Miracle-dz/UniFAR-FTUs huggingface dataset:
Further Towards Unambiguous Edge Bundling: Investigating Power-Confluent Drawings for Network Visualization

Bach et al. [1] recently presented an algorithm for constructing confluent drawings, by leveraging power graph decomposition to generate an auxiliary routing graph. We identify two issues with their method which we call the node split and short-circuit problems, and solve both by modifying the routing graph to retain the hierarchical structure of power groups. We also classify the exact type of confluent drawings that the algorithm can produce as ‘power-confluent’, and prove that it is a subclass of the previously studied ‘strict confluent’ drawing. A description and source code of our implementation is also provided, which additionally includes an improved method for power graph construction.
Comparison of Turkish Problematic and Non-Problematic Internet Users

The current study aims to determine the difference between university students who are identified as problematic and non-problematic internet users in terms of gender, grades, program type attended, academic achievement level and internet activities such as personal e-mail, searching general information, file transfer, news, shopping, online gaming and chat room. Data was collected from 1058 university students by utilizing the Problematic Internet Usage Scale and a questionnaire. The results of the analyses indicated that problematic internet users experienced more negative consequences of internet, used internet to get more social benefit/comfort and used internet more excessively than non-problematic internet users. In addition, the results revealed that more male students than females were determined as problematic internet users, more students who attended the programs in the field of science than the ones who attended the programs in the field of social sciences were found as problematic internet users. There were no significant differences according to grades and academic achievement level. Based on the internet activities, the results pointed out that the problematic internet users preferred the internet activities such as e-mail, file transfer, news, shopping, online gaming and chat rooms more than non-problematic internet users did, and also the problematic internet users spent more time for these activities, except for searching general information activities.
Multi-Modal Measurements of Mental Load

This position paper describes an experiment conducted to understand the relationships between different physiological measures including pupil Diameter, Blinking Rate, Heart Rate, and Heart Rate Variability in order to develop an estimation of users' mental load in real-time (see Sidebar 1). Our experiment involved performing a task to spot a correct or an incorrect word or sentence with different difficulties in order to induce mental load. We briefly present the analysis of task performance and response time for the items of the experiment task.
Deep Neural Network Based Resource Allocation for V2X Communications

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for solving the power allocation problem namely the weighted minimum mean square error (WMMSE) algorithm and the deep learning-based method. In the WMMSE algorithm, we solve the problem using block coordinate descent (BCD) method. Then we adopt supervised learning technique for the deep neural network (DNN) based approach considering the power allocation from the WMMSE algorithm as the target output. We exploit an efficient implementation of the mini-batch gradient descent algorithm for training the DNN. Extensive simulation results demonstrate that the DNN algorithm can provide very good approximation of the iterative WMMSE algorithm yet reducing the computational overhead significantly.
On The Dimension of The Subfield Subcodes of 1-Point Hermitian Codes

Subfield subcodes of algebraic-geometric codes are good candidates for the use in post-quantum cryptosystems, provided their true parameters such as dimension and minimum distance can be determined. In this paper we present new values of the true dimension of subfield subcodes of $1$--point Hermitian codes, including the case when the subfield is not binary.
Effects of Filler Configuration and Moisture on Dissipation Factor and Critical Electric Field of Epoxy Composites for HV-ICs Encapsulation

Molding compounds (MCs) are widely used as an encapsulation material for integrated circuits; however, traditional MCs are susceptible to moisture and charge spreading over time. The increase in dissipation factor due to the increase of parasitic electrical conductivity (<inline-formula> <tex-math notation="LaTeX">$\sigma$ </tex-math></inline-formula>) and the decrease in dielectric strength <inline-formula> <tex-math notation="LaTeX">$(E_{\mathrm {MC}}^{\mathrm {Crit}})$ </tex-math></inline-formula> restrict their applications. Thus, a fundamental understanding of moisture transport as a function of MC parameters is essential to suppress moisture diffusion and broaden their applications. In this article, we: 1) propose a generalized effective medium and solubility (GEMS) Langmuir model by identifying a set of parameters that control water uptake as a function of filler configuration and relative humidity; 2) investigate the dominant impact of reacted-water on <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> through numerical simulations, mass-uptake, and dc conductivity measurements; 3) investigate electric field distribution to explain how moisture ingress reduces <inline-formula> <tex-math notation="LaTeX">$E_{\mathrm {MC}}^{\mathrm {Crit}}$ </tex-math></inline-formula>; and finally 4) optimize the filler configuration to lower the dissipation factor, and enhance <inline-formula> <tex-math notation="LaTeX">$E_{\mathrm {MC}}^{\mathrm {Crit}}$ </tex-math></inline-formula>. The GEMS-Langmuir model can be used for any application (e.g., photovoltaics, biosensors) where moisture ingress leads to reliability challenges.
Learning Features with Differentiable Closed-Form Solver for Tracking

