Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival of Patients with Oral Cavity Cancer in Germany

The purpose of the present study was to describe the survival of patients diagnosed with oral cavity cancer in Germany. The analyses relied on data from eleven population-based cancer registries in Germany covering a population of 33 million inhabitants. Patients with a diagnosis of oral cavity cancer (ICD-10: C00-06) between 1997 and 2006 are included. Period analysis for 2002–2006 was applied to estimate five-year age-standardized relative survival, taking into account patients' sex as well as grade and tumor stage. Overall five-year relative survival for oral cavity cancer patients was 54.6%. According to tumor localization, five-year survival was 86.5% for lip cancer, 48.1% for tongue cancer and 51.7% for other regions of the oral cavity. Differences in survival were identified with respect to age, sex, tumor grade and stage. The present study is the first to provide a comprehensive overview on survival of oral cavity cancer patients in Germany.
Late-Onset Neutropenia in Very Low Birth Weight Infants

Background. Neutropenia, defined as absolute neutrophil count (ANC) <1500/mm3, affects 6% to 58% of premature infants in the first week of life. This early-onset neutropenia in premature infants has previously been correlated with sepsis, maternal hypertension, severe asphyxia, and periventricular hemorrhage. Late-onset neutropenia, defined as ANC <1500/mm3 at a postnatal age of ≥3 weeks, has not been previously reported. Objectives. The purposes of this study were to determine the prevalence of late-onset neutropenia in very low birth weight (VLBW) infants and to examine the factors that may be associated with this phenomenon. Methods. A weekly complete blood cell count (CBC) was performed routinely in all premature infants with birth weight ≤1500 g (n = 225) admitted to the neonatal intensive care in a 3-year period who survived until discharge. CBC and differentials were recorded at day 1, day 3, and then weekly until discharge. The clinical data of the study infants were collected by reviewing the medical records retrospectively. Results. Late-onset neutropenia was detected in 51 infants (22%). In both neutropenic (n = 51) and nonneutropenic infants (n = 174), ANC increased postnatally, remained above 5000/mm3 for the first 3 weeks of life, and had a marked decrease at ∼4 weeks of age. Thereafter, ANC decreased to a level of ∼1400/mm3 in the neutropenic infants and 4000/mm3 in the nonneutropenic infants. The neutropenic infants had a significantly lower nadir ANC, lower hemoglobin, and higher reticulocyte count than did the nonneutropenic infants with similar platelet counts. None of the study infants received erythropoietin during their hospitalization. This late-onset neutropenia occurred at postnatal age of 6 ± 2 weeks (range: 3–10 weeks). The duration of neutropenia was 1.7 ± . 7 weeks (range: 1–3 weeks). All of the neutropenic infants had anemia of prematurity with high reticulocyte count and normal platelet count. The neutropenic infants were stable, growing on full oral feedings, and had no signs or symptoms of sepsis. No adverse effects of late-onset neutropenia were apparent in these infants. Conclusion. Late-onset neutropenia is a common incidental finding in stable, growing VLBW infants that has not been previously reported. Late-onset neutropenia is a phenomenon that occurs in anemic premature infants who have marked reticulocytosis. Normal regulation of hematopoiesis is accompanied by a balance between colony-stimulating factors, such as erythropoietin and granulocyte colony-stimulating factor, which regulate erythropoiesis and granulopoiesis. We speculate that imbalance of these factors with increased reticulocytopoiesis in response to anemia of prematurity may explain this phenomenon. We recommend avoiding institution of aggressive, potentially harmful therapy for this phenomenon in healthy, growing VLBW infants.
Decentralized dynamic surface control for a class of interconnected nonlinear systems

