Miracle-dz/UniFAR-FTUs huggingface dataset:
OHMIC CONTACT WITH ENHANCED STABILITY TO POLYCRYSTALLINE SILICON CARBIDE VIA CARBON INTERFACIAL LAYER

The development of electrical contacts to silicon carbide with low specific resistivity and stability is a critical requirement for harsh environment MEMS applications. In this paper, we present a novel method to lower the ohmic contact resistivity and enhance the stability of Pt contacts to polycrystalline 3C-SiC (poly-SiC) operated at elevated temperatures. In particular, nanocrystalline graphite is grown at the interface between poly-SiC and Pt. The contact resistance of Pt/C/SiC is found to be half the value of Pt/SiC at room temperature. In addition, the temperature dependence of the contact resistivity results show that with a carbon interfacial layer, stable ohmic contacts to poly-SiC are achieved at 540°C
When and by how much can helper node selection improve regenerating codes?

Regenerating codes (RCs) can significantly reduce the repair bandwidth of distributed storage networks. Initially, the analysis of RCs was based on the assumption that during the repair process, the newcomer does not distinguish (among all surviving nodes) which nodes to access, i.e., the newcomer is oblivious to the set of helpers being used. Such a scheme is termed the blind repair (BR) scheme. Nonetheless, it is intuitive in practice that the newcomer should access only those “good” helpers. This paper focuses on characterizing the effect of choosing the helper nodes in terms of the storage-bandwidth tradeoff. The results fully answer the following fundamental questions: Under what conditions does proactively choosing the helper nodes improve the storage-bandwidth tradeoff? Can this improvement be analytically quantified?
Low-Density Parity-Check Codes for Two-Way Relay Channels

In this paper, we propose Low-density Parity-check (LDPC) codes for two-way relay channels, where two sources communicate with each other through a relay. At the relay, a simpler interference cancellation joint decoder decodes for the multiple access phase of two-way relay channels. Then, the relay codeword is encoded from two decoded source codewords with LDPC codes. At source, a sub-optimal belief propagation decoder decodes message from codewords received from the relay, the other source and its own source. Simulation results are given to show that our proposed simpler interference cancellation joint decoder at the relay is only 0.2 dB away from the existing sub-optimal belief propagation joint decoder. The required Eb/No at the source is 1.5 dB less than that of the single user channel with the same bit error rate (BER).
Stent-Assisted Endovascular Treatment Placement after Previous Incomplete Internal Carotid Artery Aneurysm for Coil Direct Percutaneous Puncture of a Cervical

Summary: Direct percutaneous puncture for coil placement has been described for visceral aneurysms, but the procedure has not been previously reported for aneurysms of the head and neck. We report a case in which stent-assisted endovascular treatment was successfully combined with direct puncture for additional coil placement to treat a symptomatic giant aneurysm of the cervical internal carotid artery.
Bio-Broker: a biological data and services mediator system

Diversity, dispersion and heterogeneity in data and services strongly constraint the integrated exploitation of biological data. Bio-Broker, an XML-based architectural framework, aims to assist systems developers in the construction of mediator-services. The unique characteristic of the platform is its ability to manage software tools and algorithms to allow dynamic, flexible and intuitive “wiring” of new services expanding the functionality and enabling the easy incorporation of new procedures to customize the system for specific concerns. The mediator offers a view of the system as a single data source where EVASs (Extended Valued Added Services) are readily available for enhancing query processing. A diverse set of applications that combine gene expression and genomic data are used to demonstrate the usefulness of the mediator system.
Epidermal Growth Factor Receptor (EGFR) Signaling Regulates Global Metabolic Pathways in EGFR-mutated Lung Adenocarcinoma*

