Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral / Systems / Cognitive Dynamics of Cortical Neuronal Ensembles Transit from Decision Making to Storage for Later Report

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamics of Cortical Neuronal Ensembles Transit from Decision Making to Storage for Later Report Adrián Ponce-Alvarez, Verónica Nácher, Rogelio Luna, Alexa Riehle, Ranulfo Romo
Predatory Lending Laws and the Cost of Credit Authors :

Various states and other local jurisdictions have enacted laws intending to reduce predatory and abusive lending in the subprime mortgage market. These laws have created substantial geographic variation in the regulation of mortgage credit. This article examines whether these laws are associated with a higher or lower cost of credit. Empirical results indicate that the laws are associated with at most a modest increase in cost. However, the impact depends on the product type. In particular, loans with fixed (adjustable) rates are associated with a modest increase (decrease) in cost. Predatory lending laws are today’s usury laws. The laws focus on the high cost or subprime segment of the mortgage market and typically restrict certain types of loans such as loans with prepayment penalties and balloon payments. Those borrowers who are still able to get a loan when a law is in place may be required to pay for at least part of the regulatory costs associated with complying or violating the laws (assuming compliance is nontrivial). This article tests to see whether the existence of a predatory lending law is associated with systematic change in annual percentage rates (APRs)—which represent the full cost of borrowing, including upfront points and fees—or with higher periodic interest rates. In addition, a law index is used to measure the relative strength of each law and test whether stronger laws, in terms of restrictions and coverage, differentially impact the cost of credit. The major findings of this article are that Home Mortgage Disclosure Data (HMDA), even with the interest rate information collected since 2004, provide insufficient information to learn about the impact of predatory lending laws on the cost of credit. However, using a more detailed and national data set of subprime loans, we find that stronger laws, as measured by their extent of restrictiveness, tend to increase the cost of borrowing on fixed-rate loans. In contrast, stronger laws tend to be associated with a lower cost for adjustable-or hybrid-rate loans. We propose that this potential paradox can be explained by the ability of the lender to pass the costs of regulatory compliance for adjustable-rate loans on to the consumer in ways that do not affect the APR calculation. The introduction of predatory lending laws at the state, county and city levels has provided substantial geographic variation in the regulation of high-cost mortgage credit. We largely focus on the impact of state laws because they have been the most durable in the face
L G ] 1 9 M ay 2 01 9 Probability Functional Descent : A Unifying Perspective on GANs , Variational Inference , and Reinforcement Learning

The goal of this paper is to provide a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.
Identifying Events In Social Media Streams In Real Time Using Semantic Analysis Of Irrelevant Phrases

: Social media interactions have made it possible for everyone, regardless of location, to obtain instant access to information about events occurring across the world. Nevertheless, the semantic analysis of social media data is hampered by obstacles such as linguistic complexity, unstructured data, and ambiguity. In this research, the Social Media Analysis Framework for Event Detection was suggested (SMAFED). SMAFED seeks to enhance semantic analysis of noisy phrases in social media streams, content representation/embedding, and event cluster summarization in social media streams. We used essential ideas such as integrated knowledge base, addressing ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness to accomplish this. Two tests were done to verify the methodology. Initially, we assessed the effect of SMAFED's data enrichment layer. SMAFED beat alternative pre-processing frameworks with a loss function of 0.15 on the first dataset and 0.05 on the second. Second, we evaluated SMAFED's accuracy in detecting events in social media streams. This second experiment demonstrated that SMAFED outperformed current event detection methods by achieving higher Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The results of the research demonstrate that SMAFED is a more effective method for event identification in social media.
Generative Squeeze Net-Unet Model For Image Reconstruction

In recent times, Artificial Intelligence (AI) attracted many researchers in medical image analysis. Analyzing the vast medical data through traditional approaches is a bit tedious and time-consuming in designing feature descriptors. Therefore, we present a Generative Squeeze Net-UNet framework for image retrieval in this paper. Proposed framework consists of an image reconstruction module using an encoder with series of six encoder blocks taken from Squeeze Net and a sequential decoder from UNet with transpose fire modules. Also, the image matching module is followed by retrieval of similar images from database. Encoder module of the proposed framework consists of encoder blocks with Squeeze Net architecture that encode the features from a given image with different kernel size and are forwarded to decoder module. Subsequently, reconstruction of image takes place. Extracted features form the basis for reconstruction of input image. Encoded latent features are used for image matching and retrieval of similar images from a database. Performance of the Generative Squeeze Net-UNet framework is analyzed using standard image databases like VIA/ELCAP-CT and OASIS. Proposed method is compared with the state-of-the-art deep learning as well as local descriptor approaches. Average precision and recall are calculated against two datasets. Metrics suchas d1 and Euclidian distances are used in the process. Experimental results show that the proposed framework outperforms the existing works.
JoMSS JOURNAL OF MANAGERIAL SCIENCES AND STUDIES

