Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network

Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions. However, despite that many work have been proposed, they fail to tackle one major problem: previous methods determine a future transmission rate as a single for value which will be used in an entire time-slot, while real-world network conditions, unlike lab setup, often suffer from rapid and stochastic changes, resulting in the failures of predictions. In this paper, we propose a delay-constrained rate control approach based on end-to-end deep learning. The proposed model predicts future bit rate not as a single value, but as possible bit rate ranges using target delay gradient, with which the transmission delay is guaranteed. We collect a large scale of real-world live streaming data to train our model, and as a result, it automatically learns the correlation between throughput and target delay gradient. We build a testbed to evaluate our approach. Compared with the state-of-the-art methods, our approach demonstrates a better performance in bandwidth utilization. In all considered scenarios, a range based rate control approach outperforms the one without range by 19% to 35% in average QoE improvement.
Rate-Utility Optimized Streaming of Volumetric Media for Augmented Reality

Volumetric media, popularly known as holograms, need to be delivered to users using both on-demand and live streaming, for new augmented reality and virtual reality experiences. As in video streaming, hologram streaming must support network adaptivity and fast startup, but must also handle large bandwidths, multiple simultaneously streaming objects, and frequent user interaction, which requires a low delay. In this paper, we introduce the first system to our knowledge designed specifically for streaming volumetric media. The system reduces bandwidth by introducing 3D tiles and culling them or reducing their level of detail depending on their relation to the user’s view frustum and distance to the user. Our system reduces latency by introducing a window-based buffer, which in contrast to a queue-based buffer allows insertions near the head of the buffer rather than only at the tail of the buffer, to respond quickly to user interaction. To allocate bits between different tiles across multiple objects, we introduce a simple greedy yet provably optimal algorithm for rate-utility optimization. We introduce utility measures based not only on the underlying quality of the representation, but on the level of detail relative to the user’s viewpoint and device resolution. The simulation results show that the proposed algorithm provides a superior quality compared with the existing video-streaming approaches adapted to hologram streaming, in terms of utility and user experience over variable, throughput-constrained networks.
How people protect their privacy on facebook: A cost‐benefit view

Realizing the many benefits from Facebook require users to share information reciprocally, which has overtime created trillions of bytes of information online—a treasure trove for cybercriminals. The sole protection for any user are three sets of privacy protections afforded by Facebook: settings that control information privacy (i.e., security of social media accounts and identity information), accessibility privacy or anonymity (i.e., manage who can connect with a user), and those that control expressive privacy (i.e., control who can see a user's posts and tag you). Using these settings, however, involves a trade‐off between making oneself accessible and thereby vulnerable to potential attacks, or enacting stringent protections that could potentially make someone inaccessible thereby reducing the benefits that are accruable through social media. Using two theoretical frameworks, Uses and Gratifications (U&G) and Protection Motivation Theory (PMT), the research examined how individuals congitvely juxtaposed the cost of maintaining privacy through the use of these settings against the benefits of openness. The application of the U&G framework revealed that social need fulfillment was the single most significant benefit driving privacy management. From the cost standpoint, the PMT framework pointed to perceived severity impacting expressive and information privacy, and perceived susceptability influencing accessibility privacy.
A Location Prediction-Based Helper Selection Scheme for Suspicious Eavesdroppers

This paper aims to improve security performance of data transmission with a mobile eavesdropper in a wireless network. The instantaneous channel state information (CSI) of the mobile eavesdropper is unknown to legitimate users during the communication process. Different from existing work, we intend to reduce power consumption of friendly jamming signals. Motivated by the goal, this work presents a location-based prediction scheme to predict where the eavesdropper will be later and to decide whether a friendly jamming measure should be selected against the eavesdropper. The legitimate users only take the measure when the prediction result shows that there will be a risk during data transmission. According to the proposed method, system power can be saved to a large degree. Particularly, we first derive the expression of the secrecy outage probability and set a secrecy performance target. After providing a Markov mobile model of an eavesdropper, we design a prediction scheme to predict its location, so as to decide whether to employ cooperative jamming or not, and then design a power allocation scheme and a fast suboptimal helper selection method to achieve targeted and efficient cooperative jamming. Finally, numerical simulation results demonstrate the effectiveness of the proposed schemes.
URECA: A Compiler Solution to Manage Uniﬁed Register File for CGRAs

