layout. Following the design requirements change, the responsive fluid grid supports various graphical content, sizes, and alignments. Thus, compared with existing layout algorithms, the proposed algorithm is validated with metrics like Intersection of Union (IoU), alignment accuracy, content coverage ratio, visual score, scalability ratio, and overall layout quality.