Miracle-dz/UniFAR-FTUs huggingface dataset:
A Privacy-preserving Method to Optimize Distributed Resource Allocation

We consider a resource allocation problem involving a large number of agents with individual constraints subject to privacy, and a central operator whose objective is to optimize a global, possibly nonconvex, cost while satisfying the agents' constraints, for instance an energy operator in charge of the management of energy consumption flexibilities of many individual consumers. We provide a privacy-preserving algorithm that does compute the optimal allocation of resources, avoiding each agent to reveal her private information (constraints and individual solution profile) neither to the central operator nor to a third party. Our method relies on an aggregation procedure: we compute iteratively a global allocation of resources, and gradually ensure existence of a disaggregation, that is individual profiles satisfying agents' private constraints, by a protocol involving the generation of polyhedral cuts and secure multiparty computations (SMC). To obtain these cuts, we use an alternate projection method, which is implemented locally by each agent, preserving her privacy needs. We adress especially the case in which the local and global constraints define a transportation polytope. Then, we provide theoretical convergence estimates together with numerical results, showing that the algorithm can be effectively used to solve the allocation problem in high dimension, while addressing privacy issues.
Received Signal Strength Based Wireless Source Localization with Inexact Anchor Position

Received signal strength(RSS)-based approach of wireless localization is easy to implement at low cost. In practice, exact positions of anchors may not be available. This paper focuses on determining the location of the source in the presence of inexact position of anchors based on RSS directly. This study at first uses Taylor expansion and a min-max approach to get the approximate maximum likelihood estimator of the source coordinates. Then this paper proposes a relaxed semi-definite programming model to circumvent the non-convexity. This paper also proposes a rounding algorithm concerning both the inexact source location and the inaccurate anchor location. Experimental results together with analysis are presented to validate the proposed method
Mart: a mutant generation tool for LLVM

Program mutation makes small syntactic alterations to programs' code in order to artificially create faulty programs (mutants). Mutants creation (generation) tools are often characterized by their mutation operators and the way they create and represent the mutants. This paper presents Mart, a mutants generation tool, for LLVM bitcode, that supports the fine-grained definition of mutation operators (as matching rule - replacing pattern pair; uses 816 defined pairs by default) and the restriction of the code parts to mutate. New operators are implemented in Mart by implementing their matching rules and replacing patterns. Mart also implements in-memory Trivial Compiler Equivalence to eliminate equivalent and duplicate mutants during mutants generation. Mart generates mutant code as separated mutant files, meta-mutants file, weak mutation and mutant coverage instrumented files. Mart is publicly available (https://github.com/thierry-tct/mart). Mart has been applied to generate mutants for several research experiments and generated more than 4,000,000 mutants.
Disturbance Estimation and Rejection for High-Precision Multirotor Position Control

Many multirotor Unmanned Aerial Systems applications have a critical need for precise position control in environments with strong dynamic external disturbances such as wind gusts or ground and wall effects. Moreover, to maximize flight time, small multirotor platforms have to operate within strict constraints on payload and thus computational performance. In this paper, we present the design and experimental comparison of Model Predictive and PID multirotor position controllers augmented with a disturbance estimator to reject strong wind gusts up to 12 m/s and ground effect. For disturbance estimation, we compare Extended and Unscented Kalman filtering. In extensive in- and outdoor flight tests, we evaluate the suitability of the developed control and estimation algorithms to run on a computationally constrained platform. This allows to draw a conclusion on whether potential performance improvements justify the increased computational complexity of MPC for multirotor position control and UKF for disturbance estimation. SUPPLEMENTARY MATERIAL Video: https://youtu.be/P0XrPHUCowY
Pushpak: Voice Command-based eBook Navigator

Effective utilization of screen reading software requires a user to remember various keystrokes/gestures. For beginners, this requirement increases the learning curve. This is even more challenging on touch screen devices due to limited possible gestures and lack of semantic intuition. In this project, we have implemented a voice command-based eBook navigator to address the challenge of the heavy learning curve, as voice command provides flexibility through the intent understanding module.
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization

We consider a distributed multi-agent policy evaluation problem in reinforcement learning. In our setup, a group of agents with jointly observed states and private local actions and rewards collaborates to learn the value function of a given policy. When the dimension of state-action space is large, the temporal-difference learning with linear function approximation is widely used. Under the assumption that the samples are i.i. d. , the best-known convergence rate for multi-agent temporal-difference learning is $O(1/\sqrt{T})$ minimizing the mean square projected Bellman error. In this paper, we formulate the temporal-difference learning as a distributed stochastic saddle point problem, and propose a new homotopy primal-dual algorithm by adaptively restarting the gradient update from the average of previous iterations. We prove that our algorithm enjoys an $O(1/T)$ convergence rate up to logarithmic factors of $T$, thereby significantly improving the previously-known convergence results on multi-agent temporal-difference learning. Furthermore, since our result explicitly takes into account the Markovian nature of the sampling in policy evaluation, it addresses a broader class of problems than the commonly used i.i. d. sampling scenario. From a stochastic optimization perspective, to the best of our knowledge, the proposed homotopy primal-dual algorithm is the first to achieve $O(1/T)$ convergence rate for distributed stochastic saddle point problem.
EdgeNet: Semantic Scene Completion from RGB-D images

Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Completion from RGB-D data used either only depth or depth with colour by projecting the 2D image into the 3D volume resulting in a sparse data representation. In this work, we present a new strategy to encode colour information in 3D space using edge detection and flipped truncated signed distance. We also present EdgeNet, a new end-to-end neural network architecture capable of handling features generated from the fusion of depth and edge information. Experimental results show improvement of 6.9% over the state-of-the-art result on real data, for end-to-end approaches.
Scalable Cell-Free Massive MIMO Systems

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio (MR) processing and also performs closely to the best unscalable alternatives.
Multi-client Secure Encrypted Search Using Searching Adversarial Networks

With the rapid development of cloud computing, searchable encryption for multiple data owners model (multi-owner model) draws much attention as it enables data users to perform searches on encrypted cloud data outsourced by multiple data owners. However, there are still some issues yet to be solved nowadays, such as precise query, fast query, “curse of dimensionality” and flexible system dynamic maintenance. To target these issues, this paper proposes a secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks (MRSM SAN). Specifically, we exploit searching adversarial networks to achieve optimal pseudokeyword padding, and obtains the optimal game equilibrium for query precision and privacy protection strength. In order to achieve fast query, maximum likelihood search balanced tree is proposed, which brings the query complexity closer to O(logN). We reduce data dimension with fast index clustering, and enable low-overhead system maintenance based on balanced index forest. In addition, attribute based encryption is used to achieve more secure and convenient key management as well as authorized access control. Compared with previous work, our solution maintains query precision above 95% while ensuring adequate privacy protection, significantly improving search efficiency, enabling more flexible system dynamic maintenance, and reducing the overhead on computation and storage.
The review linkage graph for code review analytics: a recovery approach and empirical study

Modern Code Review (MCR) is a pillar of contemporary quality assurance approaches, where developers discuss and improve code changes prior to integration. Since review interactions (e.g., comments, revisions) are archived, analytics approaches like reviewer recommendation and review outcome prediction have been proposed to support the MCR process. These approaches assume that reviews evolve and are adjudicated independently; yet in practice, reviews can be interdependent. In this paper, we set out to better understand the impact of review linkage on code review analytics. To do so, we extract review linkage graphs where nodes represent reviews, while edges represent recovered links between reviews. Through a quantitative analysis of six software communities, we observe that (a) linked reviews occur regularly, with linked review rates of 25% in OpenStack, 17% in Chromium, and 3%–8% in Android, Qt, Eclipse, and Libreoffice; and (b) linkage has become more prevalent over time. Through qualitative analysis, we discover that links span 16 types that belong to five categories. To automate link category recovery, we train classifiers to label links according to the surrounding document content. Those classifiers achieve F1-scores of 0.71–0.79, at least doubling the F1-scores of a ZeroR baseline. Finally, we show that the F1-scores of reviewer recommenders can be improved by 37%–88% (5–14 percentage points) by incorporating information from linked reviews that is available at prediction time. Indeed, review linkage should be exploited by future code review analytics.
What can natural edges of gallery forests teach us about woody community performance in sharp ecotones?