Background: Genetic mutations in cancer-driver genes induce specific metabolic alterations in cancer cells. Results: EGF receptor signaling has an important role for glycolysis, pentose phosphate pathway, and pyrimidine biosynthesis in EGFR-mutated lung cancer. Conclusion: Our work reveals the relationship between the EGFR signaling axis and key metabolic changes. Significance: These data implicate a possible link between therapeutic response and regulation of metabolism in EGFR-mutated LAD. Genetic mutations in tumor cells cause several unique metabolic phenotypes that are critical for cancer cell proliferation. Mutations in the tyrosine kinase epidermal growth factor receptor (EGFR) induce oncogenic addiction in lung adenocarcinoma (LAD). However, the linkage between oncogenic mutated EGFR and cancer cell metabolism has not yet been clearly elucidated. Here we show that EGFR signaling plays an important role in aerobic glycolysis in EGFR-mutated LAD cells. EGFR-tyrosine kinase inhibitors (TKIs) decreased lactate production, glucose consumption, and the glucose-induced extracellular acidification rate (ECAR), indicating that EGFR signaling maintained aerobic glycolysis in LAD cells. Metabolomic analysis revealed that metabolites in the glycolysis, pentose phosphate pathway (PPP), pyrimidine biosynthesis, and redox metabolism were significantly decreased after treatment of LAD cells with EGFR-TKI. On a molecular basis, the glucose transport carried out by glucose transporter 3 (GLUT3) was downregulated in TKI-sensitive LAD cells. Moreover, EGFR signaling activated carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), which catalyzes the first step in de novo pyrimidine synthesis. We conclude that EGFR signaling regulates the global metabolic pathway in EGFR-mutated LAD cells. Our data provide evidence that may link therapeutic response to the regulation of metabolism, which is an attractive target for the development of more effective targeted therapies to treat patients with EGFR-mutated LAD.
Renal stone disease in Christchurch, New Zealand. Part 2: a community study on the burden of renal stone disease.

AIM To quantify the annual burden of a 12-month cohort of newly diagnosed renal stones in the defined community of Christchurch, New Zealand, and to assess this burden by stone size and position. METHOD In this prospective study of stone burden, patients in the Christchurch region of New Zealand with newly diagnosed renal stones maintained a weekly diary for a 12-month period to record the utilisation of health services and financial and social costs to families and partners. Patient records were matched with diagnostic and clinical information to provide a comprehensive database. The economic costs of the various services were estimated. RESULTS From November 2001 to November 2002, 422 newly diagnosed renal stones were detected--an annual incidence of 105 per 100,000 population. The annual mean cost of these stones was NZ$4274 per person in the first 12 months. The greatest costs were those for emergency visits, hospitalisations and for operative procedures (23.8%, 22.7%, and 21.8% of total financial burden respectively). Patient workdays lost accounted for 10.9% of total costs. Ureteric stones caused greater social burden than kidney stones. Costs were influenced by stone location and size, being significantly higher for ureteric stones and for larger stones. CONCLUSIONS Renal stone disease places a considerable burden on the community. The main burdens were related to health service costs, with personal and pharmaceutical costs representing only a small component. The financial burden to society is estimated at $450,000 per 100,000 population ($NZ in 2001/02).
A flexible library for dependable master-worker parallel programs

We describe a robust library for parallel and dependable master-worker programs (raftnet). From a software client's viewpoint, flexibility hardware independence and ease of use are the main features offered by this library, while its users may take advantage of a framework that optimises the available redundant resources so as to reach at the same time dependability, high performance and load balancing. A wide variety of applications can be targeted without major rewritings of the existing sequential code. This paper describes the implementation of this library and provides evidence to our claims describing two case studies from vision and multimedia applications.
An integration of top-down and bottom-up visual attention for categorization of natural scene images

An integrated technology of top-down and bottom-up visual attention used to the solution of the feature selection and saliency detection problems in object extraction and categorization of natural scene images is proposed. A decision criterion based on the top-down, goal-driven component is introduced to select the features of desired detection object which best distinguish the object from the other parts of scenes in image. The bottom-up, image-driven component of visual attention can be tuned by the learnt knowledge according to the optimal features to optimize the saliency detection. The saliency detection of the interest objects is performed through each of the images by the optimized bottom-up component. The parameter value of category confidence is computed to conduct the categorization of images. The experiment on a set of natural images is carried out to test the adaptability of saliency discrimination and accuracy of image categorization provided in this paper. The experimental evidence shows that the integrated technology introduced in this paper has the capability of capture the intrinsic information with respect to image categorization.
An Extension to the Selective Acknowledgement (SACK) Option for TCP

