Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflammatory Bowel Diseases and Non-Alcoholic Fatty Liver Disease: Piecing a Complex Puzzle Together

Inflammatory bowel diseases (IBD), comprising Crohn’s disease and ulcerative colitis, are systemic and multifaceted disorders which affect other organs in addition to the gastrointestinal tract in up to 50% of cases. Extraintestinal manifestations may present before or after IBD diagnosis and negatively impact the intestinal disease course and patients’ quality of life, often requiring additional diagnostic evaluations or specific treatments. Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide. Current evidence shows an increased prevalence of NAFLD (and its more advanced stages, such as liver fibrosis and steatohepatitis) in IBD patients compared to the general population. Many different IBD-specific etiopathogenetic mechanisms have been hypothesized, including chronic inflammation, malabsorption, previous surgical interventions, changes in fecal microbiota, and drugs. However, the pathophysiological link between these two diseases is still poorly understood. In this review, we aim to provide a comprehensive overview of the potential mechanisms which have been investigated so far and highlight open issues still to be addressed for future studies.
Learning Patterns of Activity Using Real-Time Tracking

Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site.
An Accurate Analytical Method to Determine the Range of Quiet Zone in Compact Antenna Test Range

Compact antenna test range (CATR) can generate a region of uniform pseudo-plane wave called quiet zone, which can provide a test zone for obtaining far-field measurement data of high-performance antenna in the near field. The most important thing to be concerned in CATR is the evaluation of quiet zone. However, most of CATRs are mainly composed of reflectors, which are electric large size antennas, especially in millimeter (mm) and terahertz (THz) waveband. Thus, it is time-consuming and inefficient to use full-wave simulation method, such as the method of moment (MoM) or finite-difference time domain (FDTD) to analyze their performance, and high-frequency solving techniques cannot calculate the exact radiated field in some cases. In this article, an accurate analytical method to fast determine the quiet zone of CATR is proposed. The aperture electric field distribution on the surface of main reflector can be obtained by the modified near-field physical optics (PO) algorithm. According to the known aperture distribution, the near-field calculation formula is derived to calculate near fields of it, and the correctness of our proposed expression is verified via the comparison with the reflector antenna working in the mm waveband with full-wave simulated method. Our proposed expression can be applied to the fast estimation of quiet zone. Different typical kinds of antennas applied in the compact range are employed for verification, and the deviations between quiet zone amplitudes calculated by the proposed expression and amplitudes obtained from simulation or published paper are both less than 0.5 dB. Our analytical method can ensure the calculation accuracy and improve the speed of quiet zone determination at the same time.
Endocannabinoid Release Modulates Electrical Coupling between CCK Cells Connected via Chemical and Electrical Synapses in CA1

Electrical coupling between some subclasses of interneurons is thought to promote coordinated firing that generates rhythmic synchronous activity in cortical regions. Synaptic activity of cholecystokinin (CCK) interneurons which co-express cannabinoid type-1 (CB1) receptors are powerful modulators of network activity via the actions of endocannabinoids. We investigated the modulatory actions of endocannabinoids between chemically and electrically connected synapses of CCK cells using paired whole-cell recordings combined with biocytin and double immunofluorescence labeling in acute slices of rat hippocampus at P18–20 days. CA1 stratum radiatum CCK Schaffer collateral-associated cells were coupled electrically with each other as well as CCK basket cells and CCK cells with axonal projections expanding to dentate gyrus. Approximately 50% of electrically coupled cells received facilitating, asynchronously released inhibitory postsynaptic potential (IPSPs) that curtailed the steady-state coupling coefficient by 57%. Tonic CB1 receptor activity which reduces inhibition enhanced electrical coupling between cells that were connected via chemical and electrical synapses. Blocking CB1 receptors with antagonist, AM-251 (5 μM) resulted in the synchronized release of larger IPSPs and this enhanced inhibition further reduced the steady-state coupling coefficient by 85%. Depolarization induced suppression of inhibition (DSI), maintained the asynchronicity of IPSP latency, but reduced IPSP amplitudes by 95% and enhanced the steady-state coupling coefficient by 104% and IPSP duration by 200%. However, DSI did not did not enhance electrical coupling at purely electrical synapses. These data suggest that different morphological subclasses of CCK interneurons are interconnected via gap junctions. The synergy between the chemical and electrical coupling between CCK cells probably plays a role in activity-dependent endocannabinoid modulation of rhythmic synchronization.
Statistical theory of the continuous double auction

