Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of Combined Oculoplethysmography, Carotid Phonoangiography and Doppler in the Non‐Invasive Diagnosis of Extracranial Carotid Occlusive Disease

One hundred and eight patients were studied with fluid-Ailed oculoplethysmography and carotid phonoangiography (OPG-CPA) and by arteriography. Thirty-two patients also had “Doppler evaluation” of supraorbital arterial flow. The OPG-CPA correctly predicted the degree of occlusion in 76% of the involved vessels, including the degree of ocdusion of each carotid for each patient (63%). The OPG-CPA identified at least one obstructing carotid lesion in 51 of the 56 (91%) patients with obstructing lesions demonstrated by arteriography. On a per patient basis, which requires that both carotids be correctly assessed, the OPG-CPA had a false negative rate of 9.6% and false positive rate of 50%. The supraorbital artery “Doppler evaluation” had an accuracy rate of 66%, a per patient false negative rate of 50%, and a per patient false positive rate of 12%. The OPG-CPA and supraorbital artery “Doppler evaluation" are adjunctive tests for evaluating patients with cerebral vascular insufficiency and should not, at present, replace arteriography in symptomatic patients or in certain asymptomatic patients.
Localizing Unordered Panoramic Images Using the Levenshtein Distance

This paper proposes a feature-based method for recovering the relative positions of the viewpoints of a set of panoramic images for which no a priori order information is available, along with certain structure information regarding the imaged environment. The proposed approach operates incrementally, employing the Levenshtein distance to deduce the spatial proximity of image viewpoints and thus determine the order in which images should be processed. The Levenshtein distance also provides matches between images, from which their underlying environment points can be recovered. Recovered points that are visible in multiple views permit the localization of more views which in turn allow the recovery of more points. The process repeats until all views have been localized. Periodic refinement of the reconstruction with the aid of bundle adjustment, distributes the reconstruction errors among images. The method is demonstrated on several unordered sets of panoramic images obtained in an indoor environment.
Testing +/- 1-Weight Halfspaces

We consider the problem of testing whether a Boolean function f : {−1, 1}n → {−1, 1} is a ±1-weight halfspace, i.e. a function of the form f(x) = sgn(w1x1 + w2x2 + · · · + wnxn) where the weights wi take values in {−1, 1}. We show that the complexity of this problem is markedly different from the problem of testing whether f is a general halfspace with arbitrary weights. While the latter can be done with a number of queries that is independent of n [7], to distinguish whether f is a ±1-weight halfspace versus -far from all such halfspaces we prove that nonadaptive algorithms must make Ω(log n) queries. We complement this lower bound with a sublinear upper bound showing that O( √ n·poly( 1 )) queries suffice.
1 Failure Analysis of Internet Services

Un-availability can be quantitatively defined as the ratio of MTTR (mean time to repair) to the sum of MTTR (mean time to repair) and MTTF (mean time to failure). Decreasing MTTR is as valuable as increasing MTTF. By learning why systems fail, failure distribution across parts of an IS, we attempt to envision a plausible model for future Information Technology (IT). In this endeavor, we focus on discovering and characterizing failure causes, and suggesting methods to improve recovery from failures, consequently increasing availability. As an example, pervasive use of redundancy improves disaster tolerance. Improving recovery from failures automatically improves availability.
Hole Transport Layer Free Photovoltaic Cell Based on Inorganic Perovskite for Indoor Energy Harvesting for IoT Applications

The present work focuses on optimizing hole transport layer (HTL)-free inorganic perovskite photovoltaic (PV) device, specifically designed for efficient energy harvesting in indoor light environments. The optical optimization and numerical study aims to enhance the performance of these devices by utilizing inorganic perovskite, Cesium Lead Iodide, as the active material and exploring novel device configurations. Techniques such as anti-reflection coatings and pyramidal nanostructures are investigated to improve light absorption and minimize reflection losses. Additionally, the integration of a back surface field (BSF) layer is explored to reduce carrier recombination and enhance overall device efficiency. The results have shown a considerable increase in the performance of the device with nanostructures and seems to be a good candidate for low cost indoor energy harvesting for IoT applications. Advanced software tools, including rigorous coupled wave analysis are employed for simulating the optical properties and optimizing the device structures.
On Recognizable Languages Of Infinite Pictures

