Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Performance-Centric Bandwidth Allocation with Fairness Tradeoff

Fair bandwidth allocation in datacenter networks has received a substantial amount of research attention, as multiple tenants are hosted by virtual machines in a public cloud. In the context of private datacenters, link bandwidth is shared among applications running data parallel frameworks, such as MapReduce, instead. In this paper, we introduce the rigorous definition of <italic>performance-centric fairness</italic>, with the guiding principle that the performance that data parallel applications will enjoy should be proportional to their weights. We first investigate the problem of maximizing application performance while maintaining strict performance-centric fairness. We then present an inherent tradeoff between fairness and efficiency, which is interpreted from the perspectives of bandwidth utilization and social welfare, respectively. From the first perspective, we propose an algorithm to improve bandwidth utilization by introducing an extended version of fairness. From the second perspective, we formulate an optimization problem of bandwidth allocation that maximizes the social welfare across all the applications, allowing a tunable degree of relaxation on performance-centric fairness. A distributed algorithm is then presented to solve the problem, based on dual based decomposition. With extensive simulations, we demonstrate the effectiveness of our algorithms in improving efficiency and application performance (by up to <inline-formula><tex-math notation="LaTeX"> $1.4X$</tex-math><alternatives><inline-graphic xlink:href="chen-ieq1-2808202.gif"/></alternatives></inline-formula>), with flexible degree of relaxation on the performance-centric fairness.
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing

We investigate distribution testing with access to non-adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a distribution: it submits a query set S to an oracle, which returns a sample from the distribution conditioned on being from S. In the non-adaptive setting, all query sets must be specified in advance of viewing the outcomes.Our main result is the first polylogarithmic-query algorithm for equivalence testing, deciding whether two unknown distributions are equal to or far from each other. This is an exponential improvement over the previous best upper bound, and demonstrates that the complexity of the problem in this model is intermediate to the the complexity of the problem in the standard sampling model and the adaptive conditional sampling model. We also significantly improve the sample complexity for the easier problems of uniformity and identity testing. For the former, our algorithm requires only O(log n) queries, matching the information-theoretic lower bound up to a O(log log n)-factor.Our algorithm works by reducing the problem from e1-testing to e∞-testing, which enjoys a much cheaper sample complexity. Necessitated by the limited power of the non-adaptive model, our algorithm is very simple to state. However, there are significant challenges in the analysis, due to the complex structure of how two arbitrary distributions may differ.
Generating Synthetic Data for Neural Keyword-to-Question Models

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation.
Robust Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions

Robots are being deployed in unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Sophisticated control strategies can guarantee high performance in these changing environments. In this work, we propose a novel robust adaptive model predictive controller that combines robust model predictive control (MPC) with an underlying L1 adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The L1 adaptive controller forces the system to behave close to a specified linear reference model. The controlled system may still deviate from the reference model, but this deviation is shown to be upper bounded. An outer-loop robust MPC uses this upper bound, the linear reference model and system constraints to calculate the optimal reference input that minimizes the given cost function. The proposed robust adaptive MPC is able to achieve high-accuracy trajectory tracking even in the presence of unknown disturbances. We show preliminary experimental results of an adaptive MPC on a quadrotor. The adaptive MPC has a lower trajectory tracking error compared to a predictive, non-adaptive approach, even when wind disturbances are applied.
Supermodular Locality Sensitive Hashes

In this work, we show deep connections between Locality Sensitive Hashability and submodular analysis. We show that the LSHablility of the most commonly analyzed set similarities is in one-to-one correspondance with the supermodularity of these similarities when taken with respect to the symmetric difference of their arguments. We find that the supermodularity of equivalent LSHable similarities can be dependent on the set encoding. While monotonicity and supermodularity does not imply the metric condition necessary for supermodularity, this condition is guaranteed for the more restricted class of supermodular Hamming similarities that we introduce. We show moreover that LSH preserving transformations are also supermodular-preserving, yielding a way to generate families of similarities both LSHable and supermodular. Finally, we show that even the more restricted family of cardinality-based supermodular Hamming similarities presents promising aspects for the study of the link between LSHability and supermodularity. We hope that the several bridges that we introduce between LSHability and supermodularity paves the way to a better understanding both of supermodular analysis and LSHability, notably in the context of large-scale supermodular optimization.
Retrospondyloptosis of the Spine Secondary to Nonaccidental Trauma

