Miracle-dz/UniFAR-FTUs huggingface dataset:
Muscle-Specific RING Finger 1 Negatively Regulates Pathological Cardiac Hypertrophy Through Downregulation of Calcineurin A

Background—Muscle-specific RING finger protein-1 (MuRF1) is an E3 ligase that inhibits cardiac hypertrophy. However, how MuRF1 regulates cardiac hypertrophy and function during pressure overload (PO) remains poorly understood. We investigated the role of endogenous MuRF1 in regulating cardiac hypertrophy in response to PO in vivo. Methods and Results—Transverse aortic constriction (TAC) for 4 weeks significantly reduced expression of MuRF1 in the mouse heart. After 2 and 4 weeks of TAC, MuRF1 knockout (Murf1−/−) mice exhibited enhanced cardiac hypertrophy and left ventricular (LV) dysfunction compared with that of nontransgenic (NTg) mice. Histological analyses showed that Murf1−/− mice exhibited more severe fibrosis and apoptosis than NTg mice after TAC. TAC-induced increases in the activity of a nuclear factor of activated T cells (NFAT) luciferase reporter were significantly greater in Murf1−/− than in NTg mice. TAC-induced increases in calcineurin A (CnA) expression were also significantly enhanced in Murf1−/− compared with that in NTg mice. Coimmunoprecipitation assays showed that endogenous MuRF1 and CnA interact with one another. Polyubiquitination of CnA was attenuated in Murf1−/− mouse hearts at baseline and in response to TAC, and the protein stability of CnA was enhanced in cardiomyocytes, in which MuRF1 was downregulated in vitro. Furthermore, MuRF1 directly ubiquitinated CnA in vitro. Cardiac-specific overexpression of ZAKI-4&bgr;, an endogenous inhibitor of CnA, significantly suppressed the enhancement of TAC-induced cardiac hypertrophy and dysfunction, as well as increases in cardiac fibrosis and apoptosis, in Murf1−/− mice. Conclusions—Endogenous MuRF1 negatively regulates cardiac hypertrophy and dysfunction in response to PO through inhibition of the calcineurin–NFAT pathway.
Rethink prevalent machine learning attack detection methods from a generalization perspective

To address attacks from different threat actors and scenarios, the generalizability of network attack detection methods is crucial for critical infrastructure like the power system. Machine learning-based attack detection has become the de facto standard solution in this field. However, existing research has primarily focused on scenarios where training and testing distributions are consistent, overlooking the machine learning models' ability to generalize to different distributions. This paper emphasizes two key dimensions: the impact of background traffic and the role of statistical features in the detection process. By embedding identical attack behaviors in different background flows, we reveal significant performance disparities in different traffic scenarios (data domains), highlighting the insufficient generalizability of existing methods. Through a comparison between DDoS attacks and XSS attacks, we identify the limitations of relying solely on statistical data for detecting various attack behaviors. Based on the new research perspective proposed in this paper for the attack detection domain, we advocate for adopting a comprehensive approach that integrates both feature and behavior considerations. It involves incorporating security domain knowledge before applying machine learning to enhance the generalization performance of machine learning-based attack detection methods in the complex and dynamic network environment of power systems.
rankIMPACT: A Study of Rank Attacks impact over RPL based Internet of Things Networks

One of these rapidly evolving technologies, the Internet of Things (IoT), is increasingly demonstrating its capacity to enable more efficient and sustainable smart city infrastructure. Smart cities rely heavily on IoT apps because of their many advantages and ease of integration. The "Internet of Things in Industry," also known as the Industrial Internet of Things (IIoT), is a domain where linked devices are utilized to deliver a range of services related to operational technology, manufacturing, utilities, and machine monitoring. IoT is however susceptible to a number of significant security problems that need to be addressed because of its broad use. In environments where IIoT resources are limited, the Routing Protocol for Low Power and Lossy Networks (RPL), based on IPv6, is the ideal choice. Additionally, RPL is more susceptible to a variety of attacks and, if they are not handled, could result in a decline in network performance. We propose the study of rank attacks and their impacts on a network’s packet delivery ratio (PDR), throughput, inter-packet time, and power consumption are the main objectives of this work. Further, the examination is surely helpful in estimating the performance losses in the network and accordingly devising the mitigation models further. To this end, the results of the study have clearly shown evidence that the attack has significantly lowered the performance of RPL-based IoT and IIoT contexts.
An analysis of the ability of small urban wetlands to treat stormwater: the case of Princess Vlei wetland, Cape Town

