Miracle-dz/UniFAR-FTUs huggingface dataset:
Alkenones as a Promising Green Alternative for Waxes in Cosmetics and Personal Care Products

The move toward green, sustainable, natural products has been growing in the cosmetic and personal care industry. Ingredients derived from marine organisms and algae are present in many cosmetic products. In this study, a new green ingredient, a wax (i.e., long-chain alkenones) derived from Isochyrsis sp. , was evaluated as an alternative for cosmetic waxes. First, the melting point was determined (71.1–77.4 °C), then the alkenones’ thickening capability in five emollients was evaluated and compared to microcrystalline wax and ozokerite. Alkenones were compatible with three emollients and thickened the emollients similarly to the other waxes. Then, lipsticks and lip balms were formulated with and without alkenones. All products remained stable at room temperature for 10 weeks. Lipstick formulated with alkenones was the most resistant to high temperature. Finally, alkenones were compared to three cosmetic thickening waxes in creams. Viscosity, rheology, and stability of the creams were evaluated. All creams had a gel-like behavior. Both viscosity and storage modulus increased in the same order: cream with alkenones < cetyl alcohol < stearic acid < glyceryl monostearate. Overall, alkenones’ performance was comparable to the other three waxes. Alkenones can thus offer a potential green choice as a new cosmetic structuring agent.
Evaluating Susceptibility of VPN Implementations to DoS Attacks Using Adversarial Testing

Many systems today rely heavily on virtual private network (VPN) technology to connect networks and protect their services on the Internet. While prior studies compare the performance of different implementations, they do not consider adversarial settings. To address this gap, we evaluate the resilience of VPN implementations to flooding-based denial-of-service (DoS) attacks. We focus on a class of stateless flooding attacks, which are particularly threatening to real connections, as they can be carried out by an off-path attacker using spoofed IP addresses. We have implemented various attacks to evaluate DoS resilience for three major open-source VPN solutions, with surprising results: On high-performance hardware with a $40\,\mathrm{Gb/s}$ interface, data transfer over established WireGuard connections can be fully denied with $700\,\mathrm{Mb/s}$ of attack traffic. For strongSwan (IPsec), an adversary can block any legitimate connections from being established using only $75\,\mathrm{Mb/s}$ of attack traffic. OpenVPN can be overwhelmed with $100\,\mathrm{Mb/s}$ of flood traffic denying data transfer through the VPN connection as well as connection establishment completely. Further analysis has revealed implementation bugs and major inefficiencies in the implementations related to concurrency aspects. These findings demonstrate a need for more adversarial testing of VPN implementations with respect to DoS resilience.
Dark energy with oscillatory tracking potential: Observational Constraints and Perturbative effects

The cosmological models exhibiting tracker properties have great significance in the context of dark energy as they can reach the present value of dark energy density from a wide range of initial conditions, thereby alleviating both the fine-tuning and the cosmic coincidence problem. The α-attractors, which are originally discussed in the context of inflation, can exhibit the properties of dark energy as they can behave like cosmological trackers at early times and show the late time behaviour of a cosmological constant. In the present paper, we study the Oscillatory Tracker Model (OTM), which belongs to the family of α-attractor dark energy models. Using the current observational data sets like Cosmic Microwave Background (CMB), Baryon Acoustic Oscillation (BAO) and type 1a supernova data (Pantheon compilation), we constrain the parameters of the model and estimate both the mean and best-fit values. Although the oscillatory tracker model contains a larger set of parameters than the usual LCDM model, the common set of parameters of both agree within 1 σ error limits. Our observations using both high redshift and low redshift data supports Hubble parameter value H0 = 67.4 Kms−1Mpc−1. We study the effect of the OTM on the CMB temperature and polarization power spectra, matter power spectrum and fσ8. Our analysis of the CMB power spectrum and matter power spectrum suggests that the oscillatory tracker dark energy model has noticeable differences from usual LCDM predictions. Yet, in most cases, the agreement is very close.
Fed-LAMB: Layerwise and Dimensionwise Locally Adaptive Optimization Algorithm

