Miracle-dz/UniFAR-FTUs huggingface dataset:
Production of Cationic Nanogels with Potential Use in Controlled Drug Delivery

Dual‐stimuli‐responsive nanogel particles based on poly(2‐(diethylamino)ethyl) methacrylate (PDEAEMA) cross‐linked with ethylene glycol dimethacrylate (EGDMA) are prepared via batch emulsion polymerization. An in‐depth study of their swelling behavior is carried out, which presents a dual‐dependent thermo‐ and pH‐sensitivity tunable with the ionic strength. The study reveals that nanogel particles undergo a unique transition from a swollen state to a collapsed state at physiological conditions. Such systems presenting tunable sensitivity and synthesized using biocompatible components could be suitable for applications in the field of controlled drug or gene delivery.
Identifying Progress Toward Ethnoracial Achievement Equity Across U.S. School Districts: A New Approach

Abstract We draw on novel district-level test score data to describe novel approaches for measuring ethnoracial achievement gaps and assessing trends toward achievement equity from 2009 to 2016. Using SEDA data, we estimate gap trends for each grade over time in each district. We measure trends in both within-district gaps—comparing Black or Hispanic to White students in the same district—and national gaps—comparing a district’s Black or Hispanic students to White students nationally. Withindistrict ethnoracial gaps shrunk in one-third to two-thirds of districts, depending on subject and ethnoracial dyad. Across subjects and ethnoracial dyads, national gaps shrunk in more than half of districts, indicating that non-White students gained on White students nationally, but not in their own districts. Our findings add complexity to the achievement gap literature by (1) estimating gaps at the district level; (2) noting considerable variation in the magnitude of gap shrinkage across districts; (3) pointing to the importance of comparison group and imperfect correspondence of within-district and national gap trends in districts; and (4) identifying variation in gap trends across grades and subjects.
Green Synthesis of High-performance Graphene Reinforced Geopolymer Composites: A Review on Environment-Friendly Extraction of Nanomaterials

The field of nanomaterials has greatly advanced in the last decade following a wide range of applications in the fields of electronics, automobiles, construction, and healthcare. Synthesis of the nanomaterials plays a crucial role in redefining the current engineering and science field. At the same time, procuring an environment-friendly end product through eco-friendly solutions and sustainable processes is the key to many global problems. Green synthesis of nanomaterials like graphene and its derivatives involves mild reaction conditions and nontoxic precursors because it is simple, cost-effective, relatively reproducible, and often results in more stable materials. This paper primarily focuses on the green synthesis of graphene and its derivatives (graphene oxide & reduced graphene oxide) and geopolymers; a green technology for preparing graphene reinforced geopolymer composites. Various methods used globally for green synthesis of graphene and geopolymer are briefly discussed and this paper tries to integrate these two areas for a green end product. Preparation techniques and possible applications of these green composites are also discussed to provide insights on the current growth and developments.
Shipwright: A Human-in-the-Loop System for Dockerfile Repair

Docker is a tool for lightweight OS-level virtualization. Docker images are created by performing a build, controlled by a source-level artifact called a Dockerfile. We studied Dockerfiles on GitHub, and-to our great surprise-found that over a quarter of the examined Dockerfiles failed to build (and thus to produce images). To address this problem, we propose SHIPWRIGHT, a human-in-the-loop system for finding repairs to broken Dockerfiles. SHIPWRIGHT uses a modified version of the BERT language model to embed build logs and to cluster broken Dockerfiles. Using these clusters and a search-based procedure, we were able to design 13 rules for making automated repairs to Dockerfiles. With the aid of SHIPWRIGHT, we submitted 45 pull requests (with a 42.2% acceptance rate) to GitHub projects with broken Dockerfiles. Furthermore, in a "time-travel" analysis of broken Dockerfiles that were later fixed, we found that SHIPWRIGHT proposed repairs that were equivalent to human-authored patches in 22.77% of the cases we studied. Finally, we compared our work with recent, state-of-the-art, static Dockerfile analyses, and found that, while static tools detected possible build-failure-inducing issues in 20.6–33.8% of the files we examined, SHIPWRIGHT was able to detect possible issues in 73.25% of the files and, additionally, provide automated repairs for 18.9% of the files.
1D and 2D NMR study of some allenic carotenoids of the fucoxanthin series