Decentralized dynamic surface control (DDSC) for a class of interconnected nonlinear systems is presented in this paper. While a centralized design approach of DSC was developed, the decentralized approach to deal with large-scale interconnected systems is proposed under the assumption that interconnected functions among subsystems are unknown but bounded. As done in the centralized approach, augmented error dynamics for N nonlinear subsystems with DDSC are derived and it is shown that these have an additional term due to the interconnected functions compared with the centralized approach. Then, the reachable set for the interconnected nonlinear system with DDSC will be approximated numerically in the form of an ellipsoid in the framework of convex optimization. Finally an illustrative example will be presented
Blind Timing Skew Estimation Using Source Spectrum Sparsity in Time-Interleaved ADCs

Timing skews in time-interleaved analog-to-digital converter (ADC) (TIADC) greatly degrade the spurious-free dynamic range of the TIADC, which can be improved effectively by digital compensation algorithms. Literature review shows that many nonblind compensation algorithms required the accurate timing skew parameters measured before the compensation process. In this paper, the blind timing skew estimation (TSE) for the TIADC system is investigated. With the evaluation of the working rationale of the TIADC system and the undersampling nature in each sub-ADC, the relation between the output digital spectra of the sub-ADCs and the source analog spectrum is formulated. A novel subband partition approach has been proposed, and the corresponding expression of the output digital subband spectra of the sub-ADCs in terms of the output digital subband spectra of the TIADC is obtained. With the assumption of the source spectra sparsity, the nonoverlapping frequency points can be determined from the output digital subband spectra of the sub-ADCs. Making use of the determined nonoverlapping frequency points, the phase information of the output digital subband spectra of the sub-ADCs can be employed to provide a good metric to estimate the timing skews, and a closed-form expression of the TSE has been derived. As the result, the source spectrum sparsity-based blind TSE (SS-BLTSE) algorithm has been developed and evaluated. Computer simulation results show that the SS-BLTSE algorithm provides good TSE performance for arbitrarily narrow or wideband source signals. Moreover, it has many merits over the existing blind TSE algorithms, such as there is no prior requirement of the source signal except that it has certain spectrum sparsity, no limit to the number of the sub-ADCs, and the frequency of the input signal can be approaching the Nyquist frequency.
Intelligence Graph

In fact, there exist three genres of intelligence architectures: logics (e.g. Random Forest, A∗ Searching), neurons (e.g. CNN, LSTM) and probabilities (e.g. Naive Bayes, HMM), all of which are incompatible to each other. However, to construct powerful intelligence systems with various methods, we propose the intelligence graph (short as iGraph), which is composed by both of neural and probabilistic graph, under the framework of forward-backward propagation. By the paradigm of iGraph, we design a recommendation model with semantic principle. First, the probabilistic distributions of categories are generated from the embedding representations of users/items, in the manner of neurons. Second, the probabilistic graph infers the distributions of features, in the manner of probabilities. Last, for the recommendation diversity, we perform an expectation computation then conduct a logic judgment, in the manner of logics. Experimentally, we beat the state-of-the-art baselines and verify our conclusions.
Clench Interface: Novel Biting Input Techniques

People eat every day and biting is one of the most fundamental and natural actions that they perform on a daily basis. Existing work has explored tooth click location and jaw movement as input techniques, however clenching has the potential to add control to this input channel. We propose clench interaction that leverages clenching as an actively controlled physiological signal that can facilitate interactions. We conducted a user study to investigate users' ability to control their clench force. We found that users can easily discriminate three force levels, and that they can quickly confirm actions by unclenching (quick release). We developed a design space for clench interaction based on the results and investigated the usability of the clench interface. Participants preferred the clench over baselines and indicated a willingness to use clench-based interactions. This novel technique can provide an additional input method in cases where users' eyes or hands are busy, augment immersive experiences such as virtual/augmented reality, and assist individuals with disabilities.
Polarity detection movie reviews in hindi language

