Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Power Control for Multi-Hop Energy Harvesting Links With Retransmission

In this paper, we consider an energy harvesting (EH) node that periodically takes a measurement and conveys it to a destination over multiple EH relays operating in the decode-and-forward fashion, using the automatic repeat request protocol. Packets that are not delivered to the destination before the next measurement is taken are dropped. We seek to design an online retransmission-index based power control policy (RIP) for each node which minimizes the packet drop probability (PDP). To this end, we first derive an expression for the PDP in terms of the RIPs at the nodes. Next, when the energy cost for decoding a packet is negligible, we obtain closed form expressions for the optimal RIPs. We also extend the results to the case where the peak transmit power is constrained. When the energy cost of decoding is non-negligible, we present a geometric programming based iterative algorithm to obtain near-optimal RIPs. In both the scenarios, in order to obtain insight into the impact of channel coherence time, we design the RIPs for both slow and fast fading channels. Through Monte Carlo simulations, we show that the proposed policies significantly outperform state-of-the-art solutions.
Depression in General Practice:

Summary General practice (GP) depressives prescribed an antidepressant were compared with those given other treatment, and with antidepressant-treated psychiatric out-patient depressives. GP depressives were considerably less severely ill than out-patients, with fewer depressive symptoms and shorter illness, as well as less primary and less endogenous depression. The two groups of GP depressives differed less, but those receiving other treatment tended to have less severe depression than those receiving antidepressants and were less likely to satisfy diagnostic criteria for depression. Depressives in GP differ considerably in clinical characteristics from psychiatric out-patient depressives, and clinical features influence the GP's decision to treat with antidepressants.
Experimental Study on the Pickling of Zircaloy Tubes and Appendages

The pressurized heavy water reactors use natural uranium oxide (UO2) as fuel and use cladding material made up of zircaloy, an alloy of zirconium. Pickling of zircaloy tubes and appendages viz., spacer and bearing pads is carried out to remove the oxide layer and surface contaminants. Experiments were conducted to optimize the composition of zircaloy pickling solution. The HF-HNO3 ratio in pickling solution and the effect of the optimized solution composition with both fresh and regenerated solution was studied. In this present paper, the authors present their experience about the recycling suitability of the used pickling solution and the rate of pickling reaction with the optimized solution.
Controllable two-photon interference with versatile quantum frequency processor

Quantum information is the next frontier in information science, promising unconditionally secure communications, enhanced channel capacities, and computing capabilities far beyond their classical counterparts. And as quantum information processing devices continue to transition from the lab to the field, the demand for the foundational infrastructure connecting them with each other and their users---the quantum internet---will only increase. Due to the remarkable success of frequency multiplexing and control in the classical internet, quantum information encoding in optical frequency offers an intriguing synergy with state-of-the-art fiber-optic networks. Yet coherent quantum frequency operations prove extremely challenging, due to the difficulties in mixing frequencies efficiently, arbitrarily, in parallel, and with low noise. Here we implement an original approach based on a reconfigurable quantum frequency processor, designed to perform arbitrary manipulations of spectrally encoded qubits. This processor's unique tunability allows us to demonstrate frequency-bin Hong-Ou-Mandel interference with record-high 94% visibility. Furthermore, by incorporating such tunability with our method's natural parallelizability, we synthesize independent quantum frequency gates in the same device, realizing the first high-fidelity flip of spectral correlations on two entangled photons. Compared to quantum frequency mixing approaches based on nonlinear optics, our linear method removes the need for additional pump fields and significantly reduces background noise. Our results demonstrate multiple functionalities in parallel in a single platform, representing a huge step forward for the frequency-multiplexed quantum internet.
Effects of Gradient Coil Noise and Gradient Coil Replacement on the Reproducibility of Resting State Networks

The stability of the MRI scanner throughout a given study is critical in minimizing hardware-induced variability in the acquired imaging data set. However, MRI scanners do malfunction at times, which could generate image artifacts and would require the replacement of a major component such as its gradient coil. In this article, we examined the effect of low intensity, randomly occurring hardware-related noise due to a faulty gradient coil on brain morphometric measures derived from T1-weighted images and resting state networks (RSNs) constructed from resting state functional MRI. We also introduced a method to detect and minimize the effect of the noise associated with a faulty gradient coil. Finally, we assessed the reproducibility of these morphometric measures and RSNs before and after gradient coil replacement. Our results showed that gradient coil noise, even at relatively low intensities, could introduce a large number of voxels exhibiting spurious significant connectivity changes in several RSNs. However, censoring the affected volumes during the analysis could minimize, if not completely eliminate, these spurious connectivity changes and could lead to reproducible RSNs even after gradient coil replacement.
A note on Stokes' problem in dense granular media using the $\mu(I)$--rheology