We present a novel and easy-to-implement training framework for visual tracking. Our approach mainly focuses on learning feature embeddings in an end-to-end way, which can generalize well to the trackers based on online discriminatively trained ridge regression model. This goal is efficiently achieved by taking advantage of the following two important theories. 1) Ridge regression problem has closed-form solution and is implicit differentiation under the optimality condition. Therefore, its solver can be embedded as a layer with efficient forward and backward processes in training deep convolutional neural networks. 2) Woodbury identity can be utilized to ensure efficient solution of ridge regression problem when the high-dimensional feature embeddings are employed. Moreover, in order to address the extreme foreground-background class imbalance during training, we modify the origin shrinkage loss and then employ it as the loss function for efficient and effective training. It is worth mentioning that the above core parts of our proposed training framework are easy to be implemented with several lines of code under the current popular deep learning frameworks, thus our approach is easy to be followed. Extensive experiments on six public benchmarks, OTB2015, NFS, TrackingNet, GOT10k, VOT2018, and VOT2019, show that the proposed tracker achieves state-of-the-art performance, while running at over 30 FPS. Code will be made available.
A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously unobserved behaviors, (ii) provide effective forecasting predictions (extrapolation), and (iii) be certifiable. Autonomous systems will necessarily interact with changing and uncertain environments, motivating the need for models that can accurately extrapolate based on physical principles (e.g. Newton’s universal second law for classical mechanics, $F=ma$ ). Standard ML approaches have shown impressive performance for predicting dynamics in an interpolatory regime, but the resulting models often lack interpretability and fail to generalize. We build on a sparse regression framework that discovers governing dynamical systems models from data, selecting relevant terms in the dynamics from a library of possible functions. Our critically enabling innovation introduces a relaxed version of a sparse optimization framework that allows the use of non-convex sparsity promoting regularization functions and addresses three open challenges in scientific problems and data sets: (i) robust handling of outliers and corrupt data within noisy sensor measurements, (ii) parametric dependencies in candidate library functions, and (iii) the imposition of physical constraints. By explicitly addressing these open challenges, the integrated and unified algorithm developed provides a significant advancement over current state-of-the-art sparse model discovery methods. We show that the approach discovers parsimonious dynamical models on several example systems. This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.
The Coming Age of Pervasive Data Processing

Emerging Big Data analytics and machine learning applications require a significant amount of computational power. While there exists a plethora of large-scale data processing frameworks which thrive in handling the various complexities of data-intensive workloads, the ever-increasing demand of applications have made us reconsider the traditional ways of scaling (e.g., scale-out) and seek new opportunities for improving the performance. In order to prepare for an era where data collection and processing occur on a wide range of devices, from powerful HPC machines to small embedded devices, it is crucial to investigate and eliminate the potential sources of inefficiency in the current state of the art platforms. In this paper, we address the current and upcoming challenges of pervasive data processing and present directions for designing the next generation of large-scale data processing systems.
From IP ID to Device ID and KASLR Bypass (Extended Version)

IP headers include a 16-bit ID field. Our work examines the generation of this field in Windows (versions 8 and higher), Linux and Android, and shows that the IP ID field enables remote servers to assign a unique ID to each device and thus be able to identify subsequent transmissions sent from that device. This identification works across all browsers and over network changes. In modern Linux and Android versions, this field leaks a kernel address, thus we also break KASLR. Our work includes reverse-engineering of the Windows IP ID generation code, and a cryptanalysis of this code and of the Linux kernel IP ID generation code. It provides practical techniques to partially extract the key used by each of these algorithms, overcoming different implementation issues, and observing that this key can identify individual devices. We deployed a demo (for Windows) showing that key extraction and machine fingerprinting works in the wild, and tested it from networks around the world.
Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.
Energy Efficient WSN: a Cross-layer Graph Signal Processing Solution to Information Redundancy

