Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-efficacy in patients with breast cancer: A health belief model approach

Background: Breast cancer is a disturbance in the growth of normal breast cells characterized by abnormal cells arising from normal cells that can infiltrate lymphatic tissue and blood vessels. This will have a major impact on patients and their families, both physically, psychologically, economically and in other aspects of life which certainly affect the patient's self-efficacy about their own ability to carry out healthy behaviors that have an impact on their treatment. One of the support efforts that can be given is the intensification of activities through the Health Belief Model.Purpose: To determine the effect of Self-efficacy in patients with breast cancer: A health belief model approachMethod: The design used in this study was a quasi-experimental design with one group pretest-posttest. Determination of the sample by purposive sampling method as many as 60 participants. Data was collected using a self-efficacy questionnaire.Results: The Marginal homogeneity test obtained a p-value = 0.001, which means that there is an effect of giving interventions with the Health Belief Model on self-efficacy in breast cancer patients.Conclusion: Intervention with the Health Belief Model approach can increase the self-efficacy of breast cancer patients who are undergoing treatment.
Determinants of Export Performance in Uganda (1989-2020)

The study is set to investigate the factors determining export performance in Uganda. The specific objectives of the study are to examine the relationship between Foreign Direct Investment net inflows, inflation, Real Exchange Rate and export performance from 1989 to 2020. The study adopted a longitudinal design and the analysis was based on stationarity tests, co-integration tests, ordinary least squares tests and finally diagnostic tests. The study results show that there is a significant relationship between foreign direct investments and export performance. Secondly, it was found that there was a relationship between inflation and export performance. The study also observed a significant relationship between inflation and export performance. Finally, it was found that the real exchange rate affects export performance. The study concludes that the development of foreign direct investments induces export performance; hence export performance can be generated through foreign direct investments. The study also concludes that the state of inflation reduction can be a general development of the export performance for the countries, while the real exchange rate is a key determinant for the export performance in Uganda from 1989 to 2020. The study recommends that there is a need for regulations and monitoring to ensure the proper form of foreign businesses in the country together with enhancing the management situation for the management of the business. Secondly, the policies intended to reduce inflation are needed to increase the capacity of businesses to operate and attain mechanisms for the reduction of inflation. Finally, the government should also embark on strong fiscal policy to reduce unnecessary money supply which can lead to inflation that negatively impacts on economy & reduces economic growth.
Implementation of Experiential Learning Model to Improve Science Process Skills

Data collection in this study aims to describe the effect of the experiential learning model on improving student's science process skills. The factor that causes low science process skills is that teachers. The experiential learning model is a learning model that implements a direct student learning process. This study uses a quantitative approach with experimental methods. The type of research research is a one-group pre-test and post-test design. The sample in this study was using a saturated sampling of 22 students. The research instrument is science process skills (KPS) test questions oriented to description and multiple choice questions. Data were analyzed descriptively quantitatively by normality test for normal data testing and normalized gain-score test to see the improvement of students' KPS. In addition, it is known how much the KPS increase using the N-Gain Test is 0.51, with a moderate category included in the normalized gain criteria of 0.3 ≥ g ≤ 0.7. Based on the results of the research data obtained, the average score on the pre-test was 59.09, while the average score on the post-test showed an increase to 80. This research proves that learning using the Experiential Learning model can make students understand the material in depth, which is taught through the search process carried out by students.
The frequency and the time-domain responses of a buried axial conductor

Our objective is to analyze the frequency and the time‐domain responses of a two‐dimensional lateral inhomogeneity such as an axial conductor which is buried in a stratified earth. Such inhomogeneities in the earth’s structure give complex transients that cannot be interpreted using just a uniformly layered earth model. To proceed, we determine the fields of a thin axial conductor carrying a filamental current situated in the lower medium of a two‐layered earth. This infinitely long axial conductor is characterized by a specific axial impedance. The impedance condition can be used to determine the total field response when the source is a magnetic dipole. Some results are presented that show the waveform at the induced current in a buried axial conductor excited by an impulsive current in a vertical magnetic dipole source located on the earth’s surface. A feature of the waveform is the reversal of the polarity that would not occur in the absence of the buried cable.
ARSENIC IN THE SHALLOW GROUND WATERS OF CONTERMINOUS UNITED STATES: ASSESSMENT, HEALTH RISKS, AND COSTS FOR MCL COMPLIANCE 1