In the emerging paradigm of federated learning (FL), large amount of clients, such as mobile devices, are used to train possibly high-dimensional models on their respective data. Due to the low bandwidth of mobile devices, decentralized optimization methods need to shift the computation burden from those clients to the computation server while preserving privacy and reasonable communication cost . In this paper, we focus on the training of deep, as in multilayered, neural networks, under the FL settings. We present Fed-LAMB , a novel federated learning method based on a layerwise and dimensionwise updates of the local models, alleviating the noncon-vexity and the multilayered nature of the optimization task at hand. We provide a thorough ﬁnite-time convergence analysis for Fed-LAMB characterizing how fast its gradient decreases. We provide experimental results under iid and non-iid settings to corroborate not only our theory, but also exhibit the faster convergence of our method, compared to the state-of-the-art.
Lagrangian Inference for Ranking Problems

Understanding ranking orders of different items is of great importance in many applications such as sports, online game, and recommendation, among many others. This paper provides a novel approach to inferring the ranking systems. In particular, the paper aims to answer questions like, is item A better than item B? Is item A among the top 10 items? Such inference problems are challenging as they involve combinatorial structures. The key technical innovation is a new Lagrangian inference framework with new bootstrap tools. Strong theoretical guarantees are provided showing the optimality of the proposed method. A novel application of inferring movies’ ranking using a large-scale data set is provided to demonstrate the applicability of the proposed method.
Fast full N-body simulations of generic modified gravity: conformal coupling models

We present mg-glam, a code developed for the very fast production of full N-body cosmological simulations in modified gravity (MG) models. We describe the implementation, numerical tests and first results of a large suite of cosmological simulations for three classes of MG models with conformal coupling terms: the f(R) gravity, symmetron and coupled quintessence models. Derived from the parallel particle-mesh code glam, mg-glam incorporates an efficient multigrid relaxation technique to solve the characteristic nonlinear partial differential equations of these models. For f(R) gravity, we have included new variants to diversify the model behaviour, and we have tailored the relaxation algorithms to these to maintain high computational efficiency. In a companion paper, we describe versions of this code developed for derivative coupling MG models, including the Vainshtein- and K-mouflage-type models. mg-glam can model the prototypes for most MG models of interest, and is broad and versatile. The code is highly optimised, with a tremendous speedup of a factor of more than a hundred compared with earlier N-body codes, while still giving accurate predictions of the matter power spectrum and dark matter halo abundance. mg-glam is ideal for the generation of large numbers of MG simulations that can be used in the construction of mock galaxy catalogues and the production of accurate emulators for ongoing and future galaxy surveys.
A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse

We study two-stage stochastic optimization problems with random recourse, where the coefficients of the adaptive decisions involve uncertain parameters. To deal with the infinite-dimensional recourse decisions, we propose a scalable approximation scheme via piecewise linear and piecewise quadratic decision rules. We develop a data-driven distributionally robust framework with two layers of robustness to address distributional uncertainty. We also establish out-of-sample performance guarantees for the proposed scheme. Applying known ideas, the resulting optimization problem can be reformulated as an exact copositive program that admits semidefinite programming approximations. We design an iterative decomposition algorithm, which converges under some regularity conditions, to reduce the runtime needed to solve this program. Through numerical examples for various known operations management applications, we demonstrate that our method produces significantly better solutions than the traditional sample-average approximation scheme especially when the data are limited. For the problem instances for which only the recourse cost coefficients are random, our method exhibits slightly inferior out-of-sample performance but shorter runtimes compared with a competing approach. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0306 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0306 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github. io/ .
Video Temporal Relationship Mining for Data-Efficient Person Re-identification

This paper is a technical report to our submission to the ICCV 2021 VIPriors Re-identification Challenge. In order to make full use of the visual inductive priors of the data, we treat the query and gallery images of the same identity as continuous frames in a video sequence. And we propose one novel post-processing strategy for video temporal relationship mining, which not only calculates the distance matrix between query and gallery images, but also the matrix between gallery images. The initial query image is used to retrieve the most similar image from the gallery, then the retrieved image is treated as a new query to retrieve its most similar image from the gallery. By iteratively searching for the closest image, we can achieve accurate image retrieval and finally obtain a robust retrieval sequence.
TyXe: Pyro-based Bayesian neural nets for Pytorch

