Miracle-dz/UniFAR-FTUs huggingface dataset:
Connect on the fly: Enhancing and prototyping of cycle-reconfigurable modules

This paper introduces cycle-reconfigurable modules that enhance FPGA architectures with efficient support for dynamic data accesses: data accesses with accessed data size and location known only at runtime. The proposed module adopts new reconfiguration strategies based on dynamic FIFOs, dynamic caches, and dynamic shared memories to significantly reduce configuration generation and routing complexity. We develop a prototype FPGA chip with the proposed cycle-reconfigurable module in the SMIC 130-nm technology. The integrated module takes less than the chip area of 39 CLBs, and reconfigures thousands of runtime connections in 1.2 ns. Applications for large-scale sorting, sparse matrix-vector multiplication, and Memcached are developed. The proposed modules enable 1.4 and 11 times reduction in area-delay product compared with those applications mapped to previous architectures and conventional FPGAs.
Recurrent Cartesian Genetic Programming Applied to Series Forecasting

Recurrent Cartesian Genetic Programming is a recently proposed extension to Cartesian Genetic Programming which allows cyclic program structures to be evolved. We apply both standard and Recurrent Cartesian Genetic Programming to the domain of series forecasting. Their performance is then compared to a number of well-known classical forecasting approaches. Our results show that not only does Recurrent Cartesian Genetic Programming outperform standard Cartesian Genetic Programming, but it also outperforms many standard forecasting techniques.
Protein synthesis during transition and stationary phases under glucose limitation in Saccharomyces cerevisiae

Metabolic changes have been investigated during continuous growth of yeast cells inoculated in glucose-containing medium until the cells entered the stationary phase in response to glucose exhaustion. Well in advance of glucose exhaustion, a transition phase was observed, characterized by a decrease in the growth rate and a progressive reduction of protein and RNA accumulation. Two-dimensional gel analysis of the proteins synthesized during this stage showed that the pattern of proteins remained similar to that of log-phase cells. When the cells entered the stationary phase, protein accumulation was 10% of that in log-phase cells, and incorporation of labeled RNA precursor was undetectable. Analysis of protein synthesis gave evidence that the synthesis of 95% of the proteins present in log-phase cells was arrested in stationary-phase cells. Among the 20 proteins whose synthesis continues throughout the stationary phase were identified actin, aldehyde dehydrogenase, enolase, hexokinase, glyceraldehyde-3-phosphate dehydrogenase, and five heat shock proteins. In addition, the synthesis of six new proteins was observed. The occurrence of these new proteins in stationary-phase cells is presumed to result from the release of carbon catabolite repression due to glucose exhaustion.
Towards Imitation Learning of Dynamic Manipulation Tasks: A Framework to Learn from Failures

We present an imitation learning approach for a dynamic fluid pouring task. Our approach allows learning from errors made by humans and how they recovered from these error s subsequently. We collect both successful and failed human demonstrations of the task. Our algorithm combines a s upport vector machine based classifier and iterative search to generate initial task parameters for the robot. Ne xt, a refinement algorithm, capturing how demonstrators change parameters to transition from failure to success, en abl s the robot to address failures. Experimental results with a physical robot are reported to illustrate our approac h.
A tensor-based approach for automatic music genre classification

Most music genre classification techniques employ pattern recognition algorithms to classify feature vectors extracted from recordings into genres. An automatic music genre classification system using tensor representations is proposed, where each recording is represented by a feature matrix over time. Thus, a feature tensor is created by concatenating the feature matrices associated to the recordings. A novel algorithm for non-negative tensor factorization (NTF), which employs the Frobenius norm between an n-dimensional raw feature tensor and its decomposition into a sum of elementary rank-1 tensors, is developed. Moreover, a supervised NTF classifier is proposed. A variety of sound description features are extracted from recordings from the GTZAN dataset, covering 10 genre classes. NTF classifier performance is compared against multilayer perceptrons, support vector machines, and non-negative matrix factorization classifiers. On average, genre classification accuracy equal to 75% with a standard deviation of 1% is achieved. It is demonstrated that NTF classifiers outperform matrix-based ones.
Alison Cawsey: Explanation and Interaction. The Computer Generation of Explanatory Dialogues.