Aims The diversity and structure of forest edges are a central issue in ecology. However, most studies have focused on anthropogenic forest edges, being natural ones least understood. We studied the communities of shrubs and trees in natural edges of gallery forests with the main goal of learning what are the main factors that shape the structure and diversity of these natural sharp ecotones. Methods We evaluated 10 gallery forest sites, allocating in each of them three 15 × 20 m plots. The plots were laid out in relatively rectilinear stretches of forest edges, respecting a minimal distance of 10 m between each plot. As they are permanent plots and meant to study eventual fluctuations in the forest–grasslands limits, we allocated the plots with their longest side parallel to the forest edge and covering perpendicularly 5 m of the grassland and 10 m of forest. Inside the plots we identified, mapped and measured all shrub and tree individuals with diameter at breast height (DBH) ≥ 1 cm. Important Findings Although many differences exist among the communities in terms of density and basal area, all of them were highly dense and had relatively low basal areas. In addition, both the number of individuals and the basal area increased rapidly towards the forest interior. Our results also revealed that the edge communities are composed mainly by small individuals that demand a great amount of light. Both the vertical stratification and the proportion of the functional groups are similar among the sites yet change quickly in the first 10 m of the forest edge. However, the floristic similarities were low among the communities mainly due to the species turnover within each area. Therefore, although the overall structure is constant in the studied edges, the low floristic similarity among them possibly indicates that the species within the same functional groups are substituting each other across the sites. Despite older and seemingly stable, our results showed the gallery forest edges are quite similar to the anthropogenic edges or recent clearings. This points out that, independent of the age, those characteristics are possibly permanent in edges.
A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme.
Xylitol’s Health Benefits beyond Dental Health: A Comprehensive Review

Xylitol has been widely documented to have dental health benefits, such as reducing the risk for dental caries. Here we report on other health benefits that have been investigated for xylitol. In skin, xylitol has been reported to improve barrier function and suppress the growth of potential skin pathogens. As a non-digestible carbohydrate, xylitol enters the colon where it is fermented by members of the colonic microbiota; species of the genus Anaerostipes have been reported to ferment xylitol and produce butyrate. The most common Lactobacillus and Bifidobacterium species do not appear to be able to grow on xylitol. The non-digestible but fermentable nature of xylitol also contributes to a constipation relieving effect and improved bone mineral density. Xylitol also modulates the immune system, which, together with its antimicrobial activity contribute to a reduced respiratory tract infection, sinusitis, and otitis media risk. As a low caloric sweetener, xylitol may contribute to weight management. It has been suggested that xylitol also increases satiety, but these results are not convincing yet. The benefit of xylitol on metabolic health, in addition to the benefit of the mere replacement of sucrose, remains to be determined in humans. Additional health benefits of xylitol have thus been reported and indicate further opportunities but need to be confirmed in human studies.
Relating auditory attributes of multichannel sound to preference and to physical parameters

Sound reproduced by multichannel systems is affected by many factors giving rise to various sensations, or auditory attributes. Relating specific attributes to overall preference and to physical measures of the sound field provides valuable information for a better understanding of the parameters playing a role in sound quality evaluation. Eight selected attributes are quantified by a panel of 39 listeners using paired-comparison judgments and probabilistic choice models, and related to overall preference. A multiple-regression model predicts preference well, and some similarities are observed within and between musical program materials, allowing for a careful generalization regarding the perception of spatial audio reproduction. Finally, a set of objective measures is derived from analysis of the sound field at the listening position in an attempt to predict the auditory attributes.
Structural basis for AcrVA4 inhibition of specific CRISPR-Cas12a