Nowadays peoples are actively involved in giving comments and reviews on social networking websites and other websites like shopping websites, news websites etc. large number of people everyday share their opinion on the web, results is a large number of user data is collected . users also find it trivial task to read all the reviews and then reached into the decision. It would be better if these reviews are classified into some category so that the user finds it easier to read. Opinion Mining or Sentiment Analysis is a natural language processing task that mines information from various text forms such as reviews, news, and blogs and classify them on the basis of their polarity as positive, negative or neutral. But, from the last few years, user content in Hindi language is also increasing at a rapid rate on the Web. So it is very important to perform opinion mining in Hindi language as well. In this paper a Hindi language opinion mining system is proposed. The system classifies the reviews as positive, negative and neutral for Hindi language. Negation is also handled in the proposed system. Experimental results using reviews of movies show the effectiveness of the system
Supporting vehicular mobility in urban multi-hop wireless networks

Deployments of city-wide multi-hop 802.11 networks introduce challenges for maintaining client performance at vehicular speeds. We experimentally demonstrate that current network interfaces employ policies that result in long outage durations, even when clients are always in range of at least one access point. Consequently, we design and evaluate a family of client-driven handoff techniques that target vehicular mobility in multi-tier multi-hop wireless mesh networks. Our key technique is for clients to invoke an association change based on (i) joint use of channel quality measurements and AP quality scores that reflect long-term differences in AP performance and (ii) controlled measurement and hand-off time scales to balance the need for the instantaneously best association against performance penalties incurred from spurious handoffs due to channel fluctuations and marginally improved associations. We utilize a 4,000 user urban deployment to evaluate the performance of a broad class of hand-off policies.
Novel ultra-wideband slot-line antenna designed by adaptive real coded genetic algorithm

In the proposed paper, a novel types of genetic algorithms are used as a global optimization method to seek the geometry of ultra-band slot-line antenna (UWB SLA). The global optimization method is combined with local optimization method for accurate results of optimization. For accurate and wideband computation, time domain method in CST MW Studio is used. The antenna is optimized for two basic parameters: wideband impedance matching and directivity pattern of the antenna.
Goblint : Path-Sensitive Data Race Analysis ?

We present Goblint, a static analyzer for detecting potential data races in the multithreaded C code. The implemented analysis is sound on a “safe” subset of C and sufficiently efficient to be used for race-detection of multithreaded programs up to about 25 thousand lines of code. It uses a global invariant approach to avoid the state space explosion problem and is both contextand path-sensitive.
Non-Invasive Brain-Machine Interaction

The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain electrical activity online, several groups have designed brain-actuated devices that provide alternative channels for communication, entertainment and control. Thus, a person can write messages using a virtual keyboard on a computer screen and also browse the internet. Alternatively, subjects can operate simple computer games, or brain games, and interact with educational software. Work with humans has shown that it is possible for them to move a cursor and even to drive a wheelchair. This paper briefly reviews the field of BCI, with a focus on non-invasive systems based on electroencephalogram (EEG) signals. It also describes three brain-actuated devices we have developed: a virtual keyboard, a brain game, and a wheelchair. Finally, it shortly discusses current research directions we are pursuing in order to improve the performance and robustness of our BCI system, especially for real-time control of brain actuated robots.
p-th Power total variation regularization in photon-limited imaging via iterative reweighting

Recent work in ℓp-norm regularized sparsity recovery problems (where 0 ≤ p ≤ 1) has shown that signals can be recovered with very high accuracy despite the fact that the solution to these nonconvex optimization problems are not necessarily the global minima but are instead potentially local minima. In particular, ℓp-norm regularization has been used effectively for signal reconstruction from measurements corrupted by zero-mean additive Gaussian noise. This paper describes a p-th power total variation (TVp) regularized op timization approach for image recovery problems in photon-limited settings using iterative reweighting. The proposed method iteratively convexities a sequence of nonconvex TVp subproblems using a weighted TV approach and is solved using a modification to the FISTA method for TV-based de-noising. We explore the effectiveness of the proposed method through numerical experiments in image deblurring.
Wood‐Based Nanotechnologies toward Sustainability

