Miracle-dz/UniFAR-FTUs huggingface dataset:
MAPPING THE HETEROGENEOUS COLORECTAL TUMOR MICROENVIRONMENT WITH MULTIPLEXED IMAGING AND MACHINE LEARNING

Background Colorectal carcinoma (CRC) represents a major worldwide health burden and shows an increasing incidence particularly in younger patients. The majority of metastatic CRC (microsatellite stable, MSS) do not respond to current immunotherapies in contrast to the small subset of microsatellite unstable (MSI) patients. However, responses to immuno-therapy were recently observed in subsets of primary MSS tumors indicating their potential vulnerability to such therapeutic approaches. Here, we use multiplex imaging coupled with powerful image analysis tools to highlight important phenotypic differences between MSI and MSS.
SpaceWire Codec VIP: an innovative architecture of UVM-based Verification Environment: SpaceWire Test and Verification, Short Paper

SpaceWire (SpW) devices are widely used in space applications on-board satellites. Their verification is fundamental because it ensures that the Design Under Test (DUT) is bug-free, without the risk of compromising an entire space mission. In this paper an innovative architecture of a Verification Intellectual Property (VIP) based on Universal Verification Methodology (UVM) for the testing of a SpW Codec IP core is presented. Its core is the Twin Model, developed in SystemVerilog and compliant with UVM, which emulates the ideal behavior of the SpW Codec. It communicates directly with the DUT through the Data-Strobe interface with the advantage of automatically generating and sending the data needed to create and maintain the link and leaving only the definition of the payload data to be transmitted to the user. Other two Twin Models are used to create a secondary communication link, named Twin Link, in parallel with the main one. This is the best solution to verify the correct behavior of the DUT in case of errors on the link. In fact, since the Twin Link emulates the ideal communication link behavior, it provides a benchmark for verifying the one including the DUT. A specific functional block is designed to inject on the link all types of errors defined by the standard. The result is a highly reliable and configurable VIP that allows for automatic testing of all the functionalities of any SpW codec. Finally, a full verification campaign was performed with two different DUTs, achieving 100% functional coverage.
All Attention U-NET for Semantic Segmentation of Intracranial Hemorrhages In Head CT Images

Intracranial hemorrhages in head CT scans serve as a first line tool to help specialists diagnose different types. However, their types have diverse shapes in the same type but similar confusing shape, size and location between types. To solve this problem, this paper proposes an all attention U-Net. It uses channel attentions in the U-Net encoder side to enhance class specific feature extraction, and space and channel attentions in the U-Net decoder side for more accurate shape extraction and type classification. The simulation results show up to a 31.8% improvement compared to baseline, ResNet50 + U-Net, and better performance than in cases with limited attention.
Evaluation of physical-layer security schemes for space-time block coding under imperfect channel estimation

With the advent of massive machine type of communications, security protection becomes more important than ever. Efforts have been made to impose security protection capability to physical-layer signal design, so called physical-layer security (PLS). The purpose of this paper is to evaluate the performance of PLS schemes for a multi-input-multi-output (MIMO) systems with space-time block coding (STBC) under imperfect channel estimation. Three PLS schemes for STBC schemes are modeled and their bit error rate (BER) performances are evaluated under various channel estimation error environments, and their performance characteristics are analyzed.
Community Embeddings with Bayesian Gaussian Mixture Model and Variational Inference

Graphs, such as social networks, emerge naturally from various real-world situations. Recently, graph embedding methods have gained traction in data science research. The graph and community embedding algorithm ComE aims to preserve first-, second- and higher-order proximity. ComE requires prior knowledge of the number of communities K. In this paper, ComE is extended to utilize a Bayesian Gaussian mixture model with variational inference for learning community embeddings (ComE BGMM+VI), similar to ComE+. ComE BGMM+VI takes K as the maximum number of communities and drops components through the trade-off hyperparameter weight concentration prior. The advantage of ComE BGMM+VI over the non-Bayesian ComE for an unknown number of communities K is shown for the small Karate club dataset and explored for the larger DBLP dataset.
Poster Paper: Operating System Support for Applications Performance Analysis

In order to classify common performance issues of multi-core applications, used in cloud computing and distributed systems, and offer solutions to them, performance antipatterns have been introduced by researchers. Performance antipatterns help developers refactor inefficient code, and are exceptionally useful for multi-threaded applications, where problems can be difficult to diagnose. However, existing performance antipattern detection methods do not properly examine operating system-wide resources, leading to imprecise metrics and results. In this paper, a novel system-level execution tracing method is presented for detecting the One Lane Bridge performance antipattern. This method is validated through a case study performed on an open-source multi-threaded application, where we diagnosed performance issues.
A Many-to-One Matching based Task Offloading (MATO) Scheme for Fog computing-enabled IoT Systems