A methodology consisting of ordinal logistic regression (OLR) is used to predict the probability of occurrence of arsenic concentrations in different threshold limits in shallow ground waters of the conterminous United States (CONUS) subject to a set of influencing variables. The analysis considered a number of maximum contaminant level (MCL) options as threshold values to estimate the probabilities of occurrence of arsenic in ranges defined by a given MCL of 3, 5, 10, 20, and 50 μg/l and a detection limit of 1 μg/l. The fit between the observed and predicted probability of occurrence was around 83 percent for all MCL options. The estimated probabilities were used to estimate the median background concentration of arsenic in the CONUS. The shallow ground water of the western United States is more vulnerable than the eastern United States. Arizona, Utah, Nevada, and California in particular are hotspots for arsenic contamination. The risk assessment showed that counties in southern California, Arizona, Florida, and Washington and a few others scattered throughout the CONUS face a high risk from arsenic exposure through untreated ground water consumption. A simple cost effectiveness analysis was performed to understand the household costs for MCL compliance in using arsenic contaminated ground water. The results showed that the current MCL of 10 μg/l is a good compromise based on existing treatment technologies.
A Deep Reinforcement Learning Approach to Supply Chain Inventory Management

This paper leverages recent developments in reinforcement learning and deep learning to solve the supply chain inventory management (SCIM) problem, a complex sequential decision-making problem consisting of determining the optimal quantity of products to produce and ship to different warehouses over a given time horizon. A mathematical formulation of the stochastic two-echelon supply chain environment is given, which allows an arbitrary number of ware-houses and product types to be managed. Additionally, an open-source library that interfaces with deep reinforcement learning (DRL) algorithms is developed and made publicly available for solving the SCIM problem. Performances achieved by state-of-the-art DRL algorithms are compared through a rich set of numerical experiments on synthetically generated data. The experimental plan is designed and performed, including different structures, topologies, demands, capacities, and costs of the supply chain. Results show that the PPO algorithm adapts very well to different characteristics of the environment. The VPG algorithm almost always converges to a local maximum, even if it typically achieves an acceptable performance level. Finally, A3C is the fastest algorithm, but just like VPG, it never achieves the best performance when compared to PPO. In conclusion, numerical experiments show that DRL performs consistently better than standard reorder policies, such as the static ( s , Q )-policy. Thus, it can be considered a practical and effective option for solving real-world instances of the stochastic two-echelon SCIM problem.
Shape from Shading for Robotic Manipulation

Controlling illumination can generate high quality information about object surface normals and depth discontinuities at a low computational cost. In this work we demonstrate a robot workspace-scaled controlled illumination approach that generates high quality information for table top scale objects for robotic manipulation. With our low angle of incidence directional illumination approach, we can precisely capture surface normals and depth discontinuities of monochromatic Lambertian objects. We show that this approach to shape estimation is 1) valuable for general purpose grasping with a single point vacuum gripper, 2) can measure the deformation of known objects, and 3) can estimate pose of known objects and track unknown objects in the robot's workspace.
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT
Exact output tracking in prescribed ﬁnite time via funnel control (cid:63)

We extend a recent result in high-gain feedback output tracking control to achieve exact tracking within ﬁnite time, i.e., the output of a system and its relevant derivatives have certain exact values at a predeﬁned ﬁnite time. We propose a new funnel control scheme achieving this, whereas the error between the reference and the output evolves within prescribed bounds. Applications of this are, for instance, linking up two parts of a train, or docking spaceships.
Path Analysis for Effective Fault Localization in Deep Neural Networks