The allenic carotenoid all‐trans‐(3S,5R,6S,3′S,5′R,6′R)‐19′‐butanoyloxyfucoxanthin, isolated from the marine chrysophyte Pelagococcus subviridis, and its 9′‐cis isomer, obtained in 45% yield on iodine‐catalysed stereomutation, were submitted to a detailed 1H and 13C NMR analysis. For comparison, all‐trans‐fucoxanthin [(3S,5R,6S3′S,5′R,6′R)‐5,6‐epoxy‐3,3′,5′‐trihydroxy‐6′,7′‐didehydro‐5,6,7,8,5′,6′‐hexahydro‐β,β‐caroten‐8 ‐one‐3′‐acetate] was included in the study. The complete assignments of the 400 MHz 1H NMR spectra and of the 100.6 MHz 13C NMR spectra, including the stereochemistry, were possible by application of two‐dimensional 1H,1H chemical shift correlated spectroscopy (2D COSY), 1D and 2D total correlation spectroscopy (TOCSY, HOHAHA), rotating‐frame nuclear Overhauser spectroscopy (ROESY, CAMELSPIN) and 1H‐detected one‐bond (with and without 13C decoupling) and multiple‐bond 1H, 13C‐correlated 2D spectroscopy. The 9′‐trans to 9′‐cis stereomutation was unequivocally proved for 19′‐butanoyloxyfucoxanthin, mainly by 1D and 2D ROESY experiments; this is in contrast to a previously claimed 6′R → 6′S stereomutation in related allenic carotenoids.
The Effects of an 8-week Walking Program on Serum Lipids, Circulation Matrix Metalloproteinase-9 and Tissue Inhibitor of Metalloproteinase-1 in Post-menopausal

Purpose: To examine the effects of an 8-week walking program on serum lipids, circulation matrix metalloproteinase-9 and tissue inhibitor of metalloproteinase-1 in post-menopausal women. Method: Body weight, percent body fat, body mass index, estimated maximal oxygen consumption, blood lipids, matrix metalloproteinase-9, tissue inhibitor of metalloproteinase-1 concentrations were assessed and their matrix metalloproteinase-9/tissue inhibitor of metalloproteinase-1 molar ratios were calculated in exercise (n=12) and control (n=12) groups. Exercise group completed an eight-week walking program at moderate (~6.29±0.15km/h; ~62% maximum heart rate reserve) intensity. Results: Significant changes in estimated maximal oxygen consumption, systolic and diastolic blood pressures, body weight, and body mass index (p< .05) were determined in exercise group. However, there were no significant changes in the measured blood lipids (triglyceride, cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol), matrix metalloproteinase-9, and tissue inhibitor of metalloproteinase-1 concentrations, and matrix metalloproteinase-9/ tissue inhibitor of metalloproteinase-1 molar ratio of exercise group, except for a nearly significant change in tissue inhibitor of metalloproteinase-1 level (p= .092). We determined no significant changes in any of the measured parameters in control group. Conclusion: Despite its protective effects against coronary heart disease risks, the duration and intensity of this program is not sufficient to cause significant changes in blood lipids, matrix metalloproteinase-9, and tissue inhibitor of metalloproteinase-1 concentrations. A higher-intensity, longer-duration program accompanied with diet is proposed.
Cryptographic Security of the MLS RFC, Draft 11

—Cryptographic communication protocols provide conﬁdentiality, integrity and authentication properties for end-to-end communication under strong corruption attacks, including, notably, post-compromise security (PCS). Most protocols are designed for one-to-one communication. Protocols for group communication are less common, less efﬁcient, and tend to provide weaker security guarantees. This is because group communication poses unique challenges, such as coordinated key updates, changes to group membership and complex post-compromise recovery procedures. We need to tackle this complex challenge as a community. Thus, the Internet Engineering Task Force (IETF) has created a working group with the goal of developing a sound standard for a continuous asynchronous key-exchange protocol for dynamic groups that is secure and remains efﬁcient for large group sizes. The current version of the Messaging Layer Security ( MLS ) security protocol is in a feature freeze, i.e., no changes are made in order to provide a stable basis for cryptographic analysis. The key schedule and TreeKEM design are of particular concern since they are crucial to distribute and combine several keys to achieve PCS.In this work, we provide a computational analysis of the MLS key schedule, TreeKEM and their composition, as speciﬁed in Draft 11 of the MLS RFC. The analysis is carried out using the State Separating Proofs methodology [9], and showcases the ﬂexibility of the approach, enabling us to provide a full computational analysis shortly after Draft 11 was published.
Combinatorial Bandits under Strategic Manipulations