Spinal fracture rates from NAT have been reported in <1–3% of spinal injury cases. We present a 13-month-old female who presented with signs of spinal cord injury and was found to have complete retrospondylolisthesis of T12 vertebra and multiple rib fractures in various stages of healing due to NAT. This case reports an extremely severe spinal injury due to NAT of which there are few in the literature and highlights the importance of suspicion of NAT when pediatric patients present with neurologic symptoms and spinal trauma without plausible mechanism of injury.
Mitogen-Activated Protein Kinase Signaling Regulates Proteoglycan Composition of Mast Cell Secretory Granules

Mast cells (MCs) are characterized by an abundance of lysosome-like secretory granules filled with immunomodulatory compounds including histamine, cytokines, lysosomal hydrolases, MC-restricted proteases, and serglycin proteoglycans. The latter are essential for promoting the storage of other granule compounds and are built up of the serglycin core protein to which highly sulfated and thereby negatively charged glycosaminoglycan (GAG) side chains of heparin or chondroitin sulfate type are attached. In the search for mechanisms operating in regulating MC granule homeostasis, we here investigated the role of mitogen-activated protein kinase (MAPK) signaling. We show that inhibition of MEK1/2 (a MAPK kinase) leads to increased metachromatic staining of MC granules, indicative of increased proteoglycan content. Indeed, MEK1/2 inhibition caused a profound increase in the expression of the gene coding for the serglycin core protein and of genes coding for various enzymes involved in the biosynthesis/sulfation of the GAGs attached to the serglycin core protein. This was accompanied by corresponding increases in the levels of the respective GAGs. Deletion of the serglycin core protein abrogated the induction of enzymes operative in proteoglycan synthesis, indicating that availability of the serglycin proteoglycan core protein has a regulatory function impacting on the expression of the various serglycin-modifying enzymes. MEK1/2 inhibition also caused a substantial increase in the expression of granule-localized, proteoglycan-binding proteases. Altogether, this study identifies a novel role for MAPK signaling in regulating the content of secretory granules in MCs.
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map. To make PointSeg applicable on a mobile system, we build the model based on the light-weight network, SqueezeNet, with several improvements. It maintains a good balance between memory cost and prediction performance. Our model is trained on spherical images and label masks projected from the KITTI 3D object detection dataset. Experiments show that PointSeg can achieve competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite compatible for autonomous driving applications.
Symbol Error Rate Analysis of Scheduled STBC With Power Allocation in CR-MIMO Systems

This paper studies symbol error rate (SER) analysis of a user scheduling and space-time block coding (i.e., scheduled STBC) with power allocation using statistical channel state information (CSI) in cognitive radio multiple-input multiple-output (CR-MIMO) systems. The exact SER of the proposed scheme with the statistical CSI is derived for CR-MIMO systems including asymptotic analysis under nonfeedback delay. From analytical results, we observe that the proposed scheduled STBC scheme with the statistical CSI provides the transmit and receive spatial diversity, as well as the multiuser diversity, while the conventional transmit antenna selection (TAS)-based scheme with the statistical CSI only provides the receive spatial diversity. Furthermore, the exact SER of the scheduled STBC with the statistical CSI in CR-MIMO systems is derived under feedback delay between the transmitter and the receiver of the CR system. From the analysis, we find that though the multiuser diversity is diminished by feedback delay, the proposed scheme with feedback delay still provides the transmit and receive spatial diversity, which results in diversity order gain over the conventional TAS-based scheme with nonfeedback delay. Also, the signal-to-noise ratio gain is getting better as the power correlation coefficient and the number of user terminals increase under feedback delay.
Immunocontrolling Graphene Oxide Catalytic Nanogold Reaction and Its Application to SERS Quantitative Analysis

The gold nanoreaction between HAuCl4 and H2O2 is very slow at 50 °C, and the nanoenzyme of graphene oxide (GO) greatly catalyzes the nanoreaction to form gold nanoparticles (AuNPs) with high SERS activity in the presence of Vitoria blue 4R (VB4r) molecular probes, strong resonance Rayleigh scattering (RRS), and surface plasmon resonance (SPR) absorption effect. With the increase of GO, the SERS, RRS, and SPR absorptions were enhanced linearly due to the formation of more AuNPs. The rabit antibody of human chorionic gonadotropin (RHCG) strongly adsorbed on the GO surface to inhibit its catalysis. Upon addition of human chorionic gonadotropin (HCG), the RHCG is separated from the GO surface due to the formation of HCG-RHCG specific immunocomplexes, which led to the recovery of GO catalysis. Using the new strategy of immunocontrolling GO catalysis, three types of resonance methods including SERS, RRS, and surface plasmon resonance (SPR) absorption have been developed for detection of HCG.
Understanding the Twitter usage of humanities and social sciences academic journals