Deep learning has revolutionized various real-world applications, but the quality of Deep Neural Networks (DNNs) remains a concern. DNNs are complex and have millions of parameters, making it difficult to determine their contributions to fulfilling a task. Moreover, the behavior of a DNN is highly influenced by the data used during training, making it challenging to collect enough data to exercise all potential DNN behavior under all possible scenarios. This paper proposes NP SBFL method to locate faulty neural pathways (NP) using spectrum-based fault localization (SBFL). Our method identifies critical neurons using the layer-wise relevance propagation (LRP) technique and determines which critical neurons are faulty. Moreover, we propose a multi-stage gradient ascent (MGA), an extension of gradient ascent (GA), to effectively activate a sequence of neurons one at a time while maintaining the activation of previous neurons, so we are able to test the reported faulty pathways. We evaluated the effectiveness of our method, i.e. NP-SBFL-MGA, on two commonly used datasets, MNIST and CIFAR-10, two baselines DeepFault and NP-SBFL-GA, and three suspicious neuron measures, Tarantula, Ochiai, and Barinel. The empirical results showed that NP-SBFL-MGA is statistically more effective than the baselines at identifying suspicious paths and synthesizing adversarial inputs. Particularly, Tarantula on NP-SBFL-MGA had the highest fault detection rate at 96.75%, surpassing DeepFault on Ochiai (89.90%) and NP-SBFL-GA on Ochiai (60.61%). Our approach also yielded comparable results to the baselines in synthesizing naturalness inputs, and we found a positive correlation between the coverage of critical paths and the number of failed tests in DNN fault localization.
Composer's Assistant: Interactive Transformers for Multi-Track MIDI Infilling

We consider the task of multi-track MIDI inﬁlling when arbitrary (track, measure) pairs of information have been deleted from a contiguous slice of measures from a MIDI ﬁle. We train two T5-like models to solve this task, one using a basic MIDI-like event vocabulary and one using a joined word-like version of this vocabulary. We introduce a new test set, created from the Lakh MIDI dataset, consisting of 9 multi-track MIDI inﬁlling tasks. We evaluate our models on these tasks and ﬁnd that one model works better on some tasks while the other works better on others. Our results have implications for the training of neural networks in other small-vocabulary domains, such as byte sequence modeling and protein sequence modeling. We release our source code, and we demonstrate that our models are capable of enabling real-time human-computer interactive composition in the REAPER digital audio workstation.
Sentiment Analysis Dataset in Moroccan Dialect: Bridging the Gap Between Arabic and Latin Scripted dialect

Sentiment analysis, the automated process of determining emotions or opinions expressed in text, has seen extensive exploration in the field of natural language processing. However, one aspect that has remained underrepresented is the sentiment analysis of the Moroccan dialect, which boasts a unique linguistic landscape and the coexistence of multiple scripts. Previous works in sentiment analysis primarily targeted dialects employing Arabic script. While these efforts provided valuable insights, they may not fully capture the complexity of Moroccan web content, which features a blend of Arabic and Latin script. As a result, our study emphasizes the importance of extending sentiment analysis to encompass the entire spectrum of Moroccan linguistic diversity. Central to our research is the creation of the largest public dataset for Moroccan dialect sentiment analysis that incorporates not only Moroccan dialect written in Arabic script but also in Latin letters. By assembling a diverse range of textual data, we were able to construct a dataset with a range of 20 000 manually labeled text in Moroccan dialect and also publicly available lists of stop words in Moroccan dialect. To dive into sentiment analysis, we conducted a comparative study on multiple Machine learning models to assess their compatibility with our dataset. Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing step. We were able to achieve 92% accuracy in our model and to further prove its liability we tested our model on smaller publicly available datasets of Moroccan dialect and the results were favorable.
Overleaf Example

Distributed optimization is an important direction of research in modern optimization theory. Its applications include large scale machine learning, distributed signal processing and many others. The paper studies decentralized min-max optimization for saddle point problems. Saddle point problems arise in training adversarial networks and in robust machine learning. The focus of the work is optimization over (slowly) time-varying networks. The topology of the network changes from time to time, and the velocity of changes is limited. We show that, analogically to decentralized optimization, it is sufficient to change only two edges per iteration in order to slow down convergence to the arbitrary time-varying case. At the same time, we investigate several classes of time-varying graphs for which the communication complexity can be reduced.
Computerized Medical Imaging and Graphics Unsupervised Svm-based Gridding for Dna Microarray Images

This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.
Fingerprints of GeV scale right-handed neutrinos on inflationary gravitational waves and PTA data