—Coarse-grained reconﬁgurable array (CGRA) is a promising solution to accelerate loops featuring loop-carried dependencies or low trip-counts. One challenge in compiling for CGRAs is to efﬁciently manage both recurring (repeatedly written and read) and nonrecurring (read-only) variables of loops. Although prior works manage recurring variables in rotating register ﬁle (RF), they access the nonrecurring variables through the on-chip memory. It increases memory accesses and degrades the performance. Alternatively, both the variables can be managed in separate rotating and nonrotating RFs. But, it increases code size and effective utilization of the registers becomes challenging. Instead, this paper proposes URECA, a compiler solution to manage the variables in a single nonrotating RF. During mapping loop operations on CGRA, the compiler allocates necessary registers and splits RF in rotating and nonrotating parts. It also pre-loads read-only values in the uniﬁed RF, which are then directly accessed at run-time. Evaluating compute-intensive benchmarks from MiBench show that URECA provides a geomean speedup of 11.41x over sequential loop execution. It improves the loop acceleration through CGRAs by 1.74x at 32% reduced energy consumption over state-of-the-art.
CRAM: Clued Recurrent Attention Model

To overcome the poor scalability of convolutional neural network, recurrent attention model(RAM) selectively choose what and where to look on the image. By directing recurrent attention model how to look the image, RAM can be even more successful in that the given clue narrow down the scope of the possible focus zone. In this perspective, this work proposes clued recurrent attention model (CRAM) which add clue or constraint on the RAM better problem solving. CRAM follows encoder-decoder framework, encoder utilizes recurrent attention model with spatial transformer network and decoder which varies depending on the task. To ensure the performance, CRAM tackles two computer vision task. One is the image classification task, with clue given as the binary image saliency which indicates the approximate location of object. The other is the inpainting task, with clue given as binary mask which indicates the occluded part. In both tasks, CRAM shows better performance than existing methods showing the successful extension of RAM.
1 Gene expression profile predicts outcome after anthracycline-based adjuvant chemotherapy in early breast cancer

Purpose. Prognosis of early beast cancer is heterogeneous. Today, no histo-clinical or biological factor predictive for clinical outcome after adjuvant anthracycline-based chemotherapy (CT) has been validated and introduced in routine use. Using DNA microarrays, we searched for a gene expression signature associated with metastatic relapse after adjuvant anthracycline-based CT without taxane. Methods. We profiled a multicentric series of 595 breast cancers including 498 treated with such adjuvant CT. The identification of the prognostic signature was done using a metagene-based supervised approach in a learning set of 323 patients. The signature was then tested on an independent validation set comprised of 175 similarly treated patients, 128 of them from the PACS01 prospective clinical trial. Results. We identified a 3-metagene predictor of metastatic relapse in the learning set, and confirmed its independent prognostic impact in the validation set. In multivariate analysis, the predictor outperformed the individual current prognostic factors, as well as the Nottingham Prognostic Index-based classifier, both in the learning and the validation sets, and added independent prognostic information. Among the patients treated with adjuvant anthracycline-based CT, with a median follow-up of 68 months, the 5-year metastasis-free survival was 82% in the “good-prognosis” group and 56% in the “poorprognosis” group. Conclusion. Our predictor refines the prediction of metastasis-free survival after adjuvant anthracycline-based CT and might help tailoring adjuvant CT regimens.
The prognostic value of decreased miR-101 in various cancers: a meta-analysis of 12 studies

Background A consensus regarding the prognostic value of decreased miR-101 in human cancers has not been reached. This study aimed to comprehensively investigate the internal associations between loss of miR-101 expression and prognostic implications in patients with cancer. Materials and methods All relevant literature in electronic databases, including PubMed, ISI Web of Science, and Embase, up to March 1, 2017 were searched. Correlations between decreased miR-101 and clinicopathological parameters were defined by odds ratios (ORs). The degree of association between reduced miR-101 and survival outcome was evaluated by pooled hazard ratios (HRs) and relevant 95% CIs. Results Twelve eligible studies with 2,088 patients were included in this meta-analysis. Decreased miR-101 expression was closely connected with poor overall survival, with a pooled HR of 2.15 (95% CI 1.71–2.7, P<0.001). This correlation was also revealed when stratified analysis was conducted with respect to ethnicity, cancer type, sample size, specimen source, and analysis model. However, decreased miR-101 was not associated with disease-free survival, recurrence-free survival, or progression-free survival, with a pooled HR of 1.59 (95% CI 0.83–3.03, P=0.128), despite a positive trend. In addition, reduced miR-101 was intimately related to poorer tumor differentiation (OR 2.17, 95% CI 1.14–4.13; P=0.019), advanced tumor classification (OR 5.25, 95% CI 3.39–8.12; P<0.001), and higher TNM stage (OR 6.18, 95% CI 3.79–10.09; P<0.001). Conclusion Our findings suggest that loss of miR-101 expression is correlated with worse overall survival in a variety of cancers, and could serve as a predictive indicator for clinicopathological features. Furthermore, miR-101 may become a feasible therapeutic target in most human cancers.
The failure of mobile payment: evidence from quasi-experimentations