Fog computing networks have been widely integrated in IoT-based systems to improve the quality of services (QoS) such as low response service delay through efficient offloading algorithms. However, designing an efficient offloading solution is still facing many challenges including the complicated heterogeneity of fog computing devices and complex computation tasks. In addition, the need for a scalable and distributed algorithm with low computational complexity can be unachievable by global optimization approaches with centralized information management in the dense fog networks. In these regards, this paper proposes a distributed computation offloading framework (MATO) for offloading the splittable tasks using matching theory. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems compared to some related works.
An Improvement of the No-Reference Test Scheme Based on False Edge Detection for Image Processing Application

As one of test methodologies for image processing circuits, a no-reference test scheme based on false-edge detection has been proposed. Because this scheme does not require any reference circuit or reference data to detect faults, it can reduce test cost. In this paper, we discuss an improvement of the false-edge detection for no-reference test scheme. We analyze the misjudged cases by the previous scheme and clarify that the block-based image processing, which is a general way to process images, is one of the reasons of the misjudgment. Based on this analysis, we improve the false-edge detection by extending the definition of the false-edge. Experimental results show that this improvement can enhance the ability of the false-edge detection, so that it can achieve high test accuracy without large computational overhead.
Multi-task Deep-Learning Vehicle Detection and Tracking based on Aerial Views from UAV

For vehicle management systems, vehicle detection, tracking, and recognition which provides statistical information on the number of vehicles and their characters are essential task, however, not only the vehicles themselves but also its characteristic such as types, and colors,. is important for management. Besides, not only fixed traffic cameras, autonomous UAVs with cameras also can bring more flexibility and extend the management areas from an aerial view. In this paper, we propose and implement a real-time multi-task deep-learning for four-wheel vehicle detection, classification, and tracking system on UAVs. A dataset for detection and multi-task classification including multiple colors and types is built. To archive real-time the operation, we used the ByteTrack algorithm with YOLOv5, which is more efficient for mobile and embedded vision applications. Experimental results achieved high accuracy, more than 90% in multi-task classifying, and 56.21 HOTA tracking evaluation metrics on our created test set. The processing time is fast enough for real-time operation, 14 FPS for the detector, and 64 FPS for classification based on an embedded computer, Jetson Nano.
Towards the Advanced Data Processing for Medical Applications Using Task Offloading Strategy

Broad adoption of resource-constrained devices for medical use has additional limitations in terms of execution of delay-sensitive medical applications. As one of the solutions, new ways of computational offloading could be developed and integrated. The recently emerged Mobile Edge Computing (MEC) and Mobile Cloud Computing (MCC) paradigms attempt to address this problem by offloading tasks to a the resource-rich server. In the context of the availability of eHealth services for all patients, independently of the location, the implementation of MEC and MCC could help ensure a high availability of medical services. Remote medical examination, robotic surgery, and cardiac telemetry require efficient computing solutions. This work discusses three alternative computing models: local computing, MEC, and MCC. We have designed a Matlab-based tool to calculate and compare the response time and energy efficiency. We show that local computing demands 48 times more power than MEC/MCC with increasing packet workload. On the other hand, the throughput of MEC/MCC highly depends on the parameters of the communication channel. Finding an optimal trade-off between the response time and energy consumption is an important research question that could not be solved without investigating the system's bottlenecks.
On the Performance Improvements of Data-aided Joint Sensing and Communication

Existing joint sensing and communication technique functionally separates sensing from communication, making it difficult to improve the performance of dual functions and extend to the network paradigm. To simultaneously improve the performance of target sensing and data detection, we propose a data-aided joint target sensing and data detection scheme in this paper, which can be divided into three phases. In the first pilot phase, we derive the performance limits of time delay in the multiple reflected path scenario, which are shown to be inversely proportional to the waveform coefficient with separable multipath. In the second data transmission phase, we evaluate the data detection performance using the minimum mean squared error detector via the uncertainty channel. In the third data-aided phase, we derive the sensing performance when reusing the detected data symbols with the decoding error via error analysis. Finally, simulation results are provided to show the performance improvements for both sensing and communication.
Biomimetic Knock-on of Ion Transport through Crown Ether-embedded Filter Membranes