Abstract Most modern financial markets use a continuous double auction mechanism to store and match orders and facilitate trading. In this paper we develop a microscopic dynamical statistical model for the continuous double auction under the assumption of IID random order flow, and analyse it using simulation, dimensional analysis, and theoretical tools based on mean field approximations. The model makes testable predictions for basic properties of markets, such as price volatility, the depth of stored supply and demand versus price, the bid–ask spread, the price impact function, and the time and probability of filling orders. These predictions are based on properties of order flow and the limit order book, such as share volume of market and limit orders, cancellations, typical order size, and tick size. Because these quantities can all be measured directly there are no free parameters. We show that the order size, which can be cast as a non-dimensional granularity parameter, is in most cases a more significant determinant of market behaviour than tick size. We also provide an explanation for the observed highly concave nature of the price impact function. On a broader level, this work suggests how stochastic models based on zero intelligence agents may be useful to probe the structure of market institutions. Like the model of perfect rationality, a stochastic zero intelligence model can be used to make strong predictions based on a compact set of assumptions, even if these assumptions are not fully believable.
The load, capacity and availability of quorum systems

A quorum system is a collection of sets (quorums) every two of which have a nonempty intersection. Quorum systems have been used for a number of applications in the area of distributed systems. We investigate the load, capacity and availability of quorum systems. We present four novel constructions of quorum system, all featuring optimal or near optimal load, and high availability. These desirable properties of the constructions translate into improvements of any protocol using them: a low work load on the processors and a high resilience to processor failures. The best construction, based on paths in a grid, has a load of O(1//spl radic/n), and a failure probability of exp(-O(/spl radic/n)) when the elements fail with probability p< 1/2 . Moreover, even in the presence of faults, with exponentially high probability the load of this system is still O(1//spl radic/n). The analysis of this scheme is based on Percolation Theory. <<ETX>>
Relocation of α5 subunit to uncover the β1 stalk region

K562 cells treated with ligand peptide. By contrast, it immunoprecipitated only β1 chains when the ligand peptide was absent, suggesting that high affinity binding sites on EDTA-treated cells are associated with nonfunctional β1 monomer. Additional studies show that the epitope for AG89 is constitutively exposed on mutant β1 that cannot complex with α5. These data suggest that the AG89 epitope is masked by the α5 subunit. Ligand binding and integrin activation may uncover the β1 stalk region by triggering a conformational shift of α5 relative to β1. SUMMARY
Watershed Evaluation and Habitat Response to Recent Storms : Annual Report for 1998.

Large and powerful storm systems moved through the Pacific Northwest during the wet season of 1995-96, triggering widespread flooding, mass erosion, and, possibly altering salmon habitats in affected watersheds. This project study was initiated to assess whether watershed conditions are causing damage, triggered by storm events, to salmon habitat on public lands in the Snake River basin.
Fin textures for real-time painterly aesthetics

We present a novel method for real-time stylized rendering in video games. Recent advances in painterly character authoring and rendering allow artists to create characters represented by 3D geometry as well as 3D paint strokes embedded on and around that geometry. The resulting 3D paintings are rendered in screen space using special-purpose offline rendering algorithms to achieve a unique painterly style. While providing novel styles for offline rendering, existing techniques do not support real-time applications. In this paper, we propose a method to interactively render these complex 3D paintings with a focus on character animation in video games. After observing that off-surface paint strokes can be interpreted as volumetric data in the proximity of 3D meshes, we review existing volumetric texture techniques and show that they are not adapted to paint strokes, which can be sparse and have a significant structure that should be preserved. We propose a method based on fin textures in which mesh edges are extended orthogonally off the surface and textured to replicate the results of the custom offline rendering method. Our algorithm uses a per-pixel normal calculation in order to fade in fin textures along boundary views. Our results demonstrate real-time performance using a commodity game engine while maintaining a painterly style comparable to offline methods.
Finite state residual vector quantizer for image coding