With over 30% global land coverage, the forest is one of nature's most generous gifts to human beings, providing shelters and materials for all living beings. Apart from being sustainable, renewable, and biodegradable, wood and its derivative materials are also extremely fascinating from a materials aspect, with numerous advantages including porous and hierarchical structure, excellent mechanical performance, and versatile chemistry. Here, strategies for designing novel wood‐based materials via advanced nanotechnologies are summarized, including both the controllable bottom‐up assembly from the highly crystalline nanocellulose building block and the more efficient top‐down approaches directly from wood. Beyond material design, recent advances regarding the sustainable applications of these novel wood‐based materials are also presented, focusing on areas that are traditionally dominated by man‐made nonrenewable materials such as plastic, glass, and metals, as well as more advanced applications in the areas of energy storage, wastewater treatment and solar‐steam‐assisted desalination. With all recent progress pertaining to materials' design and sustainable applications presented, a vision for the future engineering of wood‐based materials to promote continuous and healthy progress toward true sustainability is outlined.
Modellus: Automated modeling of complex internet data center applications

The rising complexity of distributed server applications in Internet data centers has made the tasks of modeling and analyzing their behavior increasingly difficult. This article presents Modellus, a novel system for automated modeling of complex web-based data center applications using methods from queuing theory, data mining, and machine learning. Modellus uses queuing theory and statistical methods to automatically derive models to predict the resource usage of an application and the workload it triggers; these models can be composed to capture multiple dependencies between interacting applications. Model accuracy is maintained by fast, distributed testing, automated relearning of models when they change, and methods to bound prediction errors in composite models. We have implemented a prototype of Modellus, deployed it on a data center testbed, and evaluated its efficacy for modeling and analysis of several distributed multitier web applications. Our results show that this feature-based modeling technique is able to make predictions across several data center tiers, and maintain predictive accuracy (typically 95% or better) in the face of significant shifts in workload composition; we also demonstrate practical applications of the Modellus system to prediction and provisioning of real-world data center applications.
The Roof is Leaking and a Storm is Raging: Repairing the Blood–Brain Barrier in the Fight Against Epilepsy

A large body of evidence that has accumulated over the past decade strongly supports the role of both blood–brain barrier (BBB) dysfunction and perivascular inflammation in the pathophysiology of epilepsy. Recent preclinical studies indicate that prolonged seizure- or brain injury-induced BBB dysfunction and subsequent perivascular inflammation may play an important role in post-traumatic epileptogenesis. In turn, perivascular inflammation can further sustain BBB dysfunction. In genetic epilepsies, such as tuberous sclerosis complex and other related epileptogenic developmental pathologies, there is an association between the underlying gene mutation, BBB dysfunction, and perivascular inflammation, but evidence for a causal link to epilepsy is lacking. Future neuroimaging studies might shed light on the role of BBB function in different epilepsies and address the potential for disease modification by targeting both the BBB and perivascular inflammation in acquired and genetic epilepsies.
Dorsal, a Drosophila Rel-like protein, is phosphorylated upon activation of the transmembrane protein Toll

The nuclear import of dorsal, a Drosophila Rel homolog, is directed by a spatially restricted extracellular ligand in blastoderm embryos. We have demonstrated both that dorsal is an embryonic phosphoprotein and that its phosphorylation state is regulated by an intracellular signaling pathway initiated by the transmembrane receptor Toll. Immunoblot analysis of cytoplasm from precisely staged embryos revealed that the phosphorylation state of dorsal is altered during the time period that Toll is activated. Moreover, mutations that constitutively activate Toll stimulated dorsal phosphorylation, while mutations that block Toll activation reduced the level of dorsal phosphorylation. We further demonstrated that signal-dependent dorsal phosphorylation is modulated by three intracellular proteins, pelle, tube, and cactus. Using double-mutant embryos, we then explored the nature of the kinase activity responsible for dorsal phosphorylation. We found that free dorsal is a substrate for a signal-independent kinase activity. In addition, our results imply that dorsal is a substrate for a Toll-dependent kinase. These results are consistent with the hypothesis that phosphorylation of Rel-related proteins may be required for the proper nuclear localization and transcriptional activity of these proteins.
On the placement and granularity of FPGA configurations