With the increase use of mobile phone and diffusion into people's everyday life as trusted communication devices, businesses have recently begun exploring their potential as payment devices. While many mobile payments were introduced in different parts of the world, some of them were successful, and others failed to achieve their expected benefits. In this paper we conduct a quasi experiment with 175 potential users in Kuwait in order to shed light on the following two issues: Why do mobile payment services fail? And what do providers need to do to better persuade adopters to use the mobile service?
Analytic Analysis of Narrowband IoT Coverage Enhancement Approaches

The introduction of Narrowband Internet of Things (NB-IoT) as a cellular (NB-IoT) technology aims to support massive Machine-Type Communications applications. These applications are characterized by massive connections from a large number of low-complexity and low-power devices. One of the goals of NB-IoT is to improve coverage extension beyond existing cellular technologies. In order to do that, NB-IoT introduces transmission repetitions and different bandwidth allocation configurations in uplink. These new transmission approaches yield many transmission options in uplink. In this paper, we propose analytical expressions that describe the influence of these new approaches in the transmission. Our analysis is based on the Shannon theorem. The transmission is studied in terms of the required Signal to Noise Ratio, bandwidth utilization, and energy per transmitted bit. Additionally, we propose an uplink link adaptation algorithm that contemplates these new transmission approaches. The conducted evaluation summarizes the influence of these approaches. Furthermore, we present the resulting uplink link adaptation from our proposed algorithm sweeping the device's coverage.
AURALISATION OF DEEP CONVOLUTIONAL NEURAL NETWORKS: LISTENING TO LEARNED FEATURES

Deep learning has been actively adopted in the ﬁeld of music information retrieval, e.g. genre classiﬁcation, mood detection, and chord recognition. Deep convolutional neural networks (CNNs), one of the most popular deep learning approach, also have been used for these tasks. However, the process of learning and prediction is little understood, particularly when it is applied to spectrograms. We introduce auralisation of CNNs to understand its underlying mechanism.
Enhanced Transmit-Antenna Selection Schemes for Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems are a core technology designed to achieve the performance objectives defined for 5G wireless communications. They achieve high spectral efficiency, reliability, and diversity gain. However, the many radio frequency chains required in base stations equipped with a high number of transmit antennas imply high hardware costs and computational complexity. Therefore, in this paper, we investigate the use of a transmit-antenna selection scheme, with which the number of required radio frequency chains in the base station can be reduced. This paper proposes two efficient transmit-antenna selection (TAS) schemes designed to consider a trade-off between performance and computational complexity in massive MIMO systems. The spectral efficiency and computational complexity of the proposed schemes are analyzed and compared with existing TAS schemes, showing that the proposed algorithms increase the TAS performance and can be used in practical systems. Additionally, the obtained results enable a better understanding of how TAS affects massive MIMO systems.
High resolution forecast models of water vapour over mountains : comparison of results from the

Propagation delay due to variable tropospheric water vapor (WV) is one of the most intractable problems for radar interferometry, particularly over mountains. The WV field can be simulated by an atmospheric model, and the difference between the two fields is used to correct the radar interferogram. Here, we report our use of the U.K. Met Office Unified Model in a nested mode to produce high-resolution forecast fields for the 3-km-high Mount Etna volcano. The simulated precipitablewater field is validated against that retrieved from the MediumResolution Imaging Spectrometer (MERIS) radiometer on the Envisat satellite, which has a resolution of 300 m. Two case studies, one from winter (November 24, 2004) and one from summer (June 25, 2005), show that the mismatch between the model and the MERIS fields (rms = 1.1 and 1.6 mm, respectively) is small. One of the main potential sources of error in the models is the timing of the WV field simulation. We show that long-wavelength upper tropospheric troughs of low WV could be identified in both the model output and Meteosat WV imagery for the November 24, 2004 case and used to choose the best time of model output.
A simple antegrade perfusion method for isolating viable single cardiomyocytes from neonatal to aged mice

