Miracle-dz/UniFAR-FTUs huggingface dataset:
Anarchic Convex Federated Learning

The rapid advances in federated learning (FL) in the past few years have recently inspired a great deal of research on this emerging topic. Existing work on FL often assume that clients participate in the learning process with some particular pattern (such as balanced participation), and/or in a synchronous manner, and/or with the same number of local iterations, while these assumptions can be hard to hold in practice. In this paper, we propose AFLC, an Anarchic Federated Learning algorithm for Convex learning problems, which gives maximum freedom to clients. In particular, AFLC allows clients to 1) participate in arbitrary rounds; 2) participate asynchronously; 3) participate with arbitrary numbers of local iterations. The proposed AFLC algorithm enables clients to participate in FL efficiently and flexibly according to their needs, e.g., based on their heterogeneous and time-varying computation and communication capabilities. We characterize performance bounds on the learning loss of AFLC as a function of clients' local model delays and local iteration numbers. Our results show that the convergence error can be made arbitrarily small by choosing appropriate learning rates, and the convergence rate matches that of existing benchmarks. The results also characterize the impacts of clients' various parameters on the learning loss, which provide useful insights. Numerical results demonstrate the efficiency of the proposed algorithm.
An Optimization Model for the Demand-Responsive Transit With Non-Fixed Stops and Multi-Vehicle Type

As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies on scheduling problems of demand responsive transit, which only focused on stop modes such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling method, the operating costs of the multi-occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode with fixed stops, the average full load ratio of the mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.
Dynamic Resource Allocation for Deep Learning Clusters with Separated Compute and Storage

The separation of compute and storage in modern cloud services eases the deployment of general applications. However, with the development of accelerators such as GPU/TPU, Deep Learning (DL) training is suffering from potential IO bottlenecks when loading data from storage clusters. Therefore, DL training jobs need to either create local cache in the compute cluster to reduce the bandwidth demands or scale up the IO capacity with higher bandwidth cost. It is full of challenges to choose the best strategy due to the heterogeneous cache/IO preference of DL models, shared dataset among multiple jobs and dynamic GPU scaling of DL training. In this work, we exploit the job characteristics based on their training throughput, dataset size and scalability. For fixed GPU allocation of jobs, we propose CBA to minimize the training cost with a closed-form approach. For clusters that can automatically scale the GPU allocations of jobs, we extend CBA to AutoCBA to support diverse job utility functions and improve social welfare within a limited budget. Extensive experiments with production traces validate that CBA and AutoCBA can reduce IO cost and improve total social welfare by up to 20.5% and 2.27×, respectively, over the state-of-the-art schedulers for DL training.
EXPLORING THE ECONOMIC IMPACTS OF SUSTAINABLE TOURISM ON LOCALS: A COMPARATIVE ANALYSIS BETWEEN BALI AND NORT SUMATERA

This comparative analysis explores the economic impacts of sustainable tourism on local communities in Bali and North Sumatra, Indonesia. The study highlights the significance of understanding the specific contexts of these regions and examines the economic benefits, challenges, and policy initiatives related to sustainable tourism development. The research methodology involved a comprehensive literature review and data analysis. The selection criteria for comparative analysis and the variables and indicators used were carefully considered to ensure a comprehensive examination of the economic impacts of sustainable tourism in Bali and North Sumatra. The findings reveal positive economic outcomes in both regions, including job creation, income generation, and support for local businesses. However, Bali exhibits a more significant economic impact with its more developed tourism industry than North Sumatra. This difference can be attributed to factors such as higher tourist influx and better tourism infrastructure in Bali. The comparative analysis also uncovers common challenges locals face in both regions, including infrastructure development, resource management, cultural preservation, community engagement, capacity building, marketing strategies, and equitable distribution of benefits. Addressing these challenges is essential for sustaining the economic benefits of sustainable tourism and ensuring the preservation of natural and cultural resources. In conclusion, this comparative analysis highlights the economic impacts of sustainable tourism in Bali and North Sumatra. It emphasizes the need to address challenges and implement effective policies to maximize economic benefits and ensure a more equitable distribution for local communities.
Fast Bit-Flipping Decoding of Polar Codes with Additional Nodes