Scholarly communication has the scope to transcend the limitations of the physical world through social media's extended coverage and shortened information paths. Accordingly, publishers have created profiles for their journals in Twitter to promote their publications and to initiate discussions with public. This paper investigates the Twitter presence of humanities and social sciences (HSS) journal titles obtained from mainstream citation indices, by analysing the interaction and communication patterns. This study utilizes webometric data collection, descriptive analysis, and social network analysis. Findings indicate that the presence of HSS journals in Twitter across disciplines is not yet substantial. Sharing of general websites appears to be the key activity performed by HSS journals in Twitter. Among them, web content from news portals and magazines are highly disseminated. Sharing of research articles and retweeting was not majorly observed. Inter‐journal communication is apparent within the same citation index, but it is very minimal with journals from the other index. However, there seems to be an effort to broaden communication beyond the research community, reaching out to connect with the public.
COPD transitions in health and self-management: service users’ experiences from everyday life

Purpose To explore how persons living with COPD experience transitions related to health, self-management, and follow-up from the healthcare services. Patients and methods This study is part of a participatory research project. Six males and five females living with COPD, with a COPD assessment test score of 21–29, participated; all the participants were living at home. Data were collected in qualitative research interviews and analyzed using qualitative content analysis highlighting the participants’ experiences. Results The findings showed two main themes: “The struggle to keep going” and “The need for continuity and competent facilitation”. The participants reported complex health transitions, with changes in roles and function, demanding exacerbations and critical events, and challenges with learning needed self-management. They expressed a great need for and had great benefit from, education, rehabilitation, and follow-up in their management of everyday life. Not all received offers in line with current guidelines. Conclusion In-depth knowledge of patients’ experienced COPD transitions offers clinicians guidance for the timing and quality of follow-up services. Life with COPD entails challenging transitions in health and self-management. Good rehabilitation and follow-up from the healthcare services are needed throughout the disease trajectory. Participation in self-management education and rehabilitation that include psychosocial aspects may facilitate health-enhancing transitions and improve self-management skills. Experienced lack of competence and flexibility among healthcare providers hinders trust and collaboration. Access to stable and competent follow-up in the primary health services may facilitate cohesive services and collaborative self-management.
Web Accessibility Evaluation in a Crowdsourcing-Based System with Expertise-Based Decision Strategy

The rising awareness of accessibility increases the demand for Web accessibility evaluation projects to verify the implementation of Web accessibility guidelines and identify accessibility barriers in websites. However, the complexity of accessibility evaluation tasks and the lack of experts limits their scope and reduces their significance. Due to this complexity, they could not directly rely on a technique called crowdsourcing, which made great contributions in many fields by dividing a problem into many tedious micro-tasks and solving tasks in parallel. Addressing this issue, we develop a new crowdsourcing-based Web accessibility evaluation system with two novel decision strategies, golden set strategy and time-based golden set strategy. These strategies enable the generation of task results with high accuracy synthesized from micro-tasks solved by workers with heterogeneous expertise. An accessibility evaluation of 98 websites by 55 workers with varying experience verifies that our system can complete the evaluation in half the time with a 7.2% improvement on accuracy than the current approach.
Test input generation with Java PathFinder: then and now (invited talk abstract)

The paper Test Input Generation With Java PathFinder was published in the International Symposium on Software Testing and Analysis (ISSTA) 2004 Proceedings, and has now been selected to receive the ISSTA 2018 Retrospective Impact Paper Award. The paper described black-box and white-box techniques for the automated testing of software systems. These techniques were based on model checking and symbolic execution and incorporated in the Java PathFinder analysis tool. The main contribution of the paper was to describe how to perform efficient test input generation for code manipulating complex data that takes into account complex method preconditions and evaluate the techniques for generating high coverage tests. We review the original paper and we discuss the research that preceded it and the research that has happened between then (2004) and now (2018) in the context of the Java PathFinder tool, its symbolic execution component that is now called Symbolic PathFinder, and closely related approaches that target testing of software that manipulates complex data structures. We close with directions for future work.
Harmonium