In a recent paper, Altenbernd, Thomas and W\"ohrle have considered acceptance of languages of infinite two-dimensional words (infinite pictures) by finite tiling systems, with the usual acceptance conditions, such as the B\"uchi and Muller ones, firstly used for infinite words. The authors asked for comparing the tiling system acceptance with an acceptance of pictures row by row using an automaton model over ordinal words of length $\omega^2$. We give in this paper a solution to this problem, showing that all languages of infinite pictures which are accepted row by row by B\"uchi or Choueka automata reading words of length $\omega^2$ are B\"uchi recognized by a finite tiling system, but the converse is not true. We give also the answer to two other questions which were raised by Altenbernd, Thomas and W\"ohrle, showing that it is undecidable whether a B\"uchi recognizable language of infinite pictures is E-recognizable (respectively, A-recognizable).
The Role of Stochastic Forcing in Generating ENSO Diversity

Numerous oceanic and atmospheric phenomena influence El Niño–Southern Oscillation (ENSO) variability, complicating both prediction and analysis of the mechanisms responsible for generating ENSO diversity. Predictability of ENSO events depends on the characteristics of both the forecast initial conditions and the stochastic forcing that occurs subsequent to forecast initialization. Within a linear inverse model framework, stochastic forcing reduces ENSO predictability when it excites unpredictable growth or interference after the forecast is initialized, but also enhances ENSO predictability when it excites optimal initial conditions that maximize deterministic ENSO growth. Linear inverse modeling (LIM) allows for straightforward separation between predictable signal and unpredictable noise and so can diagnose its own skill. While previous LIM studies of ENSO focused on deterministic dynamics, here we explore how noise forcing influences ENSO diversity and predictability. This study identifies stochastic forcing details potentially contributing to the development of central Pacific (CP) or eastern Pacific (EP) ENSO characteristics. The technique is then used to diagnose the relative roles of initial conditions and noise forcing throughout the evolution of several ENSO events. LIM results show varying roles of noise forcing for any given event, highlighting its utility in separating deterministic from noise-forced contributions to the evolution of individual ENSO events. For example, the strong 1982 event was considerably more influenced by noise forcing late in its evolution than the strong 1997 event, which was more predictable with long lead times due to its deterministic growth. Furthermore, the 2014 deterministic trajectory suggests that a strong event in 2014 was unlikely.
Using Pearson Type IV and other cinderella distributions in simulation

Univariate continuous distributions with unbounded range of variation have not been so widely used in simulation as those that are bounded (usually to the left). However situations do occur when they are needed, particularly in operations research and financial applications. Two distributions that have such unbounded range are the Pearson Type IV and Johnson SU distributions. Though both are well known in statistics, there is still a lack of methods in the literature for fitting these distributions to data which are both efficient and comprehensively reliable. Indeed the Pearson Type IV has the reputation of being difficult to fit. In this paper we identify the pitfalls and propose a fitting method that avoids them. We also show how to test the goodness of fit of estimated distributions. All the procedures described are included as VBA code in an accompanying Excel workbook available online. Two numerical examples are described in detail.
Hardware-Software Co-Design of a Collaborative DNN Accelerator for 3D Stacked Memories with Multi-Channel Data

Hardware accelerators are preferred over general-purpose processors for processing Deep Neural Networks (DNN) as the later suffer from power and memory walls. However, hardware accelerators designed as a separate logic chip from the memory still suffer from memory wall. Processing-in-memory accelerators, which try to overcome this memory wall by developing the compute elements as part of the memory structures, are highly constrained due to the memory manufacturing process. Near-data-processing (NDP) based hardware accelerator design is an alternative paradigm that could combine the benefit of high bandwidth, low access energy of processing-in-memory, and design flexibility of separate logic chip. However, NDP has area, data flow and thermal constraints, hindering high throughput designs. In this work, we propose an HBM3-based NDP accelerator that tackles the constraints of NDP with a hardware-software co-design approach. The proposed design takes only 50% area, delivers a speed-up of $3\times$, and is about $6 \times$ more energy efficient than state-of-the-art NDP hardware accelerator for inferencing workloads such as AlexNet, MobileNet, ResNet, and VGG without loss of accuracy.
Wide-Area Route Control for Distributed Services