The consequences of poor water quality on urban aquatic ecosystems have been well established by researchers worldwide. Stormwater management in the urban areas of South Africa predominately focuses on the collection and diversion of runoff into the nearest receiving water body, with little acknowledgement of the impacts on the environment. The City of Cape Town Metropolitan municipality is an authoritative entity on Cape Town pollution and has acknowledged that polluted stormwater is a significant contributor to the deterioration of its’ urban aquatic ecosystems due to the persistence of conventional drainage systems. Small urban wetlands are often overwhelmed by the quality of stormwater and urban runoff. Thus water bodies receiving urban stormwater runoff often have elevated loadings of pollutants. In theory wetlands are capable of treating these pollutants and improving water quality through various ecosystem services, but understanding the performance of wetlands under varying conditions is difficult to determine. In South Africa, there is a paucity of studies focussing on the impacts of urban development on small, urban wetlands and thus their ability to provide ecosystem services. This study aimed to identify the surface water quality of Princess Vlei, a small urban wetland, over the past 8 years, and establish the ingress and outflow of the wetland. The pollutant concentrations within the wetland were best explained by the predictor variables of total rainfall and progression of time. Impacts of total rainfall differed with various parameters resulting in larger volumes of water entering the wetland either diluting pollutant concentrations or elevating pollutant concentrations. These inverse trends were proved through the significant correlations found between total rainfall and COD and total rainfall and EC, while the variable of time influenced the wetland’s ability to provide ecosystem services, either through the accumulation, retention or flushing of pollutants. The accumulation of pollutants over time was identified through the increasing concentrations off COD and PO4 3, with the exception of NH3-N that decreased over time. This implies that the wetland was able to assimilate the NH3-N but not the COD and PO4 3. The results did not suggest that the wetland was able to treat the water, as the literature emphasises, rather, confirmed the pervasive impacts of the urban catchment on the ability of ecosystem services to treat water quality in the wetland.
A Conceptual Approach for Taxonomical Spectrum of Cloud Computing

Cloud computing has emerged as a popular computing model to support processing volumetric data using clusters of commodity computers. Nowadays computational world is opting for pay-for-use models and Hype and discussion aside, there remains no canonical definition of cloud computing. In this paper we proposed a wide spectrum of taxonomy for cloud computing, aiming at a better understanding of the categories of applications that could benefit from cloudification and that will address the landscape of enterprise IT, management services, and data governance etc.
Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification

Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between end-to-end learning and proper modeling of contextual information. Most state-of-the-art methods utilize a two-stage strategy, in which they use a pre-trained model to extract features of small patches cut from a WSI and then input these features into a classification model. These methods can not perform end-to-end learning and consider contextual information at the same time. To solve this problem, we propose a framework that models a WSI as a pathologist's observing video and utilizes Transformer to process video clips with a divide-and-conquer strategy, which helps achieve both context-awareness and end-to-end learning. Extensive experiments on three public WSI datasets show that our proposed method outperforms existing SOTA methods in both WSI classification and positive region detection.
A Stereo Matching Approach to Detect Obstacle in ALV System

In ALV system based on stereo vision, the obstacle detection is one of the most important problems. In this paper, an algorithm is proposed based on moment invariant to detect obstacle in ALV system. Firstly, a simple method is applied to binarize the images by the defined binarization function; Secondly, the binarized images are segmented using outer contour tracing algorithm and regions of left and right images are detected respectively; Lastly, the regions are matched between left and right image, the successful matched regions are the likely obstacle. Stereo pairs captured from ALV system are used to test the proposed algorithm, the result indicate that the approach is valid and feasible.
Evolution of Time Preferences and Attitudes Towards Risk

This paper explores a general model of the evolution and adaption of hedonic utility. It is shown that optimal utility will be increasing strongly in regions where choices have to be made often and decision mistakes have a severe impact on fitness. Several applications are suggested. In the context of intertemporal preferences, the model offers an evolutionary explanation for the existence of conflicting short- and long-run interests that lead to dynamic inconsistency. Concerning attitudes towards risk, an evolutionary explanation is given for S-shaped value functions that adjust to the decision-maker�s environment.
Semantic Indexing of a Competence Map to Support Scientific Collaboration in a Research Community

This paper describes a methodology to semiautomatically acquire a taxonomy of terms and term definitions in a specific research domain. The taxonomy is then used for semantic search and indexing of a knowledge base of scientific competences, called Knowledge Map. The KMap is a system to support research collaborations and sharing of results within and beyond a European Network of Excellence. The methodology is general and can be applied to model any web community - starting from the documents shared and exchanged among the community members - and to use this model for improving accessibility of data and knowledge repositories.
Using abstraction for efficient formal verification of pipelined processors with value prediction