Strategic behavior against sequential learning methods, such as "click framing'' in real recommendation systems, have been widely observed. Motivated by such behavior we study the problem of combinatorial multi-armed bandits (CMAB) under strategic manipulations of rewards, where each arm can modify the emitted reward signals for its own interest. This characterization of the adversarial behavior is a relaxation of previously well-studied settings such as adversarial attacks and adversarial corruption. We propose a strategic variant of the combinatorial UCB algorithm, which has a regret of at most O(mlog T + m B_max ) under strategic manipulations, where T is the time horizon, m is the number of arms, and B_max is the maximum budget of an arm. We provide lower bounds on the budget for arms to incur certain regret of the bandit algorithm. Extensive experiments on online worker selection for crowdsourcing systems, online influence maximization and online recommendations with both synthetic and real datasets corroborate our theoretical findings on robustness and regret bounds, in a variety of regimes of manipulation budgets.
Convex geometric reasoning for crystalline energies

“The present work revisits the classical Wulff problem restricted to crystalline integrands, a class of surface energies that gives rise to finitely faceted crystals. The general proof of the Wulff theorem was given by J.E. Taylor (1978) by methods of Geometric Measure Theory. This work follows a simpler and direct way through Minkowski Theory by taking advantage of the convex properties of the considered Wulff shapes. ” (Final version is published on Caspian Journal of Computational & Mathematical Engineering, 1, 2016)
On The Effect of Auxiliary Tasks on Representation Dynamics

While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achieved. This work develops our understanding of the relationship between auxiliary tasks, environment structure, and representations by analysing the dynamics of temporal difference algorithms. Through this approach, we establish a connection between the spectral decomposition of the transition operator and the representations induced by a variety of auxiliary tasks. We then leverage insights from these theoretical results to inform the selection of auxiliary tasks for deep reinforcement learning agents in sparse-reward environments.
A continuation method for computing the multilinear PageRank

The multilinear PageRank model [Gleich et al., SIAM J Matrix Anal Appl, 2015;36(4):1507–41] is a tensor‐based generalization of the PageRank model. Its computation requires solving a system of polynomial equations that contains a parameter α∈[0,1) . For α≈1 , this computation remains a challenging problem, especially since the solution may be nonunique. Extrapolation strategies that start from smaller values of α and “follow” the solution by slowly increasing this parameter have been suggested; however, there are known cases where these strategies fail, because a globally continuous solution curve cannot be defined as a function of α . In this article, we improve on this idea, by employing a predictor‐corrector continuation algorithm based on a more general representation of the solutions as a curve in ℝn+1 . We prove several global properties of this curve that ensure the good behavior of the algorithm, and we show in our numerical experiments that this method is significantly more reliable than the existing alternatives.
Are Anti-Feminist Communities Gateways to the Far Right? Evidence from Reddit and YouTube

Researchers have suggested that “the Manosphere,” a conglomerate of men-centered online communities, may serve as a gateway to far right movements. In that context, this paper quantitatively studies the migratory patterns between a variety of groups within the Manosphere and the Alt-right, a loosely connected far right movement that has been particularly active in mainstream social networks. Our analysis leverages over 300 million comments spread through Reddit (in 115 subreddits) and YouTube (in 526 channels) to investigate whether the audiences of channels and subreddits associated with these communities have converged between 2006 and 2018. In addition to subreddits related to the communities of interest, we also collect data on counterparts: other groups of users which we use for comparison (e.g., for YouTube we use a set of media channels). Besides measuring the similarity in the commenting user bases of these communities, we perform a migration study, calculating to which extent users in the Manosphere gradually engage with Alt-right content. Our results suggest that there is a large overlap between the user bases of the Alt-right and of the Manosphere and that members of the Manosphere have a bigger chance to engage with far right content than carefully chosen counterparts. However, our analysis also shows that migration and user base overlap varies substantially across different platforms and within the Manosphere. Members of some communities (e.g., Men’s Rights Activists) gradually engage with the Alt-right significantly more than counterparts on both Reddit and YouTube, whereas for other communities, this engagement happens mostly on Reddit (e.g., Pick Up Artists). Overall, our work paints a nuanced picture of the pipeline between the Manosphere and the Alt-right, which may inform platforms’ policies and moderation decisions regarding these communities.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.
Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning.