In this work an iterative solution to build a network lifetime-preserving sampling strategy for WSNs is presented. The paper describes the necessary steps to reconstruct a graph from application data. Once the graph structure is obtained, a sampling strategy aimed at finding the smallest number of concurrent sensors needed to reconstruct the data in the unsampled nodes within a specific error bound, is presented. An iterative method then divides the sensor nodes into sets to be sampled sequentially to increase lifetime. Results on a real-life dataset show that the reconstruction RMSE can be easily traded off for a larger number of disjoint sampling sets which improve the network lifetime linearly.
Coherent Conversion Between Microwave and Optical Photons—An Overview of Physical Implementations

Quantum information technology based on solid state qubits has created much interest in converting quantum states from the microwave to the optical domain. Optical photons, unlike microwave photons, can be transmitted by fiber, making them suitable for long distance quantum communication. Moreover, the optical domain offers access to a large set of very well‐developed quantum optical tools, such as highly efficient single‐photon detectors and long‐lived quantum memories. For a high fidelity microwave to optical transducer, efficient conversion at single photon level and low added noise is needed. Currently, the most promising approaches to build such systems are based on second‐order nonlinear phenomena such as optomechanical and electro‐optic interactions. Alternative approaches, although not yet as efficient, include magneto‐optical coupling and schemes based on isolated quantum systems like atoms, ions, or quantum dots. Herein, the necessary theoretical foundations for the most important microwave‐to‐optical conversion experiments are provided, their implementations are described, and the current limitations and future prospects are discussed.
All-digital carrier phase and clock timing recovery for 8PSK

An all-digital 8PSK (phase shift keying) demodulator including decision-directed carrier phase and clock timing recovery is introduced and analyzed. The proposed synchronization algorithms require two samples per symbol. The phase and timing discriminator characteristics are analytically derived and checked by means of computer simulations. Simulated steady-state carrier phase and clock timing mean square error results are compared with analytical findings and the Cramer-Rao bound. Mean acquisition time for joint phase and timing error has also been computed by the Monte Carlo simulation technique. Finally, digital demodulator performance in terms of bit error rate for uncoded and trellis coded 8PSK, including interpolators and synchronization loops, is presented. <<ETX>>
3DBGrowth: Volumetric Vertebrae Segmentation and Reconstruction in Magnetic Resonance Imaging

Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist.
Compositional and Functional Analysis of the Microbiome in Tissue and Saliva of Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is affected by the interaction between oral pathogen and holobionts, or the combination of the host and its microbial communities. Studies have indicated the structure and feature of the microbiome in OSCC tissue and saliva, the relationships between microbiota and OSCC sites, stages remain unclear. In the present study, OSCC tissue (T), saliva (S) and mouthwash (W) samples were collected from the same subjects and carried out the microbiome study by 16S sequencing. The results showed the T group was significantly different from the S and W groups with the character of lower richness and diversity. Proteobacteria were most enriched in the T group at the phylum level, while Firmicutes were predominant in groups S and W. At the genus level, the predominant taxa of group T were Acinetobacter and Fusobacterium, and for group S and W, the predominant taxa were Streptococcus and Prevotella. The genera related to late stage tumors were Acinetobacter and Fusobacterium, suggesting microbiota may be implicated in OSCC developing. Both compositional and functional analyses indicated that microbes in tumor tissue were potential indicator for the initiation and development of OSCC.
A distributed architecture for multiplayer interactive applications on the Internet