Designing a filter membrane that can separate Na+ and K+ ions is of great significance for the study of biomimetic ion channels. Using nonequilibrium molecular dynamics simulations, we investigated monolayer and quadrilayer graphene-embedded 18-crown-6 ether nanopores under different external voltages in solution. We showed that the crown ether nanopores exhibit Na+ ion selectivity at low electric field but turn to K+ ion selectivity at high electric field. Ion translocation time of K+ ions is longer than that of Na+ ions in the monolayer crown ether nanopore, while in the quadrilayer crown ether nanopore, ion translocation time of K+ ions is shorter than that of Na+ ions. We demonstrated a synergetic mechanism for ion transport through the filter membrane from two perspectives: free energy through the nanopore and peeling of ion hydration layer. Partially dehydrated ions pass through the nanopore in a ‘knock-on’ manner due to electrostatic repulsion. Our work highlights the application of crown ether nanopores in the field of artificial biomimetic ion channels and ion filtration.
ML Processors Are Going Multi-Core: A performance dream or a scheduling nightmare?

Applications of machine learning (ML) increasingly penetrate into our daily routines, our work, and our living environments. In this way, more complex machine intelligence algorithms fundamentally change and enhance the way we live, work, and interact. However, the real-time deployment of these algorithms toward the dreams of smart spaces, digital twins, the metaverse, or personalized health care requires a powerful compute continuum from cloud to (extreme) edge, capable of efficiently executing the compute-hungry ML workloads, such as deep neural networks (NNs). To enable the required real-time responsiveness at affordable energy or power budgets, many ML-optimized custom processors (also called accelerators) have been presented over the past decade, as depicted in Figure 1.
Testing Cyber-Physical Production System: Test Methods Categorization and dataset

Developing High-confidence Cyber-Physical Production System (CPPS) is essential to ensure system efficacy and safety. Engineering requirements for CPPS is quite tricky because of system heterogeneity. Moreover, system bugs and malfunctions may occur, requiring a test to ensure the robustness of CPPSs. There are several kinds of CPPS test Methods in research. This work aims to categorize test methods of cyber-physical production systems in research. We follow a Systematic Lit-erature Review (SLR) method to review existing research. We synthesize a dataset of 220 papers published from 2006 until 2021 seeking to survey and give structured research in the area of the CPPS test. We categorized CPPS test methods by presenting traditional methods of CPPS tests, such as formal techniques and simulation, in addition to the most used methods as testbed and containerization. We also presented used tools for testing CPPSs, and the objectives of testing CPPS.
Research on Feature Selection Method Based on Bayesian Network and Importance Measures

With the wide application of machine learning algorithms in various fields, feature selection becomes more and more important as a data preprocessing method which can not only solve the problem of dimension disaster, but also improve the generalization ability of algorithms. Based on this, the main work of this paper is as follows. Firstly, the importance measures and Bayesian network were combined to solve the problem that Bayesian network could not rank the importance of features. At the same time, a recursive feature elimination algorithm based on importance degree theory is proposed with importance degree as the screening index. Finally, the prognostic model of gallbladder cancer was established, which shows that the proposed algorithm has good performance.
A Revision on the Effectiveness of Omega-3 Polyunsaturated Fatty Acids as Monotherapy in the Treatment of Major Depressive Disorder

Background Insufficient effectiveness and a difficult tolerability profile of antidepressant drugs for the treatment of major depressive disorder (MDD) have been reported, and polyunsaturated fatty acids (PUFAs) have been posited as reliable therapeutic alternatives. The present study investigated the efficacy of omega-3 PUFAs as monotherapy for MDD. Methods Two well-trained reviewers independently looked at the most significant randomized clinical trials (RCTs) from the PubMed database regarding PUFAs' employment in MDD compared to placebo; “major depressive disorder” and “omega-3 fatty acids,” or “omega-6 fatty acids,” or “polyunsaturated fatty acids (PUFA),” or “n − 3 polyunsaturated fatty acids,” or “eicosapentaenoic acid (EPA),” or “docosahexaenoic acid (DHA)” were used as the medical subject keywords. Results Of the initial 96 potential RCTs based on titles and abstracts, 82 studies did not meet the inclusion criteria and were excluded. Six studies were excluded from the remaining 14 after full text revision. Eight RCTs met all the inclusion/exclusion criteria without reporting clear evidence of PUFAs' effectiveness in the treatment of MDD. Conclusion At present, there is no opportunity to recommend the use of omega-3 PUFAs monotherapy for the treatment of MDD, although their supplementation may be useful in some specific populations.
Flip‐flop phenomenon: observations and theory