PURPOSE We propose a general framework for quantifying predictive uncertainties of dose-related quantities and leveraging this information in a dose mimicking problem in the context of automated radiation therapy treatment planning. METHODS A three-step pipeline, comprising feature extraction, dose statistic prediction and dose mimicking, is employed. In particular, the features are produced by a convolutional variational autoencoder and used as inputs in a previously developed nonparametric Bayesian statistical method, estimating the multivariate predictive distribution of a collection of predefined dose statistics. Specially developed objective functions are then used to construct a probabilistic dose mimicking problem based on the produced distributions, creating deliverable treatment plans. RESULTS The numerical experiments are performed using a dataset of 94 retrospective treatment plans of prostate cancer patients. We show that the features extracted by the variational autoencoder capture geometric information of substantial relevance to the dose statistic prediction problem and are related to dose statistics in a more regularized fashion than hand-crafted features. The estimated predictive distributions are reasonable and outperforms a non-input-dependent benchmark method, and the deliverable plans produced by the probabilistic dose mimicking agree better with their clinical counterparts than for a non-probabilistic formulation. CONCLUSIONS We demonstrate that prediction of dose-related quantities may be extended to include uncertainty estimation and that such probabilistic information may be leveraged in a dose mimicking problem. The treatment plans produced by the proposed pipeline resemble their original counterparts well, illustrating the merits of a holistic approach to automated planning based on probabilistic modeling.
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning

Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning. To take full advantage of global information, which exploits the states from all agents and the related environment for decomposing Q values into individual credits, we propose a general meta-learning-based Mixing Network with Meta Policy Gradient~(MNMPG) framework to distill the global hierarchy for delicate reward decomposition. The excitation signal for learning global hierarchy is deduced from the episode reward difference between before and after"exercise updates"through the utility network. Our method is generally applicable to the CTDE method using a monotonic mixing network. Experiments on the StarCraft II micromanagement benchmark demonstrate that our method just with a simple utility network is able to outperform the current state-of-the-art MARL algorithms on 4 of 5 super hard scenarios. Better performance can be further achieved when combined with a role-based utility network.
Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance

Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night’s sleep is important for job performance. However, both real-world sleep behavior and job performance are difficult to measure at scale. In this work, we demonstrate that people’s everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 months, leveraging a mattress sensor and online mobile app to conduct the largest study of this kind to date. We first demonstrate that cumulative sleep measures are significantly correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% average reduction in contracts established for salespeople and a 9.5% average reduction in game grade for the athletes. We then investigate the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the job performance of the athletes, but not the salespeople. To support that our app-based performance indicator truly captures meaningful variation in psychomotor function as it relates to sleep and is robust against potential confounds, we conducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited average app interaction times that were 5.0% slower. We also find that app interaction time exhibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. The findings from this work reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.
A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

The housing structures have changed with urbanization and the growth due to the construction of high-rise buildings all around the world requires end-use appliance energy conservation and management in real-time. This shift also came along with smart-meters which enabled the estimation of appliance-specific power consumption from the buildings aggregate power consumption reading. Non-intrusive load monitoring (NILM) or energy disaggregation is aimed at separating the household energy measured at the aggregate level into constituent appliances. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Incredible research and publications have been conducted on energy disaggregation, non-intrusive load monitoring, home energy management and appliance classification. There exists an API, NILMTK, a reproducible benchmark algorithm for the same. Many other approaches to perform energy disaggregation has been adapted such as deep neural network architectures and big data approach for household energy disaggregation. This paper provides a survey of the effective NILM system frameworks and reviews the performance of the benchmark algorithms in a comprehensive manner. This paper also summarizes the wide application scope and the effectiveness of the algorithmic performance on three publicly available data sets.