Presented are abstraction techniques that accelerate the formal verification of pipelined processors with value prediction. The formal verification is done by modeling based on the logic of equality with uninterpreted functions and memories (EUFM), and using an automatic tool flow. Applying special abstractions in previous work had resulted in EUFM correctness formulas where most of the terms (abstract word-level values) appear in only positive equations (equality comparisons) or as arguments of uninterpreted functions and uninterpreted predicates, allowing such terms to be treated as distinct constants - a property called positive equality. That property produced orders of magnitude speedup. However, in processors with value prediction, the mechanism for correcting value mispredictions introduces both positive and negated equations between the actual and predicted values, thus reducing significantly the potential for exploiting positive equality. The contributions of this paper are: 1) modeling and formal verification of pipelined processors with load-value prediction and fully implemented mechanism for correcting load-value mispredictions; 2) an approach to abstract the mechanism for detecting load-value mispredictions, thus allowing the use of positive equality, at the cost of enriching the specification processor with the abstracted mechanism for detecting load-value mispredictions; and 3) the observation that this abstraction technique is general and applicable to the formal verification of pipelined processors with other forms of value prediction, e.g., branch prediction, as illustrated with experimental results. The presented abstraction technique produced an order of magnitude speedup when formally verifying a 5-stage pipelined processor with load-value prediction. It can be expected that the speedup would be significantly greater for more complex processors with value prediction
ZETA: A Zero-Trust Security based Forensic-Ready Solution for Perimeter-less Enterprise Networks

Enterprises are adopting software-defined perimeter (also known as perimeter-less network) due to factors such as cloud computing, remote work, and mobile devices. Software-defined perimeter creates a large attack surface that is spread across the Internet via Virtual Private Network (VPN). Continuous monitoring and logging of security events using the Zero-Trust (ZT) cyber security framework enables real-time detection and response to potential cyber security threats.However, Network Address Translation (NAT) routers at the network perimeter provide incomplete flow logs, and hardly any Virtual Private Network (VPN) router provides any flow logs. Moreover, each of the flow logs should be traceable to the identity of users to respond to incidents from Law Enforcement Authorities (LEAs). Insufficient traceability of flow logs back to users might lead to penalties enforced by LEAs. Often such traceability is not quantifiable. This paper provides a measurable zero-trust forensic-ready solution for perimeter-less enterprise networks.
A new short form individual quality of life measure (SEIQoL-DW): application in a cohort of individuals with HIV/AIDS

Quality of life is an increasingly important outcome measure in medicine and health care. Many measures of quality of life present patients with predetermined lists of questions that may or may not be relevant to the individual patient. This paper describes a brief measure, the SEIQoL-DW, which is derived from the schedule for evaluation of individual quality of life (SEIQoL). The measure allows respondents to nominate the areas of life which are most important, rate their level of functioning or satisfaction with each, and indicate the relative importance of each to their overall quality of life. Given its practicality and brevity, the measure should prove particularly useful in clinical situations where patient generated data on quality of life is important. This article describes the first clinical application of the measure, assessing the quality of life of a cohort of patients with HIV/AIDS managed in general practice.
Fish stock losses due to extreme floods – findings from pond‐based aquaculture in the Czech Republic

Global climate changes exhibit increasingly severe weather effects throughout Europe, which can affect agriculture by extreme flood events. Those events are related to pond‐based aquaculture damages, which lead to losses of fish stock. The objective was to examine the loss of fish in pond‐based aquaculture during extreme floods in 2002, 2006, 2009, and 2013 in the Czech Republic. The total assessed loss of fish in pond aquacultures caused by recent extreme flood events was 54.2%. The most numerous losses of fish were not achieved in 2002 of extreme events in contrast to logical assumption. Based on the statistical evaluation, carp is significantly (P < 0.001) less vulnerable (or susceptible to losses) than tench, grass carp, pike, and perch. The study revealed differences between the reactions of diverse fish species to flooding and different susceptibilities to flooding within groups of juvenile and adult fish when considering fish species and their age category. This study summarizes the knowledge about the losses of fish stock in pond‐based aquaculture and exposes pilot findings about the species sensitivity and loss rate during flood events. The outcomes also evaluate flood impacts on different aquaculture companies at the same time, and help them to deal with flood risk management.
Building a Better Metaverse: How Federated Learning is Revolutionizing Virtual Worlds

The rapid development of artificial intelligence (AI) has been made possible by the swift advancement of technology. Recently, there has been a significant increase in interest in the concept of the Metaverse, which is viewed as the ultimate manifestation of the internet. However, building a vast Metaverse requires a substantial allocation of resources to maintain it. Such circumstances pose several challenges, including the disclosure of sensitive data and the problem of non-independent and homogeneous data distribution. In this study, we will focus on explaining federated learning (FL), a methodology that addresses these issues by training localized models and then consolidating them on a global scale to create the ultimate model without sharing the original data. Additionally, we will also discuss the current challenges and potential developments that should be considered in the upcoming years.
Text to fashion image synthesis Via CW-ControlGAN