Polar codes have gained significant a ttention in recent years due to their superior performance, making them an attractive choice for use in modern communication systems. In this paper, we present a novel approach for implementing a fast decoding method for polar codes with additional nodes. Specifically, we propose a modification of the fast decoding method with bit-flipping capability, called the GSCLAF, which is designed to improve the correction capability of the decoder. To achieve this, we consider additional nodes of five types, which are obtained by a combination of basic generalized nodes, namely information, frozen, SPC, and REP nodes. To evaluate the performance of our proposed algorithm, numerical experiments were conducted using the 5G NR polar code. The results of these experiments demonstrate the effectiveness of GSCLAF relative to other popular polar decoding algorithms. Overall, our proposed algorithm provides a powerful tool for improving the decoding performance of polar codes with additional nodes, making it a promising solution for modern communication systems that require high data rates and reliable communication.
Comparative Studies of the Confined Effect of Shear Masonry Walls Made of Autoclaved Aerated Concrete Masonry Units

Confined walls are popular in areas exposed to seismic action. The advantage of such structures is increased load-bearing capacity, ductility, and energy dissipation. Confined masonry walls are also used to restrain the intensity of cracking and improve load-bearing capacity in areas exposed to seismic action. This paper describes the research on 18 confined walls and presents a comparison with research on unconfined walls (referenced models). The confined models were classified into three series: HOS-C-AAC—without openings and with confining elements around the perimeter; HAS-C1-AAC with a centrally positioned opening and circumferential confinement; and HAS-C2-AAC with a centrally positioned window opening and additional confinement along the vertical edges of the opening. The area of the window opening was 1.5 m2. All walls were made of autoclaved aerated concrete (AAC) masonry units of the nominal density class of 600. The walls were tested under initial compressive stresses σc = 0.1; 0.75; and 1.0 N/mm2. The reference models without confinement (six models of the series HOS-AAC without openings and the series HAS-AAC with openings) were prepared from the same masonry units, had almost the same outer dimensions, and were tested under the same initial compressive stresses σc. The analysis was performed for the morphology of cracks, stress values at the moment of cracking and failure, stiffness, and angles of shear strain. The morphology of cracks was found to depend on initial compressive stresses and the presence of an opening. A significant increase in compressive stress leading to cracks and failure stresses was observed with increasing values of initial compressive stresses. As the wall behavior was clearly non-linear, the bilinear relationship described by energy dissipation E, stiffness at the moment of cracking Kcr, and maximum displacement uu was proposed to be included in the engineering description of the relationship between horizontal load and displacement of confined walls. Confinement along the vertical edges of the opening having an area of 1.5 m2 (acc. to EN 1996-1-1) increased the maximum forces Pmax by ca. 45% and marginally affected the ductility of the wall when compared to the elements with circumferential confinement.
Using Genetic Algorithm and Simulated Annealing with Memory to Solve Layout Synthesis Problem for GaAs MMIC

A GaAs microwave monolithic integrated circuit (MMIC) layout synthesis technique is presented. Evolutionary algorithms and simulated annealing-like algorithms are used in the suggested technique. A graph is used to represent circuit elements. Using genetic algorithm allow us to get global placement. Using first guess (pattern) allow us to reduce population size by 5 times, which lead to reduce synthesis time, and raise the probability to get feasible layout. Breadth-first search and depth-first search algorithms are used to obtain the pattern. Detailed placement is based on random refinement and simulated annealing with memory. Using detailed placement allows us to improve by 3 times solution obtained by genetic algorithm in terms of fitness function values. A * search algorithm is used to route solution. Von Neuman neighborhood and turning penalty are used to obtain tracing line with minimal bend counts. Suggested technique is validated with synthesis of 0.5-20 GHz 2 dB digital step attenuator section. Synthesized solution has 0.5 dB attenuation error, and met the requirements to reference state insertion loss and phase error.
Enrichment of Lactic Acid-Producing Bacteria in the Fecal Microbiota of Patients with Ulcerative Colitis in North India