The aim of this study was to establish a simple and reproducible antegrade perfusion method for isolating single viable mouse heart cells and to determine the standard practical protocols that are appropriate for mice of various ages. Antegrade perfusion was performed by injecting perfusate from near the apex of the left ventricle of the excised heart, the aorta of which was clamped, using an infusion pump. This could thoroughly perfuse the myocardium through the coronary circulation. All procedures were carried out on a prewarmed heater mat under a microscope, which allows for the processes of injection and perfusion to be monitored. With appropriate adjustment of the size of the injection needle, the composition and amount of enzyme solution and the perfusion flow rate, this antegrade perfusion method could be applied to the hearts of neonatal to aged mice. We examined the morphological characteristics and electrophysiological properties of the isolated ventricular and atrial myocytes and found that these cells were mostly identical to those obtained with the traditional Langendorff‐based retrograde perfusion method. Interstitial nonmyocytes, such as cardiac progenitor cells, were also isolated simultaneously from the supernatant fraction of the centrifugation, similar to the retrograde perfusion method. The results suggest that single heart cells can be well isolated with high degree of quality by the present antegrade perfusion method, regardless of the age of the mouse.
Electric-Jet Assisted Layer-by-Layer Deposition of Gold Nanoparticles to Prepare Conducting Tracks

A suspension of 15nm diameter gold nanoparti-cles has been deposited along a line on a silicon substrate with the assistance of a jet generated in an electric field. In order to control the evaporation of the solvent used to suspend the gold nanoparticles, a heating device was used to change the substrate temperature. Layer-by- layer deposition enabled the direct writing of gold tracks having an electrical resistivity of 1.8 × 10-7 Ωm, only about an order of magnitude above the electrical resistivity of bulk gold.
WhoTracks.Me: Monitoring the online tracking landscape at scale

We present the largest and longest measurement of online tracking to date based on real users. The data, which is made publicly available, is generated from more than 780 million page loads over the course of the last 10 months. Previous attempts to measure the tracking ecosystem, are done via measurement platforms that do not interact with websites the same way a user does. We instrument a crowd-sourced measurement of third-parties across the web via users who consent to data collection via a browser extension. The collection is done with privacy-by-design in mind, and introduces no privacy side effects. This approach overcomes limitations of previous work by collecting real web usage across multiple countries, ISP and browser configurations, and on difficult to crawl pages, such as those behind logins, giving a more accurate portrayal of the online-tracking ecosystem. The data, which we plan to continue contributing to and maintain in the future, and WhoTracks.Me website - the living representation of the data, are available for researchers, regulators, journalists, web developers and users to detect tracking behaviours, analyse the tracking landscape, develop efficient tools, devise policies and raise awareness of the negative externalities tracking introduces. We believe this work provides the transparency needed to shine a light on a very opaque industry.
Innovative real-time energy management by using portfolio algorithms

Photovoltaic panels represent an intermittent way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy needs to be stored, for example by using a battery. In this work, an energy model for buildings with battery is developed, based on electrical consumption and production data. This model takes into account the depth of discharge, state of charge and efficiency over a cycle of a lithium type battery. Three rule-based strategies are then described. This leads to our optimization problem and proposed algorithms. Then an innovative real-time energy management strategy is proposed: for each switch time, the best algorithm is automatically selected from a portfolio of algorithms and maintained along the switch period. Real-time simulation conducted over one year have shown the efficiency of the approach.
Mechanism of Shiga Toxin Clustering on Membranes

The bacterial Shiga toxin interacts with its cellular receptor, the glycosphingolipid globotriaosylceramide (Gb3 or CD77), as a first step to entering target cells. Previous studies have shown that toxin molecules cluster on the plasma membrane, despite the apparent lack of direct interactions between them. The precise mechanism by which this clustering occurs remains poorly defined. Here, we used vesicle and cell systems and computer simulations to show that line tension due to curvature, height, or compositional mismatch, and lipid or solvent depletion cannot drive the clustering of Shiga toxin molecules. By contrast, in coarse-grained computer simulations, a correlation was found between clustering and toxin nanoparticle-driven suppression of membrane fluctuations, and experimentally we observed that clustering required the toxin molecules to be tightly bound to the membrane surface. The most likely interpretation of these findings is that a membrane fluctuation-induced force generates an effective attraction between toxin molecules. Such force would be of similar strength to the electrostatic force at separations around 1 nm, remain strong at distances up to the size of toxin molecules (several nanometers), and persist even beyond. This force is predicted to operate between manufactured nanoparticles providing they are sufficiently rigid and tightly bound to the plasma membrane, thereby suggesting a route for the targeting of nanoparticles to cells for biomedical applications.