We introduce TyXe, a Bayesian neural network library built on top of Pytorch and Pyro. Our leading design principle is to cleanly separate architecture, prior, inference and likelihood specification, allowing for a flexible workflow where users can quickly iterate over combinations of these components. In contrast to existing packages TyXe does not implement any layer classes, and instead relies on architectures defined in generic Pytorch code. TyXe then provides modular choices for canonical priors, variational guides, inference techniques, and layer selections for a Bayesian treatment of the specified architecture. Sampling tricks for variance reduction, such as local reparameterization or flipout, are implemented as effect handlers, which can be applied independently of other specifications. We showcase the ease of use of TyXe to explore Bayesian versions of popular models from various libraries: toy regression with a pure Pytorch neural network; large-scale image classification with torchvision ResNets; graph neural networks based on DGL; and Neural Radiance Fields built on top of Pytorch3D. Finally, we provide convenient abstractions for variational continual learning. In all cases the change from a deterministic to a Bayesian neural network comes with minimal modifications to existing code, offering a broad range of researchers and practitioners alike practical access to uncertainty estimation techniques. The library is available at https://github.com/TyXe-BDL/TyXe.
PICSAR-QED: a Monte Carlo module to simulate strong-field quantum electrodynamics in particle-in-cell codes for exascale architectures

Physical scenarios where the electromagnetic fields are so strong that quantum electrodynamics (QED) plays a substantial role are one of the frontiers of contemporary plasma physics research. Investigating those scenarios requires state-of-the-art particle-in-cell (PIC) codes able to run on top high-performance computing (HPC) machines and, at the same time, able to simulate strong-field QED processes. This work presents the PICSAR-QED library, an open-source, portable implementation of a Monte Carlo module designed to provide modern PIC codes with the capability to simulate such processes, and optimized for HPC. Detailed tests and benchmarks are carried out to validate the physical models in PICSAR-QED, to study how numerical parameters affect such models, and to demonstrate its capability to run on different architectures (CPUs and GPUs). Its integration with WarpX, a state-of-the-art PIC code designed to deliver scalable performance on upcoming exascale supercomputers, is also discussed and validated against results from the existing literature.
Visualization of acoustic power flow in suspended thin-film lithium niobate phononic devices

We report direct visualization of gigahertz-frequency acoustic waves in lithium niobate phononic circuits. Primary propagation parameters, such as the power flow angle (PFA) and propagation loss (PL), are measured by transmission-mode microwave impedance microscopy (TMIM). Using fast Fourier transform, we can separately analyze forward and backward propagating waves and quantitatively evaluate the propagation loss. Our work provides insightful information on the propagation, diffraction, and attenuation in piezoelectric thin films, which is highly desirable for designing and optimizing phononic devices for microwave signal processing.
Batch Belief Trees for Motion Planning Under Uncertainty

In this work, we develop the Batch Belief Trees (BBT) algorithm for motion planning under motion and sensing uncertainties. The algorithm interleaves between batch sampling, building a graph of nominal trajectories in the state space, and searching over the graph to find belief space motion plans. By searching over the graph, BBT finds sophisticated plans that will visit (and revisit) information-rich regions to reduce uncertainty. One of the key benefits of this algorithm is the modified interplay between exploration and exploitation. Instead of an exhaustive search (exploitation) after one exploration step, the proposed algorithm uses batch samples to explore the state space and, in addition, does not require exhaustive search before the next iteration of batch sampling, which adds flexibility.The algorithm finds motion plans that converge to the optimal one as more samples are added to the graph. We test BBT in different planning environments. Our numerical investigation confirms that BBT finds non-trivial motion plans and is faster compared with previous similar methods.
Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm

Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or loopy) structured PGMs is not guaranteed to discover the optimal solutions [1, p391]. It is in principle possible to convert loopy PGMs to an equivalent tree structure, but this is usually impractical for interesting problems due to exponential blow-up [1, p336]. To address this, we developed the “purge-and-merge” algorithm. This algorithm iteratively nudges a malleable graph structure towards a tree structure by selectively merging factors. The merging process is designed to avoid exponential blow-up by way of sparse structures from which redundancy is purged as the algorithm progresses. We set up tasks to test the algorithm on constraint-satisfaction puzzles such as Sudoku, Fill-apix, and Kakuro, and it outperformed other PGM-based approaches reported in the literature [2]–[4]. While the tasks we set focussed on the binary logic of CSP, we believe the purge-and-merge algorithm could be extended to general PGM inference.
Sim and Real: Better Together