The book presents a model of interactive explanation generation and its implementation as the tutorial dialogue system EDGE (Explanatory Discourse GEnerator). EDGE produces explanations on electronic circuits and, by entering a dialogue with the user, is able to adjust its explanations according to the user's background knowledge and her understanding of the ongoing explanation. The system, originally designed and implemented for the authors's PhD and improved on the basis of the evaluation results by some testers, is impressive in its apt functionality. Despite of the underlying model's arguably simple techniques, the system manifests a successful combination of di erent ideas and shows the author's excellent knowledge of the related elds. The book is divided into seven chapters and it contains four appendices. The rst two chapters provide an introduction to the book and an analysis of human explanatory dialogues which serve as the empirical grounding of the design decisions. The next three chapters describe the system's architecture and implementation, discussing issues of content planning (chapter 3), dialogue management (chapter 4) and user modelling (chapter 5), with reference to related works. Chapter 6 presents the system's application to the particular domain (explanations of the working of electronic circuits) and gives examples of how the whole system works. Finally, chapter 7 discusses the evaluation of the system's behaviour and the overall approach, summarises the contributions and points to extensions and future work. The four appendices show human explanations of circuit behaviour, a subset of EDGE's content and discourse planning rules, sample explanations produced by the system, and example displays. The goal of the work was to develop an "e ective and practical approach to generating interactive explanations" (p. 126). Starting with the analysis of non-interactive explanations, the author develops a model of expert-dominant tutorial dialogues, where the structure of the dialogues follows the structure of a non-interactive explanation, but the expert can ask questions to check the novice's understanding and the novice is allowed to interrupt with clari cation questions at any point of the explanation. The empirical basis of the model is found in a corpus of 52 interactive and 32 non-interactive human explanations, dealing with experts' explaning to novices about how electronic circuits work. The author then combines known techniques of text planning (schemata) and dialogue planning (dialogue grammar) to implement the model
A high-precision low-voltage low dropout regulator for SoC with adaptive biasing

A high-precision low-voltage low dropout regulator (LDR) using adaptive biasing to extend the loop bandwidth is proposed for system-on-chip power management applications. The multi-stage output-capacitor-free LDR is stabilized by miller compensation and Q-reduction technique to reduce the requirement of minimum load current. By using direct current feedback from a simple current mirror, the adaptively-biased LDR achieves higher loop bandwidth, faster load and line transient responses, higher power supply rejection and lower output impedance. The load and line regulations are also improved. Designed in a standard 0. 35µm CMOS technology (Vtn≈ 0.52V and Vtp≈ −0.72V ), the 1.2V input 1.0V output LDR requires a minimum load current of 50µA and delivers a maximum current of 100mA. Both theoretical analysis and simulation results are presented to demonstrate the advantages of the proposed LDR.
Cerebral Metabolism is Influenced by Muscle Ischaemia During Exercise in Humans

Maximal exercise reduces the cerebral metabolic ratio (O2/(glucose + 1/2lactate)) to < 4 from a resting value close to 6, and only part of this decrease is explained by the ‘intent’ to exercise. This study evaluated whether sensory stimulation of brain by muscle ischaemia would reduce the cerebral metabolic ratio. In 10 healthy human subjects the cerebral arterial‐venous differences (a‐v differences) for O2, glucose and lactate were assessed before, during and after three bouts of 10 min cycling with equal workload: (1) control exercise at light intensity, (2) exercise that elicited a high rating of perceived exertion due to a 100 mmHg thigh cuff, and (3) exercise followed by 5 min of post‐exercise muscle ischaemia that increased blood pressure by ∼ 20%. Control exercise did not significantly affect the a‐v differences. However, during the recovery from exercise with thigh cuffs the cerebral metabolic ratio decreased from a resting value of 5.4 ± 0.2 to 4.0 ± 0.4 (mean ±s.e.m. P < 0.05) as a discrete lactate efflux from the brain at rest shifted to a slight uptake. Also, following post‐exercise muscle ischaemia, the cerebral metabolic ratio decreased to 4.5 ± 0.3 (P < 0.05). The results support the hypothesis that during exercise, cerebral metabolism is influenced both by the mental effort to exercise and by sensory input from skeletal muscles.
Brain Monitoring in Critically Neurologically Impaired Patients