This note defines an extension of the Selective Acknowledgement (SACK) Option [RFC2018] for TCP. RFC 2018 specified the use of the SACK option for acknowledging out-of-sequence data not covered by TCP's cumulative acknowledgement field. This note extends RFC 2018 by specifying the use of the SACK option for acknowledging duplicate packets. This note suggests that when duplicate packets are received, the first block of the SACK option field can be used to report the sequence numbers of the packet that triggered the acknowledgement. This extension to the SACK option allows the TCP sender to infer the order of packets received at the receiver, allowing the sender to infer when it has unnecessarily retransmitted a packet. A TCP sender could then use this information for more robust operation in an environment of reordered packets [BPS99], ACK loss, packet replication, and/or early retransmit timeouts.
K-means Clustering in the Cloud -- A Mahout Test

The K-Means is a well known clustering algorithm that has been successfully applied to a wide variety of problems. However, its application has usually been restricted to small datasets. Mahout is a cloud computing approach to K-Means that runs on a Hadoop system. Both Mahout and Hadoop are free and open source. Due to their inexpensive and scalable characteristics, these platforms can be a promising technology to solve data intensive problems which were not trivial in the past. In this work we studied the performance of Mahout using a large data set. The tests were running on Amazon EC2 instances and allowed to compare the gain in runtime when running on a multi node cluster. This paper presents some results of ongoing research.
Robust structure simplification for hex re-meshing

We introduce a robust and automatic algorithm to simplify the structure and reduce the singularities of a hexahedral mesh. Our algorithm interleaves simplification operations to collapse sheets and chords of the base complex of the input mesh with a geometric optimization, which improves the elements quality. All our operations are guaranteed not to introduce elements with negative Jacobians, ensuring that our algorithm always produces valid hex-meshes, and not to increase the Hausdorff distance from the original shape more than a user-defined threshold, ensuring a faithful approximation of the input geometry. Our algorithm can improve meshes produced with any existing hexahedral meshing algorithm --- we demonstrate its effectiveness by processing a dataset of 194 hex-meshes created with octree-based, polycube-based, and field-aligned methods.
Detection and Identiﬁcation of UAVs based on Spectrum Monitoring and Deep Learning in Negative SNR Conditions

The democratization of Unmanned Aerial Vehicles (UAVs) is creating an unprecedented need to monitor any misuse of this new technology. Privacy breaching, espionage attacks, drug smuggling, are some notorious examples of illegal use of UAVs that have been reported [1]. Despite the potential threats, UAVs provide practical and cost-effective solutions for several beneﬁcial applications ranging from package deliveries, maintenance, agricultural management, to rescue missions [2]. Consequently, accurate real-time detection, classiﬁcation and identiﬁcation of UAVs are primordial to guarantee the safe use of this technology and to protect critical infrastructures. Multiple techniques have been proposed: Radio Frequency (RF) analysis both in time and in frequency domains, MAC addresses identiﬁcation, acoustic signatures and radar-based techniques, as well as direct visual detection with high-resolution cameras [2, 3]. In practice, the small cross-section of most UAVs and their ability to ﬂy at low heights hinders their detection. Despite these limitations, real-time spectrum monitoring using a combination of passive radio surveillance with Convolutional Neural Networks (CNN), revealed to be a suitable method to classify UAVs with acceptable accuracy even in the presence of moderately strong random noise [4]. For low and negative Signal to Noise Ratio (SNR), preserving the accuracy of CNN ﬁngerprinting in real-time comes with the cost of the non-trivial knowledge of the apriori SNR and also training different networks for each speciﬁc level of noise [4]. The present work tackles the challenge of classiﬁcation
Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.
Particle filter based human motion tracking

This paper proposes a particle filter based marker-less upper body motion capture system, capable of running in realtime. This system is designed for a humanoid robot application, and thus a monocular image sequence is used as input. We first set up a model of the human body, a sub-model which includes 11 Degrees of Freedom is used for the upper body tracking. Considering the realtime processing requirements, two time efficient cues are implemented in the likelihood calculation, namely the edge cue and the distance cue. The system is tested using a publicly available database, which consists of both the videos and the ground truth data, enabling quantitative error analysis. The system successfully tracks the human through arbitrary upper body motion at 20Hz.
Testing independent component patterns by inter-subject or inter-session consistency