Finite state vector quantization (FSVQ) has been proven to be a high‐quality and low‐bit rate coding scheme. An FSVQ has achieved the efficiency of a small codebook (the state codebook) VQ while maintaining the quality of a large codebook (the master codebook) VQ. However, the large master codebook becomes a primary limitation of FSVQ when the implementation is carefully taken into account. A large amount of memory would be required in storing the master codebook, and much effort would be spent in maintaining the state codebook if the master codebook became too large. This problem could be partially solved by the mean/residual technique (MRVQ)‐that is, the block means and the residual vectors would be separately coded. However, MRVQ has its own drawbacks. Additional bits would be required in coding those means. Moreover, electing the state codebooks in the residual domain would be difficult. A new hybrid coding scheme called the finite state residual vector quantization (FSRVQ) is proposed in this article for the sake of using both advantage in FSVQ and MRVQ. The codewords in FSRVQ were designed by removing the block means to reduce the codebook size. The block means were predicted by the neighboring blocks to reduce the bit rate. In addition, the predicted means were added to the residual vectors so that the state codebooks could be generated entirely. The performance of FSRVQ was indicated from the experimental results to be better than that of both ordinary FSVQ and MRVQ uniformly. ©1994 John Wiley & Sons Inc
DYNAMIC SYSTEM OF TUBERCULOSIS MODEL USING OPTIMAL CONTROL IN SEMARANG CITY INDONESIA

Tuberculosis is a disease which is very contagious among human. To prevent this from happening, Semarang city government has enacted vaccination for exposed individuals and treatment for the infected individuals. Vaccination and treatment are forms of control that will be applied to dynamic model systems of Tuberculosis. The present paper will describe epidemic model of Tuberculosis with control using Pontryagin Minimum Principle to find optimal solution of the control with fixed time and free end point. The optimal control will aim to reduce or minimize the number of infected populations. Numerical calculation is carried out with MATLAB software programming to illustrate and compare the graph of the dynamic model with and without optimal control. The results of dynamic modeling of Tuberculosis with control state that vaccination and treatment have succeeded in reducing the population of infected individuals.
Load Shedding in Classifying Multi-Source Streaming Data: A Bayes Risk Approach

Monitoring multiple streaming sources for collective decision making presents several challenges. First, streaming data are often of large volume, fast speed, and highly bursty nature. Second, it is impossible to offload classification decisions to individual data sources, each of which lacks full knowledge for the decision making. Hence, the central classifier responsible for decision making may be frequently overloaded. In this paper, we study intelligent load shedding for classifying multi-source data. We aim at maximizing classification quality under resource (CPU and bandwidth) constraints. We use a Markov model to predict the distribution of feature values over time. Then, leveraging Bayesian decision theory, we use Bayes risk analysis to model the variances among different data sources in their contributions to the classification quality. We adopt an Expected Observational Risk criterion to quantify the loss of classification quality due to load shedding, and propose a Best Feature First (BFF) algorithm that greedily minimizes such risk. The effectiveness of the approach proposed is confirmed by experiments.
Host-based code injection attacks: A popular technique used by malware

Common goals of malware authors are detection avoidance and gathering of critical information. There exist numerous techniques that help these actors to reach their goals. One especially popular technique is the Host-Based Code Injection Attack (HBCIA). According to our research 63.94% out of a malware set of 162850 samples use HBCIAs. The act of locally copying malicious code into a foreign process space and subsequently executing it is called a Host-Based Code Injection Attack. In this paper, we define HBCIAs and introduce a taxonomy for HBCIA algorithms. We show that a HBCIA algorithm can be broken down into three steps. In total there are four classes of HBCIA algorithms. Then we examine a huge set of malware samples and estimate the prevalence of HBCIA-employing malware and their target process distribution. Moreover, we analyse Intrusion Prevention System data and show that HBCIA-employing malware prefers network-related processes for its network communication. To the best of our knowledge, we are the first to thoroughly describe and formalize this phenomenon and give an estimation of its prevalence. Thus, we build a solid foundation for future work on this topic.
Using Finite-State Automata to Model and Solve Planning Problems

