Miracle-dz/UniFAR-FTUs huggingface dataset:
The Interrelatedness of Gender-Stereotypical Interest Profiles and Students’ Gender-Role Orientation, Gender, and Reasoning Abilities

This study investigates early secondary school students’ gender-stereotypical interest profiles and how they relate to students’ gender-role orientation, i.e., their traditional or egalitarian attitudes toward gender roles. Gender-stereotypical interest profiles are described by relatively high interests in either female- or male-stereotypical domains and low interests in domains that are not associated to the own gender group. In a study conducted with 4,457 students (49.2% female, sixth graders) with data from the German National Educational Panel Study1, four interest profiles were derived from the combined latent profile analysis of two academic interest domains (mathematics and German) and six vocational interest domains (realistic, investigative, artistic, social, enterprising, and conventional). Aside from two gender-stereotypical interest profiles, two gender-undifferentiated interest profiles were found. One undifferentiated interest profile was marked by generally high interests in all domains, the other by generally low interests in all domains. Students in the male-stereotypical interest profile had high values in the mathematics, realistic, investigative, and enterprising domains and low interest in the German, artistic, social, and conventional domains. The female-stereotypical interest profile was marked by the opposite pattern. The results further showed that students more likely belonged to the high or female interest profiles when they expressed egalitarian gender-role orientations. Also, boys were more likely members of the female interest profile than were girls of the male interest profile. Students with low reasoning skills were generally more likely members of the low interest profile group. Results are discussed with respect to the question whether interest profiles are more predictive of students’ academic development than single domain-specific measures of interest.
Pin1 Is Regulated by CaMKII Activation in Glutamate-Induced Retinal Neuronal Regulated Necrosis

In our previous study, we reported that peptidyl-prolyl isomerase 1 (Pin1)-modulated regulated necrosis (RN) occurred in cultured retinal neurons after glutamate injury. In the current study, we investigated the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in Pin1-modulated RN in cultured rat retinal neurons, and in an animal in vivo model. We first demonstrated that glutamate might lead to calcium overloading mainly through ionotropic glutamate receptors activation. Furthermore, CaMKII activation induced by overloaded calcium leads to Pin1 activation and subsequent RN. Inactivation of CaMKII by KN-93 (KN, i.e., a specific CaMKII inhibitor) application can decrease the glutamate-induced retinal neuronal RN. Finally, by using an animal in vivo model, we also demonstrated the important role of CaMKII in glutamate-induced RN in rat retina. In addition, flash electroretinogram results provided evidence that the impaired visual function induced by glutamate can recover after CaMKII inhibition. In conclusion, CaMKII is an up-regulator of Pin1 and responsible for the RN induced by glutamate. This study provides further understanding of the regulatory pathway of RN and is a complementary mechanism for Pin1 activation mediated necrosis. This finding will provide a potential target to protect neurons from necrosis in neurodegenerative diseases, such as glaucoma, diabetic retinopathy, and even central nervous system diseases.
Cell-Based Therapies for Stroke: Are We There Yet?

Stroke is the second leading cause of death and physical disability, with a global lifetime incidence rate of 1 in 6. Currently, the only FDA approved treatment for ischemic stroke is the administration of tissue plasminogen activator (tPA). Stem cell clinical trials for stroke have been underway for close to two decades, with data suggesting that cell therapies are safe, feasible, and potentially efficacious. However, clinical trials for stroke account for <1% of all stem cell trials. Nevertheless, the resources devoted to clinical research to identify new treatments for stroke is still significant (53–64 million US$, Phase 1–4). Notably, a quarter of cell therapy clinical trials for stroke have been withdrawn (15.2%) or terminated (6.8%) to date. This review discusses the bottlenecks in delivering a successful cell therapy for stroke, and the cost-to-benefit ratio necessary to justify these expensive trials. Further, this review will critically assess the currently available data from completed stroke trials, the importance of standardization in outcome reporting, and the role of industry-led research in the development of cell therapies for stroke.
CUED@WMT19:EWC&LMs

Two techniques provide the fabric of the Cambridge University Engineering Department’s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by finetuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned ngram LM.
Queue and Loss Distributions in Finite-Buffer Queues

We derive simple bounds on the queue distribution in finite-buffer queues with Markovian arrivals. The bounds capture a truncated exponential behavior, involving joint horizontal and vertical shifts of an exponential function; this is fundamentally different than existing results capturing horizontal shifts only. We also obtain similar bounds on the loss distribution, which is a key metric to understand the impact of finite-buffer queues on real-time applications. Simulations show that the bounds are accurate in heavy-traffic regimes, and improve existing ones by orders of magnitude.
Enhancing the crowdsourced live streaming: a deep reinforcement learning approach