____________________________________________________________ The goal of marketing exploitation methods, sometimes referred to as marketing exploration, can be to increase the company's knowledge and capabilities or to improve its current capabilities. According to a study on organisational strategy and learning, using both strategies can compromise each company's operational processes and harm its financial success. Market organisation, in our opinion, facilitates the flow of market information between the two strategy processes, integrates the two strategies by acting as a dynamic market, and aids businesses in integrating marketing exploitation and exploration strategies. Using complementary high-level marketing exploration and marketing exploitation strategies, companies with strong market orientation can improve the financial performance of new products, as measured at two different points in time, according to a study of the packaged food industry in Indonesia. However, as the trade-off foresees
Trend of Stunting Weight for Infants and Toddlers Using Decision Tree

— This papar is to show the trend of increasing the weight of infants and toddlers is of particular concern to the Indonesian government. This encourages the government to avoid stunting and malnutrition that can affect babies aged 0-59 months. The Indonesian government quickly established the Integrated Health Service Post (POSYANDU) to reduce the number of malnutrition in infants and toddlers. With the existence of POSYANDU, it is hoped that infants and toddlers can avoid malnutrition. The purpose of this study was to see the trend of weight gain for infants and toddlers aged 0-59 months to ensure that the weight of infants and toddlers continues to increase from month to month. To be able to see the trend of weight gain, a decision tree classification algorithm is needed, to see the weight gain of infants and toddlers. In addition, the feature selection process uses the single factor ANOVA method in determining the feature approach possessed by the dataset. This study will also compare the decision tree algorithm with the KNN in determining the classification of weight gain for infants and toddlers. Then the results show that the decision tree algorithm and ANOVA are better at providing accuracy, recall, F1 score, and precision than the KNN classification algorithm. Thus the decision tree + ANOVA algorithm is a classification algorithm used to see the trend of weight gain in infants and toddlers aged 0-59 months.
Socio-Demographics As Risk And Protective Factors Of Mental Health Among The Adolescents With Vision Loss

The present study explored the roles of demographic characteristics of the adolescents with visual impairment in their mental health. The most common mental health issues are depression and anxiety therefore this study focus on exploring the socio-demographic factors in determining depression and anxiety among the participants. Total 815 (Male, 468; Female= 347) visually impaired adolescents with age ranged from 12 to 18 years (M=15.50, SD= 2.65) were selected from the special education schools located in the four cities of Pakistan. Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 were administered to assess depression and anxiety. The results indicated that medical facilities, access to counseling, higher education of father and mother, higher monthly income and presence of any other family member with vision loss were protective factors whereas severity level of disability emerged as a risk factor of depression and anxiety among the adolescents with vision loss. It is concluded on the findings of the present study that not only the disability itself but the other socio-demographic characteristics play important role in determining anxiety and depression among the adolescents with visual impairment therefore these variables must be considered while designing and introducing mental health programs at prevention and treatment levels.
(2015). Optimised OpenCL workgroup synthesis for hybrid ARM-FPGA devices. In 2015 25th

—This paper presents a workgroup synthesis mechanism to compile an OpenCL kernel to FPGA-based accelerators embedded in a multi-core CPU system-on-a-chip (SoC). The OpenCL kernels considered in this paper exhibit regular data access patterns. Coping with the limited amount of internal memory in embedded FPGAs, the workgroup synthesis utilises a novel data access pattern formulation to describe the parallelism already provided by the OpenCL kernels. To provide an OpenCL framework prototype to validate the proposed technique, a source-to-source compiler that transforms the OpenCL kernel into C/C++ code is developed. Then vendor-speciﬁc high-level synthesis tools are used to convert the C/C++ code into the FPGA bitstream. Results based on popular real applications show up to 89.8% improvement in the execution time compared to the commercial FPGA OpenCL implementations. Index Terms
choice. Robust control of mitotic spindle orientation in the developing