The classical Stokes' problem describing the fluid motion due to a steadily moving infinite wall is revisited in the context of dense granular flows of mono-dispersed beads using the recently proposed $\mu(I)$--rheology. In Newtonian fluids, molecular diffusion brings about a self-similar velocity profile and the boundary layer in which the fluid motion takes place increases indefinitely with time $t$ as $\sqrt{\nu t}$, where $\nu$ is the kinematic viscosity. For a dense granular visco-plastic liquid, it is shown that the local shear stress, when properly rescaled, exhibits self-similar behaviour at short-time scales and it then rapidly evolves towards a steady-state solution. The resulting shear layer increases in thickness as $\sqrt{\nu_g t}$ analogous to a Newtonian fluid where $\nu_g$ is an equivalent granular kinematic viscosity depending not only on the intrinsic properties of the granular media such as grain diameter $d$, density $\rho$ and friction coefficients but also on the applied pressure $p_w$ at the moving wall and the solid fraction $\phi$ (constant). In addition, the $\mu(I)$--rheology indicates that this growth continues until reaching the steady-state boundary layer thickness $\delta_s = \beta_w (p_w/\phi \rho g )$, independent of the grain size, at about a finite time proportional to $\beta_w^2 (p_w/\rho g d)^{3/2} \sqrt{d/g}$, where $g$ is the acceleration due to gravity and $\beta_w = (\tau_w - \tau_s)/\tau_s$ is the relative surplus of the steady-state wall shear-stress $\tau_w$ over the critical wall shear stress $\tau_s$ (yield stress) that is needed to bring the granular media into motion. (see article for a complete abstract).
Representing and resolving ambiguities in ontology-based question answering

Ambiguities are ubiquitous in natural language and pose a major challenge for the automatic interpretation of natural language expressions. In this paper we focus on different types of lexical ambiguities that play a role in the context of ontology-based question answering, and explore strategies for capturing and resolving them. We show that by employing underspecification techniques and by using ontological reasoning in order to filter out inconsistent interpretations as early as possible, the overall number of interpretations can be effectively reduced by 44 %.
Polyethylene green composites modified with post agricultural waste filler: thermo-mechanical and damping properties

Abstract The aim of the study was to determine thermo-mechanical properties and applicability of sunflower husk waste as a filler for ultra low density polyethylene composites. The post agricultural waste filler was milled and chemically treated with (3-aminopropyl)triethoxysilane (3-APS). The amount of filler used was 5, 10 and 20 wt%. The mechanical and thermal properties of the composites containing unmodified and modified natural fillers were determined in the course of static tensile test, rebound resilience by Schob method, and dynamic mechanic thermal analysis. The influence of filler loading and chemical modification of the filler on the morphology of natural composites was evaluated by SEM analysis.
DEGREE PROGRAMME IN WIRELESS COMMUNICATIONS ENGINEERING

The unmanned aerial vehicles (UAVs) can be deployed as aerial base stations or wireless relays to enhance the coverage and guarantee the quality of service (QoS) of wireless networks. In this thesis, the positioning of a full-duplex (FD) UAV as a relay to provide coverage for an FD vehicular network is investigated. This problem is solved using two different methods. In both of the methods, the problem is formulated using a predefined set of locations for the UAV. Then this problem is solved for different configurations of the ground users and an optimal location is selected for the UAV to operate at. In the first approach, given the position of the vehicular users on the ground, a novel algorithm is proposed to find a location for the UAV to satisfy the QoS requirements of the vehicles in the network. The positioning problem is formulated as an `0 minimization which is non-combinatorial and NP-hard, and finding a globally optimal solution for this problem has exponential complexity. Therefore, the `0-norm is approximated by the `1-norm. Simulation results show that by locating the UAV using the proposed algorithm the overall performance of the network increases. In the second approach, the UAV positioning problem is solved using an MAB framework. In this case, a simple scenario where only one source node is communicating with the relay to transmit its message to the base station is considered. Given the location of the source node and the predefined locations of the UAV, the MAB algorithm can successfully identify the optimal location for the UAV so the system achieves the maximum possible sum rate. The Greedy, -Greedy, and upper confidence bound (UCB) algorithms are used to solve the problem. The comparison of these algorithms based on their regret values reveals that the UCB algorithm outperforms the performance of the other algorithms. Simulation results show that the UCB algorithm can successfully identify the optimal location for the UAV to maximize the sum rate of the communication links.
Importance of Comprehensive Molecular Profiling for Clinical Outcome in Children With Recurrent Cancer