Independent component analysis (ICA) is increasingly used to analyze patterns of sponta-neous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically signiﬁcant? Most meth-ods considering the signiﬁcance of components either consider group-differences or use arbitrary thresholds with weak statistical justiﬁcation. In previous work, we proposed a statistically principled method for testing if the coefﬁcients in the mixing matrix are similar in different subjects or sessions. In many applications of ICA, however, we would like to test the reliability of the independent components themselves and not the mixing coefﬁcients. Here, we develop a test for such an inter-subject consistency by extending our previous theory.The test is applicable, for example, to the spatial activity patterns obtained by spatial ICA in resting-state fMRI. We further improve both this and the previously proposed testing method by introducing a new way of correcting for multiple testing, new variants of the clustering method, and a computational approximation which greatly reduces the memory and computation required.
Do-calculus when the True Graph Is Unknown

One of the basic tasks of causal discovery is to estimate the causal effect of some set of variables on another given a statistical data set. In this article we bridge the gap between causal structure discovery and the do-calculus by proposing a method for the identification of causal effects on the basis of arbitrary (equivalence) classes of semi-Markovian causal models. The approach uses a general logical representation of the equivalence class of graphs obtained from a causal structure discovery algorithm, the properties of which can then be queried by procedures implementing the do-calculus inference for causal effects. We show that the method is more efficient than determining causal effects using a naive enumeration of graphs in the equivalence class. Moreover, the method is complete with respect to the identifiability of causal effects for settings, in which extant methods that do not require knowledge of the true graph, offer only incomplete results. The method is entirely modular and easily adapted for different background settings.
Supplementary: A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection

This supplementary material is for the WACV Sub-mission 1501, providing an extended overview of our A Coarse-to-Fine Pseudo-Labeling (C2FPL) framework for unsupervised video anomaly detection. This document provides further qualitative visualizations of our proposed model, as well as relevant discussions. In addition, we discuss the design choice of our self-attention and analyze its effectiveness in comparison with the other state-of-the-art (SOTA) anomaly detection methods using similar/comparable designs. Finally, we present a convergence analysis of our model’s performance
Medical Image Analysis

Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Additionally, the effective utilisation of censored samples − data points where the event time is unknown − is essential for enhancing the model’s predictive accuracy. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach to standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.
Mechatronics-twin Framework based on Stewart Platform for Effective Exploration of Operational Behaviors of Prosthetic Sockets with Amputees

: A Stewart platform is a six-degree-of-freedom parallel manipulator widely used as the motion base for flight simulators, antenna positioning systems, machine tool technology, etc. This work presents a novel mechatronics-twin framework that integrates such a manipulator with advanced biomechanical models and simulations for effective exploration of operational behaviors of prosthetic sockets with amputees. By means of the biomechanical models and simulations, the framework allows the users to first analyze the fundamental operational characteristics of individual amputees according to their specific body geometries, pelvis-femur structures, sizes of transfemoral sockets, etc. Such operational characteristics are then fed to one Stewart platform as the reference control signals for the generation of dynamic loads and behaviors of prosthetic sockets that are otherwise difficult to observe or realize with the real amputees. Experiments in form of integration testing show that the proposed control strategy is capable of generating expected dynamic operational conditions. Currently, the mechatronics-twin framework supports a wide range of biomechanical configurations and the quantification of the respective intra-socket load conditions for socket design optimization and anomaly detection.
Tool support to derive speciﬁcations for conﬂict-free composition

Finding speciﬁcation of pervasive systems is diﬃcult because it requires making certain environmental assumptions explicit at design-time, and describing the software in a way that facilitates runtime composition. This paper describes how a systematic reﬁnement of speciﬁcations from descriptions of the system’s environment and requirements can be automated. Our notion of requirements allows individual features in the system to be inconsistent with each other. Resolution of conﬂicts at design-time is often over-restrictive because it uses the strongest possible conditions for conjunctions and rules out many possible interactions between features. In order to support runtime resolution, our tool examines speciﬁcations for potential conﬂicts and augments them with information to enable detection at runtime. We use a form of temporal logic, the Event Calculus, as our formalism, and characterize the reﬁnement of requirements as a kind of abductive planning. This allows us to use an existing Event Calculus planning tool, implemented in Prolog, as a basis to develop a reasoning tool for obtaining speciﬁcations from potentially inconsistent requirements. We validate our tool by applying it to ﬁnd speciﬁcations of smart home software.