During development of the epidermis, basal progenitor cells undergo both symmetric cell divisions (SCDs) to increase surface area and asymmetric cell divisions (ACDs) to increase thick­ ness of the tissue (Fig. 1 A; Smart, 1970; Lechler and Fuchs, 2005). In SCD, mitotic spindles are oriented parallel to the under­ lying basement membrane, whereas ACDs have spindles per­ pendicular to it (Fig. 1 A). A primary role for the perpendicular spindles is to directly drive the morphogenesis (stratification) of this tissue. These divisions are also associated with a change in cell fate, as they produce one daughter cell in the proliferative basal layer of the epidermis and another daughter committed to differentiation in the suprabasal cell layer. Little is known about how the balance of SCD/ACD is controlled to allow proper development. A conserved complex of proteins (including Par3, mouse Inscuteable [Insc; mInsc], Leu­Gly­Asn–enriched protein [LGN], and NuMA) localizes to the apical cell cortex during ACDs (Kraut et al., 1996; Schober et al., 1999; Parmentier et al., 2000; Lechler and Fuchs, 2005; Siller et al., 2006). These proteins are required for orienting the mitotic spindle along the apical–basal axis of the cell. Beyond their cell cycle–dependent expression and localization, we know very little about the control of these proteins in the epidermis and whether their expression and/or activity is regulated to direct ACDs.
A Game-Theoretic Approach for Mitigating Jamming Attacks in LPWAN

Internet of Things (IoT) applications rely on low-power wide-area networks (LPWANs) for gathering data from widely dispersed devices over long distances. As IoT applications are growing in various domains, LPWANs are getting prone to jamming attacks. Jamming causes excessive packet loss, throughput reduction, long delays, and lower energy efficiency in these networks. Existing anti-jamming work in IoT mainly considers jamming in the communication channel between the gateway/base station and the server. In this paper, we propose to mitigate jamming in LPWANs using a game theoretic approach. To our knowledge, this is the first work on anti-jamming in LPWAN. Game theory can realistically model the interaction between jammer and LPWAN nodes because both try to maximize their utility while minimizing their opponent’s. While it has been used for anti-jamming approaches in various wireless networks, they either make simplified assumptions or are not directly extendable to LPWANs due to their devices’ severe power constraints. A key characteristic of our approach is that we offload all the computations and communications needed for playing the games to the base station that has more power and energy, thereby preserving the energies of the LPWAN devices. The proposed game theoretic approach is designed considering SNOW (Sensor Network Over White spaces), an LPWAN architecture to support scalable wide-area IoT over the TV white spaces. We develop the game based on frequency hopping and transmission power adjustment as anti-jamming actions. Finally, we evaluate the effectiveness of our jamming mitigation technique through both physical experiments and NS-3 simulations. The results show that our technique mitigates jamming by improving packet reception rate, throughput, and energy consumption per packet up to 31.83, 30.77, and 34 times, respectively.
A host of armor Prokaryotic immune strategies against mobile genetic elements

Prokaryotic adaptation is strongly inﬂuenced by the horizontal acquisition of beneﬁcial traits via mobile ge-netic elements (MGEs), such as viruses/bacteriophages and plasmids. However, MGEs can also impose a ﬁtness cost due to their often parasitic nature and differing evolutionary trajectories. In response, prokaryotes have evolved diverse immune mechanisms against MGEs. Recently, our understanding of the abundance and diversity of prokaryotic immune systems has greatly expanded. These defense systems can degrade the invading genetic material, inhibit genome replication, or trigger abortive infection, leading to population protection. In this review, we highlight these strategies, focusing on the most recent discoveries. The study of prokaryotic defenses not only sheds light on microbial evolution but also uncovers novel enzymatic activities with promising biotechnological applications.
An Efficient Fog-Assisted Framework For Wireless Sensor Networks By Exploiting Binary Edwards Curves Cryptography

Intelligent Sensing and IoT applications have joined forces in critical missions since the advent of Internet technology. Wireless Sensor Networks (WSNs), for example, serve as the primary activation technology in IoT architectures and expand their range of intelligent applications. This network should use dependable and cost-effective network techniques while adhering to the constraints of low power consumption and transmission. However, this technology is resource constrained and suffers from a number of vulnerabilities and security flaws. The goal of this proposal is to use the BEC encryption method to improve the efficiency of the system's security structure and to adapt it to IoT network characteristics such as resource constraints and specific attacks on these networks. To simulate, the Ifogsim simulator is used. By examining the simulation results, we concluded that the proposed solution has a better status compared to the basic article method, both in terms of encryption time and in terms of authentication time. In the case of the network consumption factor, the number of packages that should have been exchanged for various operations, such as authentication through gateways, has been reduced by using the fog element. Data recovery and security-related operations have also decreased. In terms of privacy protection, because gateway encryption was done in groups, data owners' privacy was preserved, and this shortcoming was actually compensated for in the basic article method.
Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing

