Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets

It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is selected from the archive. Thus the population size and the archive size can be arbitrarily specified as long as the archive size is not smaller than the required number of solutions. The final population is not necessarily to be a good solution set since it is not presented to the decision maker. Through computational experiments, we show the advantages of this framework over the standard final population and final archive frameworks. We also discuss how to select a final solution set and how to explain the reason for the selection, which is the first attempt towards an explainable EMO framework.
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives*

Abstract Multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards a deeper understanding of how evolutionary algorithms solve multimodal multiobjective problems, we propose the OneJumpZeroJump problem, a bi-objective problem composed of two objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multiobjective optimizer (SEMO) with probability one does not compute the full Pareto front, regardless of the runtime. In contrast, for all problem sizes n and all jump sizes k∈[4.n2-1], the global SEMO (GSEMO) covers the Pareto front in an expected number of Θ((n-2k)nk) iterations. For k=o(n), we also show the tighter bound 32enk+1±o(nk+1), which might be the first runtime bound for an MOEA that is tight apart from lower-order terms. We also combine the GSEMO with two approaches that showed advantages in single-objective multimodal problems. When using the GSEMO with a heavy-tailed mutation operator, the expected runtime improves by a factor of at least kΩ(k). When adapting the recent stagnation-detection strategy of Rajabi and Witt (2022) to the GSEMO, the expected runtime also improves by a factor of at least kΩ(k) and surpasses the heavy-tailed GSEMO by a small polynomial factor in k. Via an experimental analysis, we show that these asymptotic differences are visible already for small problem sizes: A factor-5 speed-up from heavy-tailed mutation and a factor-10 speed-up from stagnation detection can be observed already for jump size 4 and problem sizes between 10 and 50. Overall, our results show that the ideas recently developed to aid single-objective evolutionary algorithms to cope with local optima can be effectively employed also in multiobjective optimization.
Thermodynamic properties of the finite-temperature electron gas by the fermionic path integral Monte Carlo method

The new {\em ab initio} quantum path integral Monte Carlo approach has been developed and applied for the entropy difference calculations for the strongly coupled degenerated uniform electron gas (UEG), a well--known model of simple metals. Calculations have been carried out at finite temperature in canonical ensemble over the wide density and temperature ranges. Obtained data may be crucial for density functional theory. Improvements of the developed approach include the Coulomb and exchange interaction of fermions in the basic Monte Carlo cell and its periodic images and the proper change of variables in the path integral measure. The developed approach shows good agreement with available results for fermions even at temperature four times less than the Fermi energy and practically doesn't suffer from the "fermionic sign problem", which takes place in standard path integral Monte Carlo simulations of degenerate fermionic systems. Presented results include pair distribution functions, isochors and isotherms of pressure, internal energy and entropy change in strongly coupled and degenerate UEG in a wide range of density and temperature.
Erosão hídrica em um Argissolo Vermelho-Amarelo, sob diferentes padrões de chuva simulada

This study was carried out in order to determine the soil, water and nutrient losses in a Yellow-Red Ultisol, under four different patterns of simulated rainfall. The experiment was conducted at the Embrapa Agrobiologia experimental field, located in the municipality of Seropedica, Rio de Janeiro, Brazil. The study consisted in the application of simulated rains using variable rainfall intensity classified in different patterns as: advanced (AD), intermediate (IN), delayed (DE), and constant (CT), in plots 0.8 m wide and 1.0 m long. The rain had a duration of 60 min, with a total applied depth of 30 mm, and a peak of 110 mm h-1, for the variable intensity patterns. With the results obtained it was possible to verify that after 60 min of simulated DE pattern provided losses higher than CT, AD and IN, in 61.6, 46.4 and 13.6%, respectively (soil), and 42.0, 19.0 and 19.0% (run-off), respectively, for the AD, IN and CT patterns; and the nutrients losses did not differ among the rainfall patterns, being more affected by water run-off than solid material in suspension.
Procode: the Swiss Multilingual Solution for Automatic Coding and Recoding of Occupations and Economic Activities