Dynamic FPGA reconfiguration represents an overhead that can be critical to the performance of a realised circuit. To address this problem, This work presents a technique that is applicable at the times of loading the configuration data on the device. The technique involves reusing the on-chip configuration fragments to implement the next configuration thereby reducing the amount of data that must be externally transferred to the configuration memory. This work provides an analysis of the effect of circuit placement and configuration granularity on configuration reuse. The problem of finding placements of each circuit in a sequence of circuits so as to maximize configuration re-use is considered in detail. A greedy solution to this NP complete problem was found to reduce configuration overheads by less than 5% for a benchmark set. The effect of configuration granularity on configuration reuse was also considered and it was found that reducing the size of the unit of configuration allowed us to reduce the size of the benchmark configurations by 41%.
Flow-based power control in Cognitive Radio ad-hoc Network

In Cognitive Radio Networks (CRN), the appearance of more active primary users (PUs) might require blocking of one or more secondary user (SU) transmissions to maintain interference constraints at PU receivers and Quality of Service (QoS) constraints of SUs. In general, SU transmitters need to adjust their transmission powers to maintain these constraints at all times. In this paper, we are interested in maximizing the number of concurrent SU flows under new PU arrivals and performing power allocation for SU links belonging to maintained data flows to maximize the minimum flow capacity. In particular, we develop a flow maintenance and power control scheme to solve this problem, which is referred to as Flow-Based Power Control (FBPC). The proposed scheme comprises two phases where we attempt to find a best set of flows with maximum size in the first phase and maximize the minimum flow capacity through power control in the second phase. To reduce computation complexity required in the first phase, we develop a flow combination metric, which is used to find the best flow combination. Moreover, we formulate power allocation problem that maximizes the minimum flow capacity and show how to transform it into a geometric convex program. Numerical results are presented to show the efficiency of the proposed flow combination metric and the desirable performance of the proposed two-phase flow maintenance and power control scheme.
Frequency-Specific Alterations of Local Synchronization in Idiopathic Generalized Epilepsy

AbstractRecurrently and abnormally hypersynchronous discharge is a striking feature of idiopathic generalized epilepsy (IGE). Resting-state functional magnetic resonance imaging has revealed aberrant spontaneous brain synchronization, predominately in low-frequency range (<0.1 Hz), in individuals with IGE. Little is known, however, about these changes in local synchronization across different frequency bands. We examined alterations to frequency-specific local synchronization in terms of spontaneous blood oxygen level-dependent (BOLD) fluctuations across 5 bands, spanning 0 to 0.25 Hz. Specifically, we compared brain activity in a large cohort of IGE patients (n = 86) to age- and sex-matched normal controls (n = 86). IGE patients showed decreased local synchronization in low frequency (<0.073 Hz), primarily in the default mode network (DMN). IGE patients also exhibited increased local synchronization in high-frequency (>0.073 Hz) in a “conscious perception network,” which is anchored by the pregenual and dorsal anterior cingulate cortex, as well as the bilateral insular cortices, possibly contributing to impaired consciousness. Furthermore, we found frequency-specific alternating local synchronization in the posterior portion of the DMN relative to the anterior part, suggesting an interaction between the disease and frequency bands. Importantly, the aberrant high-frequency local synchronization in the middle cingulate cortex was associated with disease duration, thus linking BOLD frequency changes to disease severity. These findings provide an overview of frequency-specific local synchronization of BOLD fluctuations, and may be helpful in uncovering abnormal synchronous neuronal activity in patients with IGE at specific frequency bands.