This article describes the design, implementation, and evaluation of MiMaze, a distributed multiplayer game on the Internet, and, more precisely, it describes the design of dedicated transmission control mechanisms. MiMaze is implemented on a completely distributed communication architecture based on the IP multicast protocol suite (RTP/UDP/IP). This is the first work to analyze a distributed interactive game on the multicast Internet. The major element of the MiMaze architecture is a distributed synchronization mechanism that guarantees the consistency of the game regardless of network delay. This article provides on evaluation of the MiMaze game on the MBone, and discusses approaches to monitor and evaluate this new type of application. The main contribution of this work is to show, based on on example, the feasibility of this new family of applications on a best-effort network. It is shown that real-time interactivity can be maintained, provided that some level of inconsistency can be tolerated by the application. This work also highlights the role of multicast as an enabling technology for a real-time Internet.
Omp19 Enables Brucella abortus to Evade the Antimicrobial Activity From Host's Proteolytic Defense System

Pathogenic microorganisms confront several proteolytic events in the molecular interplay with their host, highlighting that proteolysis and its regulation play an important role during infection. Microbial inhibitors, along with their target endogenous/exogenous enzymes, may directly affect the host's defense mechanisms and promote infection. Omp19 is a Brucella spp. conserved lipoprotein anchored by the lipid portion in the Brucella outer membrane. Previous work demonstrated that purified unlipidated Omp19 (U-Omp19) has protease inhibitor activity against gastrointestinal and lysosomal proteases. In this work, we found that a Brucella omp19 deletion mutant is highly attenuated in mice when infecting by the oral route. This attenuation can be explained by bacterial increased susceptibility to host proteases met by the bacteria during establishment of infection. Omp19 deletion mutant has a cell division defect when exposed to pancreatic proteases that is linked to cell-cycle arrest in G1-phase, Omp25 degradation on the cell envelope and CtrA accumulation. Moreover, Omp19 deletion mutant is more susceptible to killing by macrophage derived microsomes than wt strain. Preincubation with gastrointestinal proteases led to an increased susceptibility of Omp19 deletion mutant to macrophage intracellular killing. Thus, in this work, we describe for the first time a physiological function of B. abortus Omp19. This activity enables Brucella to better thrive in the harsh gastrointestinal tract, where protection from proteolytic degradation can be a matter of life or death, and afterwards invade the host and bypass intracellular proteases to establish the chronic infection.
Joint power control, base station assignment and sectorization for CDMA cellular systems

A CDMA cellular scheme that dynamically adapts the cells to the spatial load distribution is proposed. The scheme maximizes the network capacity for a given number of base stations, for any distribution of the mobile users. It combines power control, base station assignment and adaptive sectorization efficiently, and can be easily integrated into the already deployed CDMA systems. Simulation results show that our relatively low complexity approach, achieves a performance quite close to that of an optimal beamforming scheme under uniform and non-uniform load scenarios.
Virtual Pilot: agent-based simulation for effective training

Introduction The military increasingly use simulators and games to train personnel for performing specialized tasks under complex conditions. In many of these training programs, Subject Matter Experts (SMEs) of the training staff play various roles such as adversaries, team mates or other own forces. The advantage of SMEs is that they have the expertise to take the context into account when responding to trainee actions, thus keeping the scenario in focus of the learning objectives. However, the downside of dependency on SMEs to deliver training is that it elevates costs of training and demands organizational and logistic efforts (van den Bosch & van Doesburg, 2007). New technology may help to solve this problem by developing virtual humans (agents) to play the supporting roles autonomously (van den Bosch, Harbers, Heuvelink, & Doesburg, 2009). Agents that show realistic and intelligent behavior can make training more traceable, more systematic, and more cost-efficient. In perspective of continuing budget reductions, the military aims to develop and employ the technology of intelligent agents for their training programs.
VRMLによる景観可視化(平成11年度 日本造園学会研究発表論文集(17))

VRML (Virtual Reality Modeling Language) は, インタ-ネット上で三次元情報を可視化するための言語であり, 動的な三次元情報を, WWWを介して容易に伝達することができる。 本研究では造園分野でのVRMLの応用例として, 景観可視化を行うことのできる簡易設計システムを開発しその可能性を示した。 VRMLは, 高度な機能を持つため, 非常に低コストに, 設計した公園や森林の中を歩き回るような疑似体験のできる仮想現実感システムの構築が可能であった。 また, このシステムで, インターネット上で三次元情報の伝達, 共有が可能であり, 将来的に大きな可能性があることを示した。