In the recent years, the representation using the state variables, i.e., SAS+ planning, has become one of the most popular formalisms for specifying the planning problems (and domains). Still, the great majority of the planning tasks, especially from the past planning competitions, are defined using the classical STRIPS-like formalism, and so an efficient transformation technique is required in order to allow performance comparison of individual planners. In the paper we present the new formalism that is based on finite-state automata (FSA), and that represents an alternative to the SAS + (state variables), providing thus a novel platform for development of the modern planners. Finally, we also propose conversion procedures between the individual planning problem representations, together with the basics of automated and semi-automated techniques that employ the new formalism and aim towards the extraction of the state variables representation from the classical representation.
Ssh! We are adding a process... [agile practices]

Google is very successful in maintaining its startup culture which is very open and engineering-centric. Project teams don't have a project manager, but organize themselves and communicate directly with all stakeholders. Most feature decisions are made by the engineering teams themselves. As well as this works for products like search, gmail. it creates issues for the AdWords frontend (AWFE) application. AWFE is much more product management and release date driven then other Google applications. This presentation discusses how we carefully introduced agile practices to coordinate the AWFE development teams and made the process more efficient and predictable
Functional Conservation in the SIAMESE-RELATED Family of Cyclin-Dependent Kinase Inhibitors in Land Plants

Although they play roles in many different developmental processes, the biochemical function of SMR-type CDK inhibitors is conserved among land plants. The best-characterized members of the plant-specific SIAMESE-RELATED (SMR) family of cyclin-dependent kinase inhibitors regulate the transition from the mitotic cell cycle to endoreplication, also known as endoreduplication, an altered version of the cell cycle in which DNA is replicated without cell division. Some other family members are implicated in cell cycle responses to biotic and abiotic stresses. However, the functions of most SMRs remain unknown, and the specific cyclin-dependent kinase complexes inhibited by SMRs are unclear. Here, we demonstrate that a diverse group of SMRs, including an SMR from the bryophyte Physcomitrella patens, can complement an Arabidopsis thaliana siamese (sim) mutant and that both Arabidopsis SIM and P. patens SMR can inhibit CDK activity in vitro. Furthermore, we show that Arabidopsis SIM can bind to and inhibit both CDKA;1 and CDKB1;1. Finally, we show that SMR2 acts to restrict cell proliferation during leaf growth in Arabidopsis and that SIM, SMR1/LGO, and SMR2 play overlapping roles in controlling the transition from cell division to endoreplication during leaf development. These results indicate that differences in SMR function in plant growth and development are primarily due to differences in transcriptional and posttranscriptional regulation, rather than to differences in fundamental biochemical function.
The Phosphoprotein StarD10 Is Overexpressed in Breast Cancer and Cooperates with ErbB Receptors in Cellular Transformation

We have identified that StarD10, a member of the START protein family, is overexpressed in both mouse and human breast tumors. StarD10 was initially discovered on the basis of its cross-reactivity with a phosphoserine-specific antibody in mammary tumors from Neu/ErbB2 transgenic mice and subsequently isolated from SKBR3 human breast carcinoma cells using a multistep biochemical purification strategy. We have shown that StarD10 is capable of binding lipids. StarD10 was found to be overexpressed in 35% of primary breast carcinomas and 64% of human breast cancer cell lines, correlating with their ErbB2/Her2 status. Coexpression of StarD10 with ErbB1/epidermal growth factor receptor in murine fibroblasts enhanced anchorage-independent growth in soft agar, providing evidence for functional cooperation between StarD10 and ErbB receptor signaling. Taken together, these data suggest that overexpression of this lipid-binding protein contributes to breast oncogenesis.
Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions
Online scheduling for FPGA computation in the Cloud

The popularization and application of Cloud Computing have provided a new approach for users to get computing resources in recent years. Meanwhile, due to the advantages including programmability and power-efficiency, FPGAs have been applied to custom computing in many domains. Previous work has made resources of FPGA available under the cloud environment. However, the effective usage of FPGAs in the cloud requires efficient online task scheduling: to properly assign as many tasks from different tenants as possible to the FPGAs. In this paper, we propose a benefit-based scheduling metric to evaluate the task assignment Based on the metric, we accelerate task execution according to our benefit-based scheduling algorithms. By applying our benefit-based scheduling metric to a real OpenStack-based cloud environment, 60.32% computing resources are saved compared with the conventional throughput-based metric. Furthermore, a Replacement-Considering algorithm, which considers the task replacement, is proposed taking the characteristics of cloud into account. The results show that our FPGA accelerated cloud system is 1.386 times faster than using the previous algorithm.