Many distributed services would benefit from control over the flow of traffic to and from their users, to offer better performance and higher reliability at a reasonable cost. Unfortunately, although today's cloud-computing platforms offer elastic computing and bandwidth resources, they do not give services control over wide-area routing. We propose replacing the data center's border router with a Transit Portal (TP) that gives each service the illusion of direct connectivity to upstream ISPs, without requiring each service to deploy hardware, acquire IP address space, or negotiate contracts with ISPs. Our TP prototype supports many layer-two connectivity mechanisms, amortizes memory and message overhead over multiple services, and protects the rest of the Internet from misconfigured and malicious applications. Our implementation extends and synthesizes open-source software components such as the Linux kernel and the Quagga routing daemon. We also implement a management plane based on the GENI control framework and couple this with our four-site TP deployment and Amazon EC2 facilities. Experiments with an anycast DNS application demonstrate the benefits the TP offers to distributed services.
A chordal decomposition approach to scalable design of structured feedback gains over directed graphs

This paper considers the problem of designing static feedback gains subject to a priori structural constraints, which is in general a non-convex problem. By exploiting the sparsity properties of the problem, and using chordal decomposition, a scalable algorithm is proposed to compute structured stabilizing feedback gains for large-scale systems over directed graphs. Specifically, we first present a chordal decomposition theorem for block-semidefinite matrices. A relaxation is then used to recast the design of structured feedback gains into a convex problem. Combining the decomposition with the relaxation, we propose a sequential design algorithm to obtain structured feedback gains clique-by-clique over a clique tree of the underlying chordal graph. Numerical simulations demonstrate the efficiency of the proposed method.
Champs geothermique d'asal (republique de djibouti)

La these comporte : - une synthese du contexte geodynamique de la region : rift de la mer rouge golfe d'aden rift est africain depression afar et presentation des differents modeles proposes depuis une vingtaine d'annees. - une description de la geologie du rift d'asal, en integrant les analyses et datations les plus recentes. Une presentation des resultats geologiques et thermiques des forages. - une synthese des differentes methodes geophysiques fondamentales et appliquees utilisees pour definir le champs geothermique d'asal. Il est propose des modeles geologiques a partir des resultats obtenus par la gravimetrie et le magnetisme ainsi qu'un modele de croute. - une derniere partie reprend les resultats essentiels de la these en proposant deux modeles de champs, un a deux dimensions et l'autre tridimentionnel.
An application of undergraduate academic growth path on the credit system based on data mining

With the development of teaching reformation in colleges and universities, the application of the credit system is an inevitable trend. On the basis of the credit system, students can select the courses freely and independently according to their academic plan. However, there are some problems about the credit system as following. How do the students select the courses in accordance of specialty knowledge? How do the students cultivate their own professional quality in accordance of social requirements? How do the students plan their academic path in accordance of talents training? Thence, the kernel key of the credit system is to cultivate the applicable and high-quality talents from colleges and universities. At the same time, the data mining is a kind of effective data analysis technologies. Because a group of big data would come exactly from the digital information of administration processing of colleges and universities, the application of data mining on it could solve these problems above and promote the implementation of the credit system in colleges and universities.
Bridging the heterogeneity of orchestrations - A Petri net-based integration of BPEL and Windows Workflow