Assessment of neurologic injury and the evolution of severe neurologic injury is limited in comatose or critically ill patients that lack a reliable neurologic examination. For common yet severe pathologies such as the comatose state after cardiac arrest, aneurysmal subarachnoid hemorrhage (aSAH), and severe traumatic brain injury (TBI), critical medical decisions are made on the basis of the neurologic injury. Decisions regarding active intensive care management, need for neurosurgical intervention, and withdrawal of care, depend on a reliable, high-quality assessment of the true state of neurologic injury, and have traditionally relied on limited assessments such as intracranial pressure monitoring and electroencephalogram. However, even within TBI there exists a spectrum of disease that is likely not captured by such limited monitoring and thus a more directed effort towards obtaining a more robust biophysical signature of the individual patient must be undertaken. In this review, multimodal monitoring including the most promising serum markers of neuronal injury, cerebral microdialysis, brain tissue oxygenation, and pressure reactivity index to access brain microenvironment will be discussed with their utility among specific pathologies that may help determine a more complete picture of the neurologic injury state for active intensive care management and long-term outcomes. Goal-directed therapy guided by a multi-modality approach appears to be superior to standard intracranial pressure (ICP) guided therapy and should be explored further across multiple pathologies. Future directions including the application of optogenetics to evaluate brain injury and recovery and even as an adjunct monitoring modality will also be discussed.
Computation of bit error ratios for a dense WDM system using the noise covariance matrix and multicanonical Monte Carlo methods

We extend the noise covariance matrix method to dense wavelength-division-multiplexed (DWDM) systems in order to efficiently and accurately compute the probability density function of the received voltage in the central channel of a DWDM 10-Gb/s chirped return-to-zero transmission system with a channel spacing of 50 GHz and a transmission distance of 6120 km. The results agree with those that we obtain using a multicanonical Monte Carlo method, which mutually validates both methods.
Evolution of morphological characteristics of Si:H p-i-n structures deposited by plasma on “corning” substrates

In this work we present a study of morphological characteristics evolution in the various layers comprising a Si:H p-i-n structure, which are deposited by PECVD on “Corning” glass substrates coated with transparent conductive Al-doped zinc oxide (AZO). AFM measurements were conducted over area of 2 × 2 μm2 to determine morphological characteristics of the substrates, carbon layer, p-layer, as well as intrinsic layer and finally the n-layer. AFM study of surface morphology showed that average height, kurtosis and rms roughness changed with the same tendency as the subsequent layers were deposited, while skewness showed opposite behavior. The maximum value of roughness is 3.14 nm for AZO layer and the lowest roughness value of 2.23 nm was achieved in the n-type layer.
Wafer bonding using microwave heating of parylene for MEMS packaging

This paper describes a novel wafer bonding technique using microwave beating of parylene intermediate layers. The bonding is achieved by parylene deposition and thermal lamination using microwave heating. Variable frequency microwave heating provides uniform, selective, and rapid heating for parylene intermediate layers. The advantages of this bonding technique include short bonding time, low bonding temperature, relatively high bonding strength, less void generation, and low thermal stress. In addition, the intermediate layer material, parylene, is chemically stable and biocompatible. This bonding technique can be used for structured wafers as well because parylene provides a conformal coating. Therefore this is a very attractive bonding tool for many MEMS devices. The bonding strength and uniformity were evaluated using diverse tools. Fracture mechanisms and the effects of bonding parameters and an adhesion promoter were investigated as well. The bonding with a structured wafer was also successfully demonstrated.
The C-Terminus of H-Ras as a Target for the Covalent Binding of Reactive Compounds Modulating Ras-Dependent Pathways