With the growing demand for crowdsourced live streaming (CLS), how to schedule the large-scale dynamic viewers effectively among different Content Delivery Network (CDN) providers has become one of the most significant challenges for CLS platforms. Although abundant algorithms have been proposed in recent years, they suffer from a critical limitation: due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we propose LTS (Learn to schedule), a deep reinforcement learning (DRL) based scheduling approach that can dynamically adapt to the variation of both viewer traffics and CDN performance. After the extensive evaluation the real data from a leading CLS platform in China, we demonstrate that LTS improves the average quality of experience (QoE) over state-of-the-art approach by 8.71%-15.63%.
Mesh HDR WPAN resource allocation optimization approaches

In the multihop IEEE 802.15. 5 networks, all the devices compete to the resources of a shared superframe. In order to distribute these resources in a fair and satisfactory manner among the competing devices, we propose a distributed optimization framework for resource allocation scheme in an IEEE 802.15. 5 hop-1. For this purpose, we introduce in this paper a suite of optimization problems for the hop-1 IEEE 802.15. 5 resource allocation to optimize fairness and satisfaction without exceeding the superframe size and respecting the demanded channel time size sent by the requesting devices. Simulation results studied and compared the satisfaction factor and fairness index of the different proposed optimization problems. Consequently, a trade-off between satisfaction and fairness should be conducted for choosing the optimal solution.
A Structural Result for Personalized PageRank and its Algorithmic Consequences

Many systems, such as the Internet, social networks, and the power grid, can be represented as graphs. When analyzing graphs, it is often useful to compute scores describing the relative importance or distance between nodes. One example is Personalized PageRank (PPR), which assigns to each node v a vector whose i-th entry describes the importance of the i-th node from the perspective of v. PPR has proven useful in many applications, such as recommending who users should follow on social networks (if this i-th entry is large, v may be interested in following the i-th user). Unfortunately, computing n PPR vectors exactly for a graph of n nodes has complexity O(n3), which is infeasible for many graphs of interest. In this work, we devise a scheme to estimate all n PPR vectors with bounded l_1 error and complexity O(nc), where c<2 depends on the degrees of the graph at hand, the desired error tolerance, and a parameter that defines PPR. This improves upon existing methods, the best of which have complexity O(n2 log n) in our setting. Our complexity guarantee holds with high probability, for certain choices of the PPR parameter, and for a certain class of random graphs (roughly speaking, the sparse directed configuration model with heavy-tailed in-degrees); our accuracy guarantee holds with probability 1 and for arbitrary graphs and PPR parameters. The complexity result arises as a consequence of our main (structural) result, which shows that the dimensionality of the set of PPR vectors scales sublinearly in n with high probability, for the same class of random graphs and for a notion of dimensionality similar to matrix rank. It is this coupling of the PPR vectors for the nodes on a common underlying graph that allows for estimating them faster. Hence, at a high level, our scheme is analogous to (but distinct from) low-rank matrix approximation. We also note that our scheme is similar to one that was proposed in [Jeh and Widom 2003] but lacked accuracy and complexity guarantees, so another contribution of our paper is to address this gap in the literature.
Playing with delay: an interactive VR demonstration

Virtual reality is now used across a range of applications, from entertainment to clinical purposes. Although the rendered visualisations have better temporal and spatial resolutions than ever, several technological constraints remain - and people still suffer side-effects. With this demonstration, we address the temporal constraints of virtual reality. We invite participants to play a game where they are in charge of the presented delay, facilitating their first-hand experience of the consequences of delay. Furthermore, the demonstration serves as a platform for future explorations into short- and long-term effects of virtual reality constraints.
Understanding the Networking Performance of Wear OS

Networking on wearable devices such as smartwatches is becoming increasingly important as fueled by new hardware, OS support, and applications. In this work, we conduct a first in-depth investigation of the networking performance of Wear OS, one of the most popular OSes for wearables. Through carefully designed controlled experiments conducted in a cross-device, cross-protocol, and cross-layer manner, we identify serious performance issues of Wear OS regarding key aspects that distinguish wearable networking from smartphone networking: Bluetooth (BT) performance, smartphone proxying, network interface selection, and BT-WiFi handover. We pinpoint their root causes and quantify their impacts on network performance and application QoE. We further propose practical suggestions to improve wearable networking performance.
Two Extensions of Kingman's GI/G/1 Bound