Previous studies have established the relationship between the gut microbiota and ulcerative colitis (UC); however, there is a scarcity of data on the fecal microbiome profile of patients with UC in the Indian population. This study aimed to examine the fecal microbiome profile of north Indian patients with UC ( n = 105 ), including with active disease ( n = 64 ) and in remission ( n = 41 ), compared to healthy controls ( n = 36 ), using 16S rRNA gene sequencing. Both relative abundance analysis and linear discriminant analysis effect size (LEfSe) revealed a significant enrichment of lactic acid-producing facultative anaerobic pathobionts, namely, Bifidobacterium, Lactobacillus, and Streptococcus, in patients with UC (both with active disease and those in remission). Additionally, a significant decrease was observed in anaerobic genera responsible for the synthesis of short-chain fatty acids (SCFAs), especially butyrate, such as Blautia, Roseburia, Lachnospiraceae, and Ruminococcaceae. Differential metabolic pathway analysis using PICRUSt2 confirmed a loss of SCFAs production and increased lactose and nitrate metabolism in UC patients. Biochemical analysis of fecal samples confirmed the increased colonization of nitrate-reducing microbes in UC patients, suggesting inflammation-driven dysbiosis. LEfSe and PICRUSt2 analyses revealed an enrichment of Prevotella and Blautia microbes and the upregulation of two specific metabolic pathways, sulfolactate degradation and reductive acetyl coenzyme A pathway-I, which can distinguish UC patients in remission from those with active disease. Our findings caution against the use of lactic acid-producing bacteria (LABs) and recommend the exploration of butyrate-producing microbes as probiotics to restore SCFAs levels in UC patients.
A Crop Recommendation System Based on Nutrients and Environmental Factors Using Machine Learning Models and IoT

With the ever-increasing population of the world, enough crop production is the biggest concern for the human race. This issue is more pressing than ever as the world population has surpassed the 8 billion mark. Smart farming has become a popular option as it solves the problem by suggesting ways to increase the quality and quantity of crop yield. It is a term associated with the practice of automating farm-related activities. This paper proposes a crop recommendation system based on machine learning algorithms for agricultural fields in India. A sensor system is also prepared to collect first-hand data from fields. These IoT sensors are then used to record levels of soil moisture content, Temperature, and the three most important macro-nutrients required for soil growth: Nitrogen (N), Phosphorus (P), and Potassium (K), from different fields. Additionally, other variables such as rainfall, sowing season, and pH value of soil are also considered to build the proposed crop recommendation system that recommends the best-yielding crop based on the other environmental factors. Multiple machine learning algorithms including Artificial Neural Networks (ANN), Random Forest, Logistic Regression, and K-Nearest Neighbor (KNN) are used and compared to identify the most efficient algorithm for the crop recommendation system. The proposed system aims to develop a model that can help farmers increase their crop yield and quality by providing personalized recommendations based on environmental variables.
A new species of Liolaemus (Iguania: Liolaemidae) from the hot deserts of northern Patagonia, Argentina

A new species of Liolaemus is described from southwest of the town of Añelo, Neuquén Province, Argentina. Integrative evidence methodology of external morphological characters and molecular phylogenetic analyses of mitochondrial DNA (cyt-b) is used to place the new species to the species group of Liolaemus boulengeri. The new species is phenotypically close to L. mapuche. The new Liolaemus is medium to large in size (males 77.64–83.98 mm, females 72.88–78.58 mm), with evident sexual dichromatism. Genetic distances of the mtDNA (cyt-b) between the new species and its closest relative species are greater than 3% (L. cuyanus 7.48–12.02%; L. josei 7.56–9.60%; L. puelche 8.23–9.93%; L. mapuche 8.51–9.79%). Molecular and morphological phylogenetic results show L. mapuche as the sister species of the new one. The new species is larger than L. mapuche. Dorsal and ventral scales are more numerous in the new species than in L. mapuche, precloacal pores in females are present in L. mapuche and absent in the new species. It has strict psammophilic habits, using sand mounds and sheltering, under Alpataco (Neltuma alpataco) bushes. The L. boulengeri group now contains 75 species distributed in Argentina, Bolivia, Brazil, Chile, Paraguay, Peru and Uruguay.
The transcriptional PPAR (cid:2) / (cid:3) network in human macrophages deﬁnes a unique agonist-induced activation state

Peroxisome proliferator-activated receptor (cid:2) / (cid:3) (PPAR (cid:2) / (cid:3) ) is a lipid ligand-inducible transcription factor with established metabolic functions, whereas its anti-inﬂammatory function is poorly understood. To address this issue, we determined the global PPAR (cid:2) / (cid:3) -regulated signaling network in human monocyte-derived macrophages. Besides cell type-independent, a macrophage-selective B multiple mediators anti-inflammatory effect specific agonists.
Read Range Investigation of RFID Tag on Human Chest