Ras proteins are crucial players in differentiation and oncogenesis and constitute important drug targets. The localization and activity of Ras proteins are highly dependent on posttranslational modifications at their C-termini. In addition to an isoprenylated cysteine, H-Ras, but not other Ras proteins, possesses two cysteine residues (C181 and C184) in the C-terminal hypervariable domain that act as palmitoylation sites in cells. Cyclopentenone prostaglandins (cyPG) are reactive lipidic mediators that covalently bind to H-Ras and activate H-Ras dependent pathways. Dienone cyPG, such as 15-deoxy-Δ12,14-PGJ2 (15d-PGJ2) and Δ12-PGJ2 selectively bind to the H-Ras hypervariable domain. Here we show that these cyPG bind simultaneously C181 and C184 of H-Ras, thus potentially altering the conformational tendencies of the hypervariable domain. Based on these results, we have explored the capacity of several bifunctional cysteine reactive small molecules to bind to the hypervariable domain of H-Ras proteins. Interestingly, phenylarsine oxide (PAO), a widely used tyrosine phosphatase inhibitor, and dibromobimane, a cross-linking agent used for cysteine mapping, effectively bind H-Ras hypervariable domain. The interaction of PAO with H-Ras takes place in vitro and in cells and blocks modification of H-Ras by 15d-PGJ2. Moreover, PAO treatment selectively alters H-Ras membrane partition and the pattern of H-Ras activation in cells, from the plasma membrane to endomembranes. These results identify H-Ras as a novel target for PAO. More importantly, these observations reveal that small molecules or reactive intermediates interacting with spatially vicinal cysteines induce intramolecular cross-linking of H-Ras C-terminus potentially contributing to the modulation of Ras-dependent pathways.
Aeronautical $Ad~Hoc$ Networking for the Internet-Above-the-Clouds

The engineering vision of relying on the “smart sky” for supporting air traffic and the “internet-above-the-clouds” for in-flight entertainment has become imperative for the future aircraft industry. Aeronautical ad hoc networking (AANET) constitutes a compelling concept for providing broadband communications above clouds by extending the coverage of air-to-ground (A2G) networks to oceanic and remote airspace via autonomous and self-configured wireless networking among commercial passenger airplanes. The AANET concept may be viewed as a new member of the family of mobile ad hoc networks (MANETs) in action above the clouds. However, AANETs have more dynamic topologies, larger and more variable geographical network size, stricter security requirements, and more hostile transmission conditions. These specific characteristics lead to more grave challenges in aircraft mobility modeling, aeronautical channel modeling, and interference mitigation as well as in network scheduling and routing. This paper provides an overview of AANET solutions by characterizing the associated scenarios, requirements, and challenges. Explicitly, the research addressing the key techniques of AANETs, such as their mobility models, network scheduling and routing, security, and interference, is reviewed. Furthermore, we also identify the remaining challenges associated with developing AANETs and present their prospective solutions as well as open issues. The design framework of AANETs and the key technical issues are investigated along with some recent research results. Furthermore, a range of performance metrics optimized in designing AANETs and a number of representative multiobjective optimization algorithms are outlined.
Microbial communities and their genetic repertoire mediate the decomposition of soil organic carbon pools in revegetation shrublands in a desert in northern China