CRISPR-Cas systems provide bacteria and archaea with programmable immunity against mobile genetic elements. Evolutionary pressure by CRISPR-Cas has driven bacteriophage to evolve small protein inhibitors, anti-CRISPRs (Acrs), that block Cas enzyme function by wide-ranging mechanisms. We show here that the inhibitor AcrVA4 uses a previously undescribed strategy to recognize the L. bacterium Cas12a (LbCas12a) pre-crRNA processing nuclease, forming a Cas12a dimer, and allosterically inhibiting DNA binding. The Ac. species Cas12a (AsCas12a) enzyme, widely used for genome editing applications, contains an ancestral helical bundle that blocks AcrVA4 binding and allows it to escape anti-CRISPR recognition. Using biochemical, microbiological, and human cell editing experiments, we show that Cas12a orthologs can be rendered either sensitive or resistant to AcrVA4 through rational structural engineering informed by evolution. Together, these findings explain a new mode of CRISPR-Cas inhibition and illustrate how structural variability in Cas effectors can drive opportunistic co-evolution of inhibitors by bacteriophage.
How do physiotherapists solicit and explore patients’ concerns in back pain consultations: a conversation analytic approach

ABSTRACT Background : Guidelines advocate that non-specific chronic low back pain (NSCLBP) be considered within a multi-dimensional bio-psychosocial (BPS) framework. This BPS approach advocates incorporating the patient’s perspective as part of the treatment process. ‘Agenda setting’ has been introduced as the key to understanding patients’ concerns in medical encounters; however, this has received little attention in physiotherapy. This study explored how physiotherapists solicit and respond to the agenda of concerns that patients with NSCLBP bring to initial encounters. Method : The research setting was primary care. Twenty initial physiotherapy consultations were video-recorded, transcribed and analyzed using conversation analysis, a qualitative observational method. Both verbal and non-verbal features of the interaction were considered. Results : This data highlights a spectrum of communication styles ranging from more physiotherapist-focused, where the physiotherapists did not attend to patients’ concerns, to a more patient-focused style, which provided greater opportunities for patients to voice their concerns. On occasions, patients were willing to pursue their own agenda when their concern was initially overlooked. Conclusion : This study provides empirical evidence on communication patterns in physiotherapy practice. A more collaborative style of communication with a shared conversational agenda provided patients with the conversational space to describe their concerns more fully.
eIF4A Inhibitors Suppress Cell-Cycle Feedback Response and Acquired Resistance to CDK4/6 Inhibition in Cancer

CDK4/6 inhibitors are FDA-approved drugs for estrogen receptor–positive (ER+) breast cancer and are being evaluated to treat other tumor types, including KRAS-mutant non–small cell lung cancer (NSCLC). However, their clinical utility is often limited by drug resistance. Here, we sought to better understand the resistant mechanisms and help devise potential strategies to overcome this challenge. We show that treatment with CDK4/6 inhibitors in both ER+ breast cancer and KRAS-mutant NSCLC cells induces feedback upregulation of cyclin D1, CDK4, and cyclin E1, mediating drug resistance. We demonstrate that rocaglates, which preferentially target translation of key cell-cycle regulators, effectively suppress this feedback upregulation induced by CDK4/6 inhibition. Consequently, combination treatment of CDK4/6 inhibitor palbociclib with the eukaryotic initiation factor (eIF) 4A inhibitor, CR-1-31-B, is synergistic in suppressing the growth of these cancer cells in vitro and in vivo. Furthermore, ER+ breast cancer and KRAS-mutant NSCLC cells that acquired resistance to palbociclib after chronic drug exposure are also highly sensitive to this combination treatment strategy. Our findings reveal a novel strategy using eIF4A inhibitors to suppress cell-cycle feedback response and to overcome resistance to CDK4/6 inhibition in cancer.