Radio Frequency Identification (RFID) technology for automatic identification, tracking, and monitoring of human is in great demand by the healthcare industry. The existence of passive tag with proximity to human body in general, represents an intrinsic challenge because of the high losses of body tissues A commercial passive ultra-high frequency (UHF) RFID tag with 860 MHz-960 MHz operating frequency range is modelled to study and analyze the effect on the read range parameter by placing the RFID tag in proximity to human body model (the chest) with respect to the placement of pocket position of any shirt The body model of the chest is consisting of three layers which are skin, fat and muscle. The commercial RFID tag that has been used for this study was INLAY-9662. The measurement is done on the commercial tag prior to modelling and simulation in the CST. Furthermore, the simulation was carried on two conditions: (1) in free-space, and (2) in proximity to human body. In addition, 1 mm, 3 mm and 10 mm have been set as the gap between the modelled tag and the chest model to examine the impacted read range at different distances. The results obtained have shown some variations when the distances are varied from the human chest. The read range were achieved at 0.5 m, 1.3 m and 2.4 m for 1 mm, 3 mm and 10 mm respectively. This study has shown the capabilities of human body to reduce the efficiency of the read range whenever RFID tags are placed in proximity to human body.
Adaptive Multiple Distributed Bidirectional Spiral Path Planning for Foraging Robot Swarms

The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and when a robot completes its search, it is assigned to either an unassigned region or a region with one robot. The two robots will then travel along the path from the starting and ending points of the spiral path. We evaluated the performance of robot swarms using the MDBSA algorithm in the ARGoS robot simulator. Our experimental results show that the proposed MDBSA algorithm outperforms DDSA. When robots deliver collected resources to regions instead of the center, it reduces collisions and significantly improves the scalability of the robot swarm. Our findings suggest that a multiple-distributed search strategy is an efficient solution for foraging robot swarms.
Building Robust Long-Term Conservation Monitoring With Design Thinking

This article reports on an on-going project that aims to develop a long-term (i.e., over 30 years) conservation monitoring plan for rare mesocarnivores on lands managed by the US Forest Service in the western US. The project employs a design thinking approach, which is relatively unknown in natural resource management, for the development of the monitoring plan. We report on our design process through the first three phases (empathize, define, and ideate), followed by a brief description of our current effort on prototyping. The early phases of the design process reveals that two touchpoints would present unique challenges in successfully implementing our plan: (1) instructions for data collection communicated through protocols and (2) the use of monitoring information for predictive decisions. Standardized template for protocols and decision support tools to address these challenges are suggested as potential solutions. A component content management approach is also suggested as a way to create a long-term monitoring that can be adaptive to changes in information over the time period that long-term monitoring is designed to cover. We conclude by postulating that the design thinking approach may be applicable to other natural resource management projects.
Forecasting the S&P 500 Index Using Mathematical-Based Sentiment Analysis and Deep Learning Models: A FinBERT Transformer Model and LSTM

Stock price prediction has been a subject of significant interest in the financial mathematics field. Recently, interest in natural language processing models has increased, and among them, transformer models, such as BERT and FinBERT, are attracting attention. This study uses a mathematical framework to investigate the effects of human sentiment on stock movements, especially in text data. In particular, FinBERT, a domain-specific language model based on BERT tailored for financial language, was employed for the sentiment analysis on the financial texts to extract sentiment information. In this study, we use “summary” text data extracted from The New York Times, representing concise summaries of news articles. Accordingly, we apply FinBERT to the summary text data to calculate sentiment scores. In addition, we employ the LSTM (Long short-term memory) methodology, one of the machine learning models, for stock price prediction using sentiment scores. Furthermore, the LSTM model was trained by stock price data and the estimated sentiment scores. We compared the predictive power of LSTM models with and without sentiment analysis based on error measures such as MSE, RMSE, and MAE. The empirical results demonstrated that including sentiment scores through the LSTM model led to improved prediction accuracy for all three measures. These findings indicate the significance of incorporating news sentiment into stock price predictions, shedding light on the potential impact of psychological factors on financial markets. By using the FinBERT transformer model, this study aimed to investigate the interplay between sentiment and stock price predictions, contributing to a deeper understanding of mathematical-based sentiment analysis in finance and its role in enhancing forecasting in financial mathematics. Furthermore, we show that using summary data instead of entire news articles is a useful strategy for mathematical-based sentiment analysis.