Revegetation using xerophytic plants in drylands can alter soil microbial community diversity and composition; however, information about the microbial communities and their genetic repertoires that are involved in soil organic carbon (SOC) processes in drylands remain unclear. This study aimed to characterize soil microbial genes and their potential to mediate SOC decomposition in three revegetation shrublands (Artemisia ordosica, Salix psammophila and Caragana microphylla) in the Mu Us Desert of northern China using an SOC mineralization experiment and shotgun metagenome sequencing. Among the three shrublands, the S. psammophila shrubland had the highest cumulative CO2 emissions and highest rates of mineralization of the slow SOC pool. The mineralization rate of the slow SOC pool and the microbial taxonomic and functional composition displayed a significant correlation. Most C‐degradation genes were assigned to three bacterial phyla, Actinobacteria, Proteobacteria and Acidobacteria. The relative abundance of microbial functional genes involved in degrading recalcitrant C‐complexes was significantly higher in the S. psammophila shrubland, resulting in the slow accumulation of recalcitrant SOC. These results suggest that the microbial functional genes that regulate the decomposition of the SOC pool in dryland shrubland soils may be influenced by shrub species selection. These findings highlight the importance of understanding how microbial catabolic potential mediates the potential for SOC sequestration in different revegetation shrublands in drylands.
Simkania negevensis, an insight into the biology and clinical importance of a novel member of the Chlamydiales order

Abstract Simkania negevensis is a Chlamydia-related bacterium discovered in 1993 and represents the founding member of the Simkaniaceae family within the Chlamydiales order. As other Chlamydiales, it is an obligate intracellular bacterium characterized by a biphasic developmental cycle. Its similarities with the pathogenic Chlamydia trachomatis and Chlamydia pneumoniae make it an interesting bacterium. So far, little is known about its biology, but S. negevensis harbors various microbiological characteristics of interest, including a strong association of the Simkania-containing vacuole with the ER and the presence of an intron in the 23S rRNA encoding gene. Evidence of human exposition has been reported worldwide. However, there is a lack of robust clinical studies evaluating its implication in human diseases; current data suggest an association with pneumonia and bronchiolitis making S. negevensis a potential emerging pathogen. Owing to its fastidious growth requirements, the clinical relevance of S. negevensis is probably underestimated. In this review, we summarize the current knowledge on S. negevensis and explore future research challenges.
TSDDR: Threshold Sensitive Density Controlled Divide and Rule Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks, efficient energy management is of great importance. In this paper, we propose a novel routing protocol, Threshold Sensitive Density Controlled Divide and Rule (TSDDR) to prolong network lifetime and stability period. To achieve these targets, we utilize static clustering with threshold aware transmissions. Simulations are done in MATLAB and the results show that our protocol has 60% longer stability period than LEACH [1] and 36% longer stability period than DDR [2]. We also implemented the Uniform Random Model (URM) to find Packet Drop to make our scheme more practical.
Recent Potential Noninvasive Biomarkers in Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a rare but devastating gastrointestinal disease that predominately affects preterm neonates. Numerous studies have revealed that NEC is strongly associated with very low birth weight, degree of prematurity, formula feeding, infection, hypoxic/ischemic injury, and enteric dysbiosis. Given these clinical associations, the search for a deeper understanding of disease pathogenesis has led to an intense interest in the discovery and development of noninvasive biomarkers of NEC from stool, urine, and serum. Biomarkers for NEC may serve at least two general purposes of urgent unmet need: to improve diagnostic accuracy and disease prediction and to reveal the mechanism of the disease. This review will provide an overview of recent research focused on clinical NEC and highlight the advances that were made within the past five years towards the development of noninvasive diagnostic biomarkers.
Atypical adenomatous hyperplasia of the lung and its differentiation from adenocarcinoma. Characterization of atypical cells by morphometry and multivariate cluster analysis

Background. Atypical adenomatous hyperplasia (AAH) of the human lung is considered to be an important lesion preceding adenocarcinoma, but its difference from well‐differentiated adenocarcinomas is so subtle as to cause diagnostic uncertainty. In view of this, the authors undertook to establish reproducible microscopic criteria for AAH, Type II pneumocyte type, and Clara cell type adenocarcinomas.