Purpose: Pediatric cancers are often difficult to classify and can be complex to treat. To ensure precise diagnostics and identify relevant treatment targets, we implemented comprehensive molecular profiling of consecutive pediatric patients with cancer relapse. We evaluated the clinical impact of extensive molecular profiling by assessing the frequency of identified biological onco-drivers, altered diagnosis, and/or identification of new relevant targeted therapies. Patients and Methods: Forty-six tumor samples (44 fresh-frozen; two formalin-fixed paraffin embedded), two bone marrow aspirates, three cerebrospinal fluid samples, and one archived DNA were obtained from 48 children (0–17 years; median 9.5) with relapsed or refractory cancer, where the disease was rapidly progressing in spite of their current treatment or they had exhausted all treatment options. The samples were analyzed by whole-exome sequencing (WES), RNA sequencing (RNAseq), transcriptome arrays, and SNP arrays. Final reports were available within 3–4 weeks after patient inclusion and included mutation status, a description of copy number alterations, differentially expressed genes, and gene fusions, as well as suggestions for targeted treatment. Results: Of the 48 patients, 33 had actionable findings. The most efficient method for the identification of actionable findings was WES (39%), followed by SNP array (37%). Of note, gene fusions were identified by RNAseq in 21% of the samples. Eleven findings led to clinical intervention, i.e., oncogenetic counseling, targeted treatment, and treatment based on changed diagnosis. Four patients received compassionate use targeted therapy. Six patients experienced direct benefits in the form of stable disease or response. Conclusion: The application of comprehensive genetic diagnostics in children with recurrent cancers allowed for discovery and implementation of effective targeted therapies and hereby improvement of outcome in some patients.
Decentralized Video Streaming in Multi-Hop Wireless Networks: Incentive Mechanism and Energy Efficiency

This paper studies video streaming from a source to multiple receivers in wireless networks. The video is streamed with the help of intermediate users who forward the video to others. Two main challenges affect user satisfaction in this network. The users usually have 1) different willingness to contribute (forwarding the video), and; 2) different preferences regarding the video quality. To overcome the challenges, we propose a framework based on an incentive/taxation mechanism in which the forwarding users, depending on their spent energy, are paid by their corresponding receivers. The video is layered such that the more video layers are received, the higher the quality-of-experience (QoE) and the higher the price. Using a decentralized game-theoretic algorithm, we define a user-specific utility function whose maximization determines the number of video layers a user wishes to receive. The utility function captures the user’s preferences including the importance of the video quality to her and her willingness to contribute. Our model supports the multicast transmission by which the receivers can use a common forwarder and share the cost. The simulation results show that the proposed model not only provides a higher QoE for the users compared to the preference-agnostic models but also improves the network social-welfare.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

We present PredRNN++, an improved recurrent network for video predictive learning. In pursuit of a greater spatiotemporal modeling capability, our approach increases the transition depth between adjacent states by leveraging a novel recurrent unit, which is named Causal LSTM for re-organizing the spatial and temporal memories in a cascaded mechanism. However, there is still a dilemma in video predictive learning: increasingly deep-in-time models have been designed for capturing complex variations, while introducing more difficulties in the gradient back-propagation. To alleviate this undesirable effect, we propose a Gradient Highway architecture, which provides alternative shorter routes for gradient flows from outputs back to long-range inputs. This architecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture short-term and long-term dependencies adaptively. We assess our model on both synthetic and real video datasets, showing its ability to ease the vanishing gradient problem and yield state-of-the-art prediction results even in a difficult objects occlusion scenario.
CINTIA: A distributed, low-latency index for big interval data