Text-to-image generation is called the method that translates human textual descriptions in the form of keywords or sentences into images with the same semantic content as text. The generated image in this field should match the meaning and content of the text and also have an acceptable quality. In the last few years, the use of Generative adversarial networks has made significant progress in increasing the visual realism, diversity, and semantic harmony of the generated images with their corresponding texts. Nevertheless, this research field is still facing many challenges. The research done is an attempt to develop a new architecture to compete with the state-of-the-art models. The research about text-to-image generation is done using two approaches: defining a loss function and then changing architecture. In the first approach, to extract more detailed features from the desired text and the generated image by the model, multi-mode transformer models that are trained on both the image and the text are used, and specifically, in this research, the text and image encoders of the CLIP model are used. The second approach deals with adding a wavelet loss function to the objective function to better train and improve the performance of the generator network in generating generated images. The obtained results show that a combination of two approaches produces good-quality images and better-attended text descriptions. The datasets used in this research are FashionGen and CUB Birds.
On the Spatial Degrees of Freedom of Multicell and Multiuser MIMO Channels

We study the converse and achievability for the degrees of freedom of the multicellular multiple-input multiple-output (MIMO) multiple access channel (MAC) with constant channel coefficients. We assume L>1 homogeneous cells with K>0 users per cell where the users have M antennas and the base stations are equipped with N antennas. The degrees of freedom outer bound for this L-cell and K-user MIMO MAC is formulated. The characterized outer bound uses insight from a limit on the total degrees of freedom for the L-cell heterogeneous MIMO network. We also show through an example that a scheme selecting a transmitter and performing partial message sharing outperforms a multiple distributed transmission strategy in terms of the total degrees of freedom. Simple linear schemes attaining the outer bound (i.e., those achieving the optimal degrees of freedom) are explores for a few cases. The conditions for the required spatial dimensions attaining the optimal degrees of freedom are characterized in terms of K, L, and the number of transmit streams. The optimal degrees of freedom for the two-cell MIMO MAC are examined by using transmit zero forcing and null space interference alignment and subsequently, simple receive zero forcing is shown to provide the optimal degrees of freedom for L>1. By the uplink and downlink duality, the degrees of freedom results in this paper are also applicable to the downlink. In the downlink scenario, we study the degrees of freedom of L-cell MIMO interference channel exploring multiuser diversity. Strong convergence modes of the instantaneous degrees of freedom as the number of users increases are characterized.
Analysis and Visualization of Industrial CT Data

Industrial X-Ray 3D computed tomography (3DCT) is on the edge of advancing from a non destructive testing method to a fully standardized means of dimensional measurement for every day industrial use. Currently 3DCT has drawn attention especially in the area of first part inspections of new components, mainly in order to overcome limitations and drawbacks of common methods. Yet an increasing number of companies is benefitting from industrial 3DCT and sporadically the first pioneers start using industrial 3DCT for quality control in the production phase of a component. As 3DCT is still a very young technology of industrial quality control, this method also faces severe problems, which seriously affect measurement results. Some of the major drawbacks for quality control are the following: Artefacts modify the spatial greyvalues, generating artificial structures in the datasets, which do not correspond to reality. Discrete sampling introduces further irregularities due to the NyquistShannon sampling theorem. Uncertainty information is missing when extracting dimensional measurement features. Specifications and limitations of the components and the special setup a 3DCT constrain the best achievable measurement precision. This thesis contributes to the state of the art by algorithmic evaluation of typical industrial tasks in the area of dimensional measurement using 3DCT. The main focus lies in the development and implementation of novel pipelines for everyday industrial use including comparisons to common methods. Convenient and easy to understand means of visualization are
Generating summary keywords for emails using topics

Email summary keywords, used to concisely represent the gist of an email, can help users manage and prioritize large numbers of messages. We develop an unsupervised learning framework for selecting summary keywords from emails using latent representations of the underlying topics in a user's mailbox. This approach selects words that describe each message in the context of existing topics rather than simply selecting keywords based on a single message in isolation. We present and compare four methods for selecting summary keywords based on two well-known models for inferring latent topics: latent semantic analysis and latent Dirichlet allocation. The quality of the summary keywords is assessed by generating summaries for emails from twelve users in the Enron corpus. The summary keywords are then used in place of entire messages in two proxy tasks: automated foldering and recipient prediction. We also evaluate the extent to which summary keywords enhance the information already available in a typical email user interface by repeating the same tasks using email subject lines.