A simple bound in GI/G/1 queues was obtained by Kingman using a discrete martingale transform. We extend this technique to 1) multiclass ΣGI/G/1 queues and 2) Markov Additive Processes (MAPs) whose background processes can be time-inhomogeneous or have an uncountable state-space. Both extensions are facilitated by a necessary and sufficient ordinary differential equation (ODE) condition for MAPs to admit continuous martingale transforms. Simulations show that the bounds on waiting time distributions are almost exact in heavy-traffic, including the cases of 1) heterogeneous input, e.g., mixing Weibull and Erlang-k classes and 2) Generalized Markovian Arrival Processes, a new class extending the Batch Markovian Arrival Processes to continuous batch sizes.
Inferring Catchment in Internet Routing

BGP is the de-facto Internet routing protocol for interconnecting Autonomous Systems (AS). Each AS selects its preferred routes based on its routing policies, which are typically not disclosed. Due to the distributed route selection and information hiding, answering questions such as "what is the expected catchment of the anycast sites of a content provider at the AS-level, if new sites are deployed? ", or "how will load-balancing behave if an ISP changes its routing policy for a prefix? ", is a hard challenge. In this work, we propose a framework and methodology to infer the routing behavior in existing or hypothetical routing configurations, and provide new capabilities and insights for informative route inference (e.g., isolating the effect of randomness that is present in prior simulation-based approaches). The proposed framework can be useful in a number of applications: measurements/monitoring, traffic engineering, network planning, Internet routing models, etc.
On the first JND and break in presence of 360-degree content: an exploratory study

Unlike traditional planar 2D visual content, immersive 360-degree images and videos undergo particular processing steps and are intended to be consumed via head-mounted displays (HMDs). To get a deeper understanding on the perception of 360-degree visual distortions when consumed through HMDs, we perform an exploratory task-based subjective study in which we have asked subjects to define the first noticeable difference and break-in-presence points when incrementally adding specific compression artifacts. The results of our study: give insights on the range of allowed visual distortions for 360-degree content; show that the added visual distortions are more tolerable in mono than in stereoscopic 3D; and identify issues with current 360-degree objective quality metrics.
GAugur: Quantifying Performance Interference of Colocated Games for Improving Resource Utilization in Cloud Gaming

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. To enable efficient colocations while providing Quality of Service (QoS) guarantees, a precise quantification of performance interference among colocated games is required. However, achieving such precise interference prediction is very challenging for games due to the complexity introduced by the contention on many shared resources across CPU and GPU. Moreover, the distinctive properties of cloud gaming require that the prediction model should be constructed beforehand and the prediction should be made instantaneously at request arrivals, which further increases the difficulty. The existing solutions are either not applicable or not effective due to many limitations. In this paper, we present GAugur, a novel methodology that enables highly accurate prediction of the performance interference among games arbitrarily colocated. By leveraging machine learning technologies, GAugur is able to capture the complex relationship between the interference and the contention features of colocated games. We evaluate GAugur through extensive experiments using a large number of real popular games. The results show that GAugur is able to identify whether a colocated game satisfies QoS requirement within an average error of 5%, and is able to quantify the performance degradation of a colocated game within an average error of 7.9%, which significantly outperforms the alternatives. Moreover, GAugur incurs an offline profiling cost linear to the number of games, and negligible overhead for online prediction. We apply GAugur to guiding efficient game colocations for cloud gaming. Experimental results show that GAugur is able to increase the resource utilization by 20% to 60%, and improve the overall performance by up to 15%, compared to the state-of-the-art solutions.
A Distributed Algorithm to Calculate Max-Min Fair Rates Without Per-Flow State