Service orchestrations are a powerful tool for implementing intra- and interorganizational business processes that base on services. Several heterogeneous orchestration languages can be found in contemporary IT landscapes. While the Web Services Business Process Execution Language (BPEL) is the de facto standard, others gain attention, including Windows Workflow (WF) in the . NET segment. When integrating orchestrations, incompatibilities between them can easily arise. In this paper, we investigate an automated Petri net-based integration between BPEL and WF to solve these issues with behavioral adapter services. We provide a mapping of WF to Open Workflow Nets (oWFNs) and implement it in a compiler. Thereby, we integrate our approach with existing approaches for BPEL and adapter synthesis and validate the integration with a standards-based case study using the two languages.
Reduced-Order Modeling of Large Linear Subcircuits via a Block Lanczos Algorithm

A method for the efficient computation of accurate reduced-order models of large linear circuits is described. The method, called MPVL, employs a novel block Lanczos algorithm to compute matrix Padé approximations of matrix-valued network transfer functions. The reduced-order models, computed to the required level of accuracy, are used to speed up the analysis of circuits containing large linear blocks. The linear blocks are replaced by their reduced-order models, and the resulting smaller circuit can be analyzed with general-purpose simulators, with significant savings in simulation time and, practically, no loss of accuracy.
Review Article Gestational Diabetes Mellitus and Future Cardiovascular Risk: an Update

The prevalence of gestational diabetes mellitus is increasing in parallel with the rising prevalence of type 2 diabetes and obesity around the world. Current evidence strongly suggests that women who have had gestational diabetes mellitus are at greater risk of cardiovascular disease later in life. Given the growing prevalence of gestational diabetes mellitus, it is important to identify appropriate reliable markers of cardiovascular disease and specific treatment strategies capable of containing obesity, diabetes, and metabolic syndrome in order to reduce the burden of cardiovascular disease in the women affected.
GSNNB: A resampling technique of Gaussian Sampling within the Nearest Neighbor Boundary for class imbalance

Class imbalance is a common phenomenon in real world where the distribution of the training data across the classes is biased or skewed. Learning from class-imbalanced data has posed a great challenge in machine learning and thus several approaches were raised to deal with it such as resampling skills, feature selection and ensemble learning. In this study, a novel algorithm called Gaussian Sampling within Nearest Neighbor Boundary (GSNNB) is proposed which is a hybrid sampling technique with two stages. The first stage is to find the nearest neighbor boundary (NNB) between minority class and majority class. In the second stage, to balance the training data, minority samples are generated based on Gaussian sampling within the NNB, while majority samples are under sampled randomly to adaptive number after clustering. To demonstrate the performance of GSNNB, experiments on 40 two-class imbalanced datasets are given and the results show that GSNNB perform better than or comparable with some other existing methods concerning geometric mean (G-mean) and area under the receiver operating curve (ROC).
Tumor Necrosis Factor Receptor 1 and Its Signaling Intermediates Are Recruited to Lipid Rafts in the Traumatized Brain

The tumor necrosis factor (TNF) ligand-receptor system plays an essential role in apoptosis that contributes to secondary damage after traumatic brain injury (TBI). TNF also stimulates inflammation by activation of gene transcription through the IκB kinase (IKK)/NF-κB and JNK (c-Jun N-terminal protein kinase)/AP-1 signaling cascades. The mechanism by which TNF signals between cell death and survival and the role of receptor localization in the activation of downstream signaling events are not fully understood. Here, TNF receptor 1 (TNFR1) signaling complexes in lipid rafts were investigated in the cerebral cortex of adult male Sprague Dawley rats subjected to moderate (1.8-2.2 atmospheres) fluid-percussion TBI and naive controls. In the normal rat cortex, a portion of TNFR1 was present in lipid raft microdomains, where it associated with the adaptor proteins TRADD (TNF receptor-associated death domain), TNF receptor-associated factor-2 (TRAF-2), the Ser/Thr kinase RIP (receptor-interacting protein), TRAF1, and cIAP-1 (cellular inhibitor of apoptosis protein-1), forming a survival signaling complex. Moderate TBI resulted in rapid recruitment of TNFR1, but not TNFR2 or Fas, to lipid rafts and induced alterations in the composition of signaling intermediates. TNFR1 and TRAF1 were polyubiquitinated in lipid rafts after TBI. Subsequently, the signaling complex contained activated caspase-8, thus initiating apoptosis. In addition, TBI caused a transient activation of NF-κB, but receptor signaling interacting proteins IKKα and IKKβ were not detected in raft-containing fractions. Thus, redistribution of TNFR1 in lipid rafts and nonraft regions of the plasma membrane may regulate the diversity of signaling responses initiated by these receptors in the normal brain and after TBI.
Enhanced Algorithm of Automated Ground Truth Generation and Validation for Lane Detection System by $\text{M}^{2}\text{BMT}$