Simulation is used extensively in autonomous systems, particularly in robotic manipulation. By far, the most common approach is to train a controller in simulation, and then use it as an initial starting point for the real system. We demonstrate how to learn simultaneously from both simulation and interaction with the real environment. We propose an algorithm for balancing the large number of samples from the high throughput but less accurate simulation and the low-throughput, high-fidelity and costly samples from the real environment. We achieve that by maintaining a replay buffer for each environment the agent interacts with. We analyze such multi-environment interaction theoretically, and provide convergence properties, through a novel theoretical replay buffer analysis. We demonstrate the efficacy of our method on a sim-to-real environment.
#ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics

The United Nations identified gender equality as a Sustainable Development Goal in 2015, recognizing the underrepresentation of women in politics as a specific barrier to achieving gender equality. Political systems around the world experience gender inequality across all levels of elected government as fewer women run for office than men. This is due in part to online abuse, particularly on social media platforms like Twitter, where women seeking or in power tend to be targeted with more toxic maltreatment than their male counterparts. In this paper, we present reflections on ParityBOT—the first natural language processing-based intervention designed to affect online discourse for women in politics for the better, at scale. Deployed across elections in Canada, the United States and New Zealand, ParityBOT was used to analyse and classify more than 12 million tweets directed at women candidates and counter toxic tweets with supportive ones. From these elections we present three case studies highlighting the current limitations of, and future research and application opportunities for, using a natural language processing-based system to detect online toxicity, specifically with regards to contextually important microaggressions. We examine the rate of false negatives, where ParityBOT failed to pick up on insults directed at specific high profile women, which would be obvious to human users. We examine the unaddressed harms of microaggressions and the potential of yet unseen damage they cause for women in these communities, and for progress towards gender equality overall, in light of these technological blindspots. This work concludes with a discussion on the benefits of partnerships between nonprofit social groups and technology experts to develop responsible, socially impactful approaches to addressing online hate.
Finite Time Exact Quantized Average Consensus with Limited Resources and Transmission Stopping for Energy-Aware Networks

Composed of spatially distributed sensors and actuators that communicate through wireless networks, networked control systems are emerging as a fundamental infrastructure technology in 5G and IoT technologies, including diverse applications, such as autonomous vehicles, UAVs, and various sensing devices. In order to increase flexibility and reduce deployment and maintenance costs, many such applications consider battery-powered or energy-harvesting networks, which bring additional limitations on the energy consumption of the wireless network. Specifically, the operation of battery-powered or energy-harvesting wireless communication networks needs to guarantee (i) efficient communication between nodes and (ii) preservation of available energy. Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed algorithm and show that its execution, on any time-invariant and strongly connected digraph, will allow all nodes to reach, in finite time, a common consensus value that is equal to the exact average (represented as the ratio of two quantized values). Furthermore, we show that our algorithm allows each node to cease transmissions once the exact average of the initial quantized values has been reached (in order to preserve its battery energy). Then, we present upper bounds on (i) the number of transmissions and computations each node has to perform during the execution of the algorithm, and (ii) the memory and energy requirements of each node in order for the algorithm to be executed.
Stochastic Model of Microparticle Scattering on a Crystal

This paper shows that the stochastic approach to solving the problem of the scattering of elementary particles on crystal can serve as an alternative to Louis de Broglie's hypothesis on the wave properties of material particles. The results of calculations presented in this work are in good agreement with the experimentally obtained electron diffraction patterns and X-ray diffraction patterns and the Davisson-Germer experiments. RESUMEN Este artículo muestra que el enfoque estocástico para resolver el problema de la dispersión de partículas elementales en el cristal puede servir como una alternativa a la hipótesis de Louis de Broglie sobre las propiedades ondulatorias de las partículas materiales. Los resultados de los cálculos presentados en este trabajo concuerdan bien con los patrones de difracción de electrones y los patrones de difracción de rayos X obtenidos experimentalmente y los experimentos de Davisson-Germer.
Evaluating the fairness of fine-tuning strategies in self-supervised learning

In this work we examine how fine-tuning impacts the fairness of contrastive Self-Supervised Learning (SSL) models. Our findings indicate that Batch Normalization (BN) statistics play a crucial role, and that updating only the BN statistics of a pre-trained SSL backbone improves its downstream fairness (36% worst subgroup, 25% mean subgroup gap). This procedure is competitive with supervised learning, while taking 4.4x less time to train and requiring only 0.35% as many parameters to be updated. Finally, inspired by recent work in supervised learning, we find that updating BN statistics and training residual skip connections (12.3% of the parameters) achieves parity with a fully fine-tuned model, while taking 1.33x less time to train.
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series

Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.