Most congestion control algorithms, like TCP, rely on a reactive control system that detects congestion, then marches carefully towards a desired operating point (e.g. by modifying the window size or adjusting a rate). In an effort to balance stability and convergence speed, they often take hundreds of RTTs to converge; an increasing problem as networks get faster, with less time to react. This paper is about an alternative class of congestion control algorithms based on proactive-scheduling: switches and NICs "pro-actively" exchange control messages to run a distributed algorithm to pick "explicit rates" for each flow. We call these Proactive Explicit Rate Control (PERC) algorithms. They take as input the routing matrix and link speeds, but not a congestion signal. By exploiting information such as the number of flows at a link, they can converge an order of magnitude faster than reactive algorithms. Our main contributions are (1) s-PERC ("stateless" PERC), a new practical distributed PERC algorithm without per-flow state at the switches, and (2) a proof that s-PERC computes exact max-min fair rates in a known bounded time, the first such algorithm to do so without per-flow state. To analyze s-PERC, we introduce a parallel variant of standard waterfilling, 2-Waterfilling. We prove that s-PERC converges to max-min fair in 6N rounds, where N is the number of iterations 2-Waterfilling takes for the same routing matrix. We describe how to make s-PERC practical and robust to deploy in real networks. We confirm using realistic simulations and an FPGA hardware testbed that s-PERC converges 10-100x faster than reactive algorithms like TCP, DCTCP and RCP in data-center networks and 1.3--6x faster in wide-area networks (WANs). Long flows complete in close to the ideal time, while short-lived flows are prioritized, making it appropriate for data-centers and WANs.
Evaluation of Positron Emission Tomography and Contrast-Enhanced Computed Tomography Scan in Nodal Staging of Early Operable Uterine Cancers

Introduction: In early operable stages of cervical an endometrial malignancies, surgical staging of lymph nodes is advocated as contrast-enhanced computed tomography (CECT) has limited sensitivity and accuracy. Although fluorine-18 (F-18) fluorodeoxyglucose (FDG)/positron emission tomography (PET)-CT has potential to identify subcentimeter-sized nodal metastases, higher prevalence of pelvic inflammatory disease in developing countries could result in lower accuracy. The present study was undertaken to assess the incremental value of PET scan over CECT for nodal staging before radical surgery. Methods: Forty-four patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA2–IIb carcinoma cervix and 28 patients of FIGO Stage I–II carcinoma endometrium underwent F-18 FDG-PET-CECT scan. A SUVmaxvalue >2.5 g/ml based on body weight was considered as positive. An enhancing node with >1 cm size in the shortest dimension, with loss of fatty hilum was considered positive on CT images. The histological findings were considered the gold standard against which the two modalities were compared. Results: All 1226 pelvic nodes were dissected, of which 65 were found to be metastatic (i.e., 5.3%). Of the 72 patients, 15 (20.83%) had pelvic nodal metastases. The overall accuracy of PET and CECT for assessment of pelvic nodal metastases was comparable (i.e., 86% vs. 85%). Conclusion: PET and CECT scans have similar accuracy in pelvic nodal staging of operable uterine malignancies. Granulomatous inflammation may not be a major cause of false-positive results. The sensitivity and negative predictive values are not high enough to obviate need of surgical nodal staging.
Wireless Device-to-Device Communications and Networks

Covering the fundamental theory together with the state-of-the-art in research and development, this practical guide provides the techniques needed to design, analyze, and optimize device-to-device (D2D) communications in wireless networking. With an ever-increasing demand for higher data rate wireless access, D2D communication is set to become a key feature supported by next generation cellular networks. This book introduces D2D-based wireless communications from the physical, MAC, network, and application layer perspectives, providing all the key background information before moving on to discuss real-world applications as well as potential future developments. Key topics are discussed in detail, such as dynamic resource sharing (for example of spectrum and power) between cellular and ad hoc D2D communications to accommodate larger volumes of traffic and provide better service to users. Readers will understand the practical challenges of resource management, optimization, security, standardization, and network topology, and learn how the design principles are applied in practice.
Gastric Emptying Scintigraphy: Beyond Numbers – An Observational Study to Differentiate between Various Etiologies and a Step toward Personalized Management

Aim: Gastric emptying (GE) scintigraphy is commonly used as a standard diagnostic procedure for the assessment of functional dyspepsia (FD). Results of the study are often reported as either normal or delayed GE times. The aim of this study was to recognize various patterns of scintigraphy among both normal and abnormal emptying times. Materials and Methods: Fifty patients with suspected FD were included in the study. GE study was performed with a standardized vegetarian solid meal. Results: Out of 50 patients, 33 patients had deranged GE. Thirty patients had delayed GE. Three patients demonstrated gastric hurrying. Five different patterns were demonstrated in patients having similar emptying and retention times such as reduced fundus compliance, decreased fundic accommodation, antral dysmotility, gastric hurrying, and gastroesophageal reflux. Conclusion: According to our findings, it may be suggested that visual assessment of GE and identification of various pattern is a very important aspect of the GE study. It not only subcategorized patients but also decreases the number of “normal” studies. This finding may have an impact on patient management in the era of personalized medicine.