Intervals have become prominent in data management as they are the main data structure to represent a number of key data types such as temporal or genomic data. Yet, there exists no solution to compactly store and efficiently query big interval data. In this paper we introduce CINTIA - the Checkpoint INTerval Index Array - an efficient data structure to store and query interval data, which achieves high memory locality and outperforms state-of-the art solutions. We also propose a low-latency, Big Data system that implements CINTIA on top of a popular distributed file system and efficiently manages large interval data on clusters of commodity machines. Our system can easily be scaled-out and was designed to accommodate large delays between the various components of a distributed infrastructure. We experimentally evaluate the performance of our approach on several datasets and show that it outperforms current solutions by several orders of magnitude in distributed settings.
Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

Significance Nonmodel bacteria have essential roles to play in the future development of biotechnology by providing new sources of biocatalysts, antibiotics, hosts for bioproduction, and engineered “living therapies. ” The characterization of such hosts can be challenging, as many are not tractable to standard molecular biology techniques. This paper presents a rapid and automated methodology for characterizing new DNA parts from a nonmodel bacterium using cell-free transcription–translation. Data analysis was performed with Bayesian parameter inference to provide an understanding of gene-expression dynamics and resource sharing. We suggest that our integrated approach is expandable to a whole range of nonmodel bacteria for the characterization of new DNA parts within a native cell-free background for new biotechnology applications. Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host, Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this to B. megaterium to quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.
Self-optimizing voice control user interface

A voice control system converts spoken commands into control actions, a process which is always imperfect due to errors of the speech recognizer. Most speech recognition research is focused on decreasing the recognizers' error rates. In this paper, we present an alternative approach: the development of a strategy that minimizes the number of errors visible on the user interface, given a fixed speech recognizer performance. We introduce a unified view on the recognition act, i.e. the classification of an incoming sound pattern, and derive a self-optimization algorithm for the user interface. With this algorithm, each recognition act is optimized with respect to its own criterion, independently from all other recognition acts. As a result, the voice control system adapts flexibly to the demands of the respective situation.
Reversible brain response to an intragastric load of l-lysine under l-lysine depletion in conscious rats

l-Lysine (Lys) is an essential amino acid and plays an important role in anxiogenic behaviour in both human subjects and rodents. Previous studies have shown the existence of neural plasticity between the Lys-deficient state and the normal state. Lys deficiency causes an increase in noradrenaline release from the hypothalamus and serotonin release from the amygdala in rats. However, no studies have used functional MRI (fMRI) to compare the brain response to ingested Lys in normal, Lys-deficient and Lys-recovered states. Therefore, in the present study, using acclimation training, we performed fMRI on conscious rats to investigate the brain response to an intragastric load of Lys. The brain responses to intragastric administration of Lys (3 mmol/kg body weight) were investigated in six rats intermittently in three states: normal, Lys-deficient and recovered state. First, in the normal state, an intragastric load of Lys activated several brain regions, including the raphe pallidus nucleus, prelimbic cortex and the ventral/lateral orbital cortex. Then, after 6 d of Lys deprivation from the normal state, an intragastric load of Lys activated the ventral tegmental area, raphe pallidus nucleus and hippocampus, as well as several hypothalamic areas. After recovering from the Lys-deficient state, brain activation was similar to that in the normal state. These results indicate that neural plasticity in the prefrontal cortex, hypothalamic area and limbic system is related to the internal Lys state and that this plasticity could have important roles in the control of Lys intake.
Hardware Accelerated Marking for Mark & Sweep Garbage Collection

SUMMARY Many mobile systems need to achieve both high performance and low memory usage, and the total performance of such the systems can be largely a ﬀ ected by the e ﬀ ectiveness of GC. Hence, the recent popularization of mobile devices makes the GC performance play one of the important roles on the wide range of platforms. The response performance degradation caused by suspending all processes for GC has been a well-known potential problem. Therefore, GC algorithms have been actively studied and improved, but they still have not reached any fundamental solution. In this paper, we focus on the point that the same objects are redundantly marked during the GC procedure implemented on DalvikVM, which is one of the famous runtime environments for the mobile devices. Then we propose a hardware support technique for improving marking routine of GC. We installed a set of tables to a processor for managing marked objects, and redundant marking for marked objects can be omitted by re-ferring these tables. The result of the simulation experiment shows that the percentage of redundant marking is reduced by more than 50%.