In many active stars the spots concentrate on two permanent active longitudes which are 180° apart. In some of these stars the dominant part of the spot activity changes the longitude every few years. This so-called flip-flop phenomenon has up to now been reported in 11 stars, both single and binary alike, and including also the Sun. To explain this phenomenon, a non-axisymmetric dynamo mode, giving rise to two permanent active longitudes at opposite stellar hemispheres, is needed together with an oscillating axisymmetric magnetic field. Here we discuss the observed characteristics of the flip-flop phenomenon and present a dynamo solution to explain them. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Disclosing the properties of low-redshift dual AGN through XMM-Newton and SDSS spectroscopy

The authors, members of the MAGNA project (http://www.issibern.ch/teams/agnactivity/Home.html), gratefully acknowledge support of the International Space Science Institute (ISSI), Bern, Switzerland, and the hospitality of the Lorentz Center for international workshops, where one portion of this work was carried out. The Italian authors acknowledge financial contribution from the agreement ASI-INAF n. 2017-14-H.O. CV acknowledges financial support from the Italian Space Agency under the contract ASI-INAF I/037/12/0. We thank the hospitality of ESA-ESTEC where part of this work has been carried out, BH is grateful for financial support from the ESA-ESTEC visitor program. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science.
DOTS : Pseudo-time-stepping solution of the discrete ordinate equations

The Discrete Ordinates with Time Stepping (DOTS) numerical solution method for the general discrete ordinates approximation of the radiative transport equation is presented. A pseudo-time iteration is employed, with simultaneous updating of all ordinates in each sweep. This may be beneficial for cases with strong scattering and therefore strong coupling between ordinate directions. The iteration is guaranteed to converge, subject to a stability criterion similar to the Courant-Friedrichs-Lewy stability condition. The DOTS algorithm was implemented for general three-dimensional geometry, boundary conditions, and medium properties. Convergence rates were significantly improved by including a multigrid process. We demonstrate the validity of the code on benchmark cases. The methods and implementation are compatible with mainstream computational fluid dynamics practices, and can be easily integrated with existing CFD codes.
Morphology of critically sized crystalline nuclei at shear-induced crystal nucleation in amorphous solid

In this work, we study morphological characteristics of the critically sized crystalline nuclei at initial stage of the shear-induced crystallization of a model single-component amorphous (glassy) system. These characteristics are estimated quantitatively through statistical treatment of the nonequilibrium molecular dynamics simulation results for the system under steady shear at various (fixed) values of the shear rate γ and at different temperatures. It is found that the sheared glassy system is crystallized through nucleation mechanism. From the analysis of time-dependent trajectories of the largest crystalline nuclei, the critical size nc and the nucleation time τc were defined. It is shown that the critically sized nuclei in the system are oriented within the shear-gradient xy-plane at moderate and high shear rates; and a tilt angle of the oriented nuclei depends on the shear rate. At extremely high shear rates and at shear deformation of the system more than 60%, the tilt angle of the nuclei tends.
Tachyon Dynamics in Open String Theory

In this review we describe our current understanding of the properties of open string tachyons on an unstable D-brane or brane–antibrane system in string theory. The various string theoretic methods used for this study include techniques of two-dimensional conformal field theory, open string field theory, boundary string field theory, noncommutative solitons, etc. We also describe various attempts to understand these results using field theoretic methods. These field theory models include toy models like singular potential models and p-adic string theory, as well as more realistic version of the tachyon effective action based on Dirac–Born–Infeld type action. Finally we study closed string background produced by the "decaying" unstable D-branes, both in the critical string theory and in the two-dimensional string theory, and describe the open string completeness conjecture that emerges out of this study. According to this conjecture the quantum dynamics of an unstable D-brane system is described by an int.
NOMA Made Practical: Removing the SIC Through Constructive Interference

In this paper a novel constructive multiple access (CoMA) scheme is proposed. The new CoMA technique aligns the superimposed signals to the users constructively to the signal of interest. Accordingly, there is no need to remove it at the receiver using successive interference cancellation (SIC) technique. In this regard, optimal CoMA precoders are designed for user paring NOMA multiple-input-single-output (MISO) systems. The results in this paper show that CoMA is an attractive solution for NOMA systems with low number of antennas, and transmission power.
Leadership 5.0. a New Approach in Higher Education

The major challenges facing higher education are issues related to training the next generation of engineers who must be recognized as leaders with technical skills but also with competencies to provide smart solutions in the problem-solving processes. Many approaches have been proposed in this context, but most are not formal, do not focus on engineering and neglect to consider trends and challenges as a starting point for defining new, practical proposals. In this paper, a set of challenges are researched to define a new proposal for training the next generation of engineers focused on leadership. The main objective is to provide guidelines to guide the training process in higher education institutions. Subsequently, new research will be carried out to put the proposal defined in this paper into a practical context.