We introduce Harmonium, a novel ultra wideband (UWB) RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive, lightweight, and FCC-compliant UWB transmitter or tag, fixed infrastructure anchors with known locations, and centralized processing that calculates the tag’s position. Anchors employ a new frequency-stepped narrowband receiver architecture that rejects narrowband interferers and extracts high-resolution timing information without the cost or complexity of traditional UWB approaches. In a complex indoor environment, 90% of position estimates obtained with Harmonium exhibit less than 31 cm of error with an average of 9 cm of inter-sample noise. In non-line-of-sight conditions (i.e., through-wall), 90% of position error is less than 42 cm. The tag draws 75 mW when actively transmitting, or 3.9 mJ per location fix at the 19 Hz update rate. Tags weigh 3 g and cost $4.50 USD at modest volumes. Furthermore, VLSI-based design concepts are identified for a simple, low-power realization of the Harmonium tag to offer a roadmap for the realization of Harmonium concepts in future integrated systems. Harmonium introduces a new design point for indoor localization and enables localization of small, fast objects such as micro quadrotors, devices previously restricted to expensive optical motion capture systems.
Romulus: Efficient Algorithms for Persistent Transactional Memory

Byte addressable persistent memory eliminates the need for serialization and deserialization of data, to and from persistent storage, allowing applications to interact with it through common store and load instructions. In the event of a process or system failure, applications rely on persistent techniques to provide consistent storage of data in non-volatile memory (NVM). For most of these techniques, consistency is ensured through logging of updates, with consequent intensive cache line flushing and persistent fences necessary to guarantee correctness. Undo log based approaches require store interposition and persistence fences before each in-place modification. Redo log based techniques can execute transactions using just two persistence fences, although they require store and load interposition which may incur a performance penalty for large transactions. So far, these techniques have been difficult to integrate with known memory allocators, requiring allocators or garbage collectors specifically designed for NVM. We present Romulus, a user-level library persistent transactional memory (PTM) which provides durable transactions through the usage of twin copies of the data. A transaction in Romulus requires at most four persistence fences, regardless of the transaction size. Romulus uses only store interposition. Any sequential implementation of a memory allocator can be adapted to work with Romulus. Thanks to its lightweight design and low synchronization overhead, Romulus achieves twice the throughput of current state of the art PTMs in update-only workloads, and more than one order of magnitude in read-mostly scenarios.
Glioblastoma in Patients over 70 Years of Age

Abstract Background Glioblastoma has in last 20 years seen the steady increase of incidence, which is most prominent in the group of older patients. These older than 70 years have significantly poorer prognosis than other patients and are considered a distinct group of glioblastoma patients. Modified prognostic factors are being used in these patients and this information is lately supplemented with the genetic and epigenetic information on tumour. The therapy is now often tailored accordingly. The aim of our study was to analyse the current treatment of the glioblastoma patients over 70 years of age to determine the impact of clinical prognostic factors. Patients and methods Among patients treated at the Institute of Oncology Ljubljana between 1997 and 2015, we found that 207 were older than 70 years. We analysed their survival, clinical prognostic factors (age, performance status) treatment modalities (extent of surgery, radiation dose, chemotherapy). Results Median survival of patients older than 70 years was 5.3 months which was statistically significant inferior to the survival of younger patients (p < 0.001). The clinical prognostic factors that influenced survival the most were performance status (p < 0.001), extent of surgical resection (p < 0.001), addition of temozolomide (p < 0.001) and addition of radiotherapy (p = 0.006). Patients receiving concomitant radiochemotherapy with temozolomide followed by adjuvant temozolomide, had same median survival as patients receiving adjuvant temozolomide after completion of radiotherapy. Conclusions The increase of the number of older patients with glioblastoma corresponds to the increase in the life expectancy but in Slovenia also to the increased availability of diagnostic procedures. Clinical prognostic markers are helpful in decision on the aggressiveness of treatment. Radiotherapy and temozolomide have the biggest impact on survival, but the radiotherapy dose seems to be of secondary importance. In selected patients, chemotherapy alone might be sufficient to achieve an optimal effect. Patients that were fitter, had more aggressive surgery, and received temozolomide fared the best. The scheduling of the temozolomide seems to have limited impact on survival as in our study, there was no difference weather patients received temozolomide concomitant with radiotherapy or after the radiotherapy. Thus far, our findings corroborate the usefulness of recursive partitioning analysis (RPA) classes in clinical decisions.