Abstract Global satellite remote sensing records show evidence of recent vegetation greening and an advancing growing season at high latitudes. Satellite remote sensing–derived measures of photosynthetic leaf area index (LAI) and vegetation gross and net primary productivity (GPP and NPP) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder record are utilized to assess annual variability in vegetation productivity for Alaska and northwest Canada in association with the Western Arctic Linkage Experiment (WALE). These results are compared with satellite microwave remote sensing measurements of springtime thaw from the Special Sensor Microwave Imager (SSM/I). The SSM/I-derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of ap.
APR-246 increases tumor antigenicity independent of p53

Michels, Venkatesh et al demonstrate that APR-246, a small molecule Michael acceptor, enhances tumor immunogenicity. Combination of APR-246 with immune modulation enhances antitumor responses. APR-246, initially described as a P53 stabilizer, induces immunogenicity irrespective of the tumor’s p53 status. We previously reported that activation of p53 by APR-246 reprograms tumor-associated macrophages to overcome immune checkpoint blockade resistance. Here, we demonstrate that APR-246 and its active moiety, methylene quinuclidinone (MQ) can enhance the immunogenicity of tumor cells directly. MQ treatment of murine B16F10 melanoma cells promoted activation of melanoma-specific CD8+ T cells and increased the efficacy of a tumor cell vaccine using MQ-treated cells even when the B16F10 cells lacked p53. We then designed a novel combination of APR-246 with the TLR-4 agonist, monophosphoryl lipid A, and a CD40 agonist to further enhance these immunogenic effects and demonstrated a significant antitumor response. We propose that the immunogenic effect of MQ can be linked to its thiol-reactive alkylating ability as we observed similar immunogenic effects with the broad-spectrum cysteine-reactive compound, iodoacetamide. Our results thus indicate that combination of APR-246 with immunomodulatory agents may elicit effective antitumor immune response irrespective of the tumor’s p53 mutation status.
Improvement the Expression and Puri�cation of Loa22: A Lipoprotein with OmpA domain from Pathogenic Leptospira Serovars

Background Outer membrane proteins (OMPs) are the main surface antigens of the pathogenic Leptospires. One of the highly conserved outer membrane proteins expressed only by pathogenic Leptospires is Loa22. This study aims is to obtain the optimum conditions for high expression and puri�cation of Loa22 recombinant (rLoa22) protein.
Intracranial pressure elevations in diffuse axonal injury: association with nonhemorrhagic MR lesions in central mesencephalic structures

OBJECTIVE Increased intracranial pressure (ICP) in patients with severe traumatic brain injury (TBI) with diffuse axonal injury (DAI) is not well defined. This study investigated the occurrence of increased ICP and whether clinical factors and lesion localization on MRI were associated with increased ICP in patients with DAI. METHODS Fifty-two patients with severe TBI (median age 24 years, range 9–61 years), who had undergone ICP monitoring and had DAI on MRI, as determined using T2*-weighted gradient echo, susceptibility-weighted imaging, and diffusion-weighted imaging (DWI) sequences, were enrolled. The proportion of good monitoring time (GMT) with ICP > 20 mm Hg during the first 120 hours postinjury was calculated and associations with clinical and MRI-related factors were evaluated using linear regression.
Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Background: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. Objective: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels ( ≥ 7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. Methods: This study was conducted at KK Women’s and Children’s Hospital in Singapore
MIXED METHOD SYSTEMATIC REVIEW: THE RELATIONSHIP BETWEEN BREAST CANCER RISK PERCEPTION AND HEALTH-PROTECTIVE BEHAVIOUR IN WOMEN WITH FAMILY HISTORY OF BREAST CANCER Running head: Health-protective behaviour in women at high risk of breast cancer

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jan.13158 This article is protected by copyright. All rights reserved. Received Date : 03-Nov-2015 Revised Date : 25-Jul-2016 Accepted Date : 30-Aug-2016 Article type : Review