Automotive imaging is a recent trend in research to assist drivers and is finally moving forward to achieve the goal of designing a driverless car. Along with a state-of-the-art algorithm, a state-of-the-art validation framework is also a requirement to ensure the quality of the system. This paper proposes an enhancement of the ground truth determination for automated lane detection system. The approach of time slicing has been built up on the binary framework. However, the classical binarization algorithms are not found to be good enough to address the particular domain of lane detection in an unconstrained environment and varied scenarios of lane structures, including curvy and dashed lane marks. This paper proposes a novel binarization algorithm based on min-between-max thresholding (MBMT). The adaptive binarization addresses the issue of outlier rejection in an efficient way and handles the effect of shadow, illumination variation, and other factors in time-sliced images in an automated manner. Additionally, this paper identifies the limitation of the classical time-slice-based approach even with time MBMT for ground truth determination and addresses the same through the second level of adaptation by spatial MBMT. Finally, a complete mathematical model is presented to validate any arbitrary lane detection algorithm with respect to the ground truth determined through the said method of hybrid or modified MBMT or $\text{M}^{2}\text{BMT}$.
EEG Anomaly Detection using Generative Adversarial Networks (GANs)

Abnormality is the unusual behavior or feature of a sample compared to the whole population. In medical applications, the task of anomaly detection is meritorious in early or in-time diagnosis of diseases by detecting disease markers in for example different neuroimaging data such as Electroencephalogram (EEG) etc. Anomaly detection approaches include supervised, unsupervised, and semi-supervised machine learning algorithms. However unsupervised and semi-supervised approaches are more preferred due to the arduous task of labeling the data and due to the intrinsic unknown definition of anomaly. The development of applications of generative adversarial networks in recent years has made them a competent tool for anomaly detection problems. They learn (normal) data distribution and detect anything different as an anomaly. The task of EEG anomaly detection is one proposed interdisciplinary problem that can have applications in early pathology detection as a starting point for later treatment plans, or in brain-computer interface studies. In this work, we used a generative adversarial model and an image representation of EEG data to detect abnormal EEG samples. The whole pipeline and the results indicate the proper standing of this approach among peers on Temple University Hospital’s (TUH) abnormal EEG corpus data set.
Optimized Valley Skipping Strategy to Improve Efficiency and Reduce THD for DCM Boost PFC Converter

This article proposes an optimized valley skipping strategy to improve efficiency and reduce total harmonic distortion (THD) for boost power factor correction (PFC) converters in discontinuous conduction mode (DCM). In DCM, the valley skipping strategy is used to reduce the switching ON loss of the power switch. However, conventional skipping strategy causes dynamic changing valleys in a half-line cycle, leading to frequency hopping and extra current distortion. To avoid this, a control strategy based on constant skipping valleys (CSV) is proposed. Firstly, the switching frequency variation with CSV is derived based on the analytical expression of parasitic resonance. Secondly, by employing CSV in control, not only is the switching frequency hopping eliminated, but also the switching cycle corresponds more accurately with the desired cycle. Therefore, an accurate average current control is derived to reduce the current distortion. Thirdly, the CSV is tuned according to a loss estimation, which further improves the efficiency under valley skipping operation. Finally, a 250W prototype is built to verify the proposed control strategy. Experimental results demonstrate that the proposed strategy improves the efficiency by an average level of 0.4% and reduces the input current THD to levels below 3.1% at rated power.