We show that the seesaw mechanisms that exhibit right-handed neutrino mass-dependent non-standard post-inflationary cosmology make blue-tilted inflationary gravitational waves (GW) compatible with the recent findings of nHz stochastic GW background by the pulsar-timing arrays (PTAs) for high reheating temperatures. The right-handed neutrino (RHN) mass scale has to be $\mathcal{O}(\rm GeV)$. Remarkably, such a scenario produces a correlated signature testable by the future LIGO run. In addition to contributing to the active neutrino masses, $\mathcal{O}(\rm GeV)$ RHNs generate baryon asymmetry of the universe via low-scale-leptogenesis. They can be searched for in collider experiments. Therefore, the recent detection by PTAs is not only exciting for GWs in the nHz range; it paves the way to test and constrain well-studied mechanisms, such as seesaws, with a low-frequency and a correlated measurement of high-frequency GW spectral features, complementary to particle physics searches.
CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language

Previous audio-visual speech separation methods use the synchronization of the speaker's facial movement and speech in the video to supervise the speech separation in a self-supervised way. In this paper, we propose a model to solve the speech separation problem assisted by both face and sign language, which we call the extended speech separation problem. We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem. To train the model, we introduce a large-scale dataset named the Chinese Sign Language News Speech (CSLNSpeech) dataset, in which three modalities of audio, face, and sign language coexist. Experiment results show that the proposed model has better performance and robustness than the usual audio-visual system. Besides, sign language modality can also be used alone to supervise speech separation tasks, and the introduction of sign language is helpful for hearing-impaired people to learn and communicate. Last, our model is a general speech separation framework and can achieve very competitive separation performance on two open-source audio-visual datasets. The code is available at https://github.com/iveveive/SLNSpeech
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition

Self-supervised learning (SSL) for WiFi-based human activity recognition (HAR) holds great promise due to its ability to address the challenge of insufficient labeled data. However, directly transplanting SSL algorithms, especially contrastive learning, originally designed for other domains to CSI data, often fails to achieve the expected performance. We attribute this issue to the inappropriate alignment criteria, which disrupt the semantic distance consistency between the feature space and the input space. To address this challenge, we introduce \textbf{A}ntenna \textbf{R}esponse \textbf{C}onsistency (ARC) as a solution to define proper alignment criteria. ARC is designed to retain semantic information from the input space while introducing robustness to real-world noise. Moreover, we substantiate the effectiveness of ARC through a comprehensive set of experiments, demonstrating its capability to enhance the performance of self-supervised learning for WiFi-based HAR by achieving an increase of over 5\% in accuracy in most cases and achieving a best accuracy of 94.97\%.
On fully nonlinear parabolic mean field games with examples of nonlocal and local diffusions

In this paper we introduce a class of fully nonlinear mean field games posed in $[0,T]\times\mathbb{R}^d$. We justify that they are related to controlled local or nonlocal diffusions, and more generally in our setting, to a new control interpretation involving time change rates of stochastic (L\'evy) processes. The main results are existence and uniqueness of solutions under general assumptions. These results are applied to strongly degenerate equations of order less than one - and non-degenerate equations - including both local second-order and nonlocal with fractional Laplacians. Uniqueness holds without strict monotonicity of couplings or strict convexity of Hamiltonians, and in the degenerate case, it relies on a new type of argument for (nonlocal) Fokker-Planck equations. We consider a rich class of nonlocal operators and processes and develop tools to work in the whole space without explicit moment assumptions.
Operator Splitting for Learning to Predict Equilibria in Convex Games

Systems of competing agents can often be modeled as games. Assuming rationality, the most likely outcomes are given by an equilibrium (e.g. a Nash equilibrium). In many practical settings, games are influenced by context, i.e. additional data beyond the control of any agent (e.g. weather for traffic and fiscal policy for market economies). Often the exact game mechanics are unknown, yet vast amounts of historical data consisting of (context, equilibrium) pairs are available, raising the possibility of learning a solver which predicts the equilibria given only the context. We introduce Nash Fixed Point Networks (N-FPNs), a class of neural networks that naturally output equilibria. Crucially, N- FPNs employ a constraint decoupling scheme to handle complicated agent action sets while avoiding expensive projections. Empirically, we find N-FPNs are compatible with the recently developed Jacobian-Free Backpropagation technique for training implicit networks, making them significantly faster and easier to train than prior models. Our experiments show N-FPNs are capable of scaling to problems orders of magnitude larger than existing learned game solvers.