Objective. Epidemiological studies require data that are in alignment with the classifications established for occupations or economic activities. The classifications usually include hundreds of codes and titles. Manual coding of raw data may result in misclassification and be time consuming. The goal was to develop and test a web-tool, named Procode, for coding of free-texts against classifications and recoding between different classifications. Methods. Three text classifiers, i.e. Complement Naive Bayes (CNB), Support Vector Machine (SVM) and Random Forest Classifier (RFC), were investigated using a k-fold cross-validation. 30 000 free-texts with manually assigned classification codes of French classification of occupations (PCS) and French classification of activities (NAF) were available. For recoding, Procode integrated a workflow that converts codes of one classification to another according to existing crosswalks. Since this is a straightforward operation, only the recoding time was measured. Results. Among the three investigated text classifiers, CNB resulted in the best performance, where the classifier predicted accurately 57-81% and 63-83% classification codes for PCS and NAF, respectively. SVM lead to somewhat lower results (by 1-2%), while RFC coded accurately up to 30% of the data. The coding operation required one minute per 10 000 records, while the recoding was faster, i.e. 5-10 seconds. Conclusion. The algorithm integrated in Procode showed satisfactory performance, since the tool had to assign the right code by choosing between 500-700 different choices. Based on the results, the authors decided to implement CNB in Procode. In future, if another classifier shows a superior performance, an update will include the required modifications.
Safe Reinforcement Learning with Stability & Safety Guarantees Using Robust MPC

Reinforcement Learning offers tools to optimize policies based on the data obtained from the real system subject to the policy. While the potential of Reinforcement Learning is well understood, many critical aspects still need to be tackled. One crucial aspect is the issue of safety and stability. Recent publications suggest the use of Nonlinear Model Predictive Control techniques in combination with Reinforcement Learning as a viable and theoretically justified approach to tackle these problems. In particular, it has been suggested that robust MPC allows for making formal stability and safety claims in the context of Reinforcement Learning. However, a formal theory detailing how safety and stability can be enforced through the parameter updates delivered by the Reinforcement Learning tools is still lacking. This paper addresses this gap. The theory is developed for the generic robust MPC case, and further detailed in the robust tube-based linear MPC case, where the theory is fairly easy to deploy in practice.
A stochastic alternating direction method of multipliers for non-smooth and non-convex optimization

Alternating direction method of multipliers (ADMM) is a popular first-order method owing to its simplicity and efficiency. However, similar to other proximal splitting methods, the performance of ADMM degrades significantly when the scale of optimization problems to solve becomes large. In this paper, we consider combining ADMM with a class of variance-reduced stochastic gradient estimators for solving large-scale non-convex and non-smooth optimization problems. Global convergence of the generated sequence is established under the additional assumption that the object function satisfies Kurdyka-Łojasiewicz property. Numerical experiments on graph-guided fused lasso and computed tomography are presented to demonstrate the performance of the proposed methods.
Leveraging Transfer Learning for Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL)

This paper proposed several transformer-based approaches for Reliable Intelligence Identification on Vietnamese social network sites at VLSP 2020 evaluation campaign. We exploit both of monolingual and multilingual pre-trained models. Besides, we utilize the ensemble method to improve the robustness of different approaches. Our team achieved a score of 0.9378 at ROC-AUC metric in the private test set which is competitive to other participants.
Thiol‐yne Click Adamantane Monolithic Stationary Phase for Capillary Electrochromatography

A porous crosslinked organic polymer based on N-acryloxysuccinimide (NAS) and ethylene dimethacrylate (EDMA) was prepared inside 75 µm i.d. fused silica capillary as functionalizable monolithic stationary phase for electrochromatographic applications. Succinimide groups on the monolith surface provide reactive sites able to react readily through standard electrophile-nucleophile chemistry. Propargylamine was used to prepare alkyne functionalized poly(NAS-co-EDMA). Onto this thiol-reactive polymer surface was grafted adamantane units via a photochemically-driven addition reaction. Chemical characterization was performed in situ after each synthetic step by means of Raman spectroscopy and grafting kinetics was investigated to ensure quantitative grafting of 1-adamantanethiol. The as-designed monolithic stationary phase exhibited typical reversed-phase separation mechanism as evidenced by the linear increase of the logarithm of retention factor of neutral aromatic solutes with the increase of the aqueous buffer content in the mobile phase.
Generative Adversarial Networks for Automatic Polyp Segmentation

This paper aims to contribute in bench-marking the automatic polyp segmentation problem using generative adversarial networks framework. Perceiving the problem as an image-to-image translation task, conditional generative adversarial networks are utilized to generate masks conditioned by the images as inputs. Both generator and discriminator are convolution neural networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2 score.
Crystal chirality magneto-optical effects in collinear antiferromagnets

The spin chirality, created by magnetic atoms, has been comprehensively understood to generate and control the magneto-optical effects. In comparison, the role of the crystal chirality that relates to nonmagnetic atoms has received much less attention. Here, we theoretically discover the crystal chirality magneto-optical (CCMO) effects, which depend on the chirality of crystal structures that originates from the rearrangement of nonmagnetic atoms. We show that the CCMO effects exist in many collinear antiferromagnets, such as RuO$_{2}$ and CoNb$_{3}$S$_{6}$, which has a local and global crystal chirality, respectively. The key character of the CCMO effects is the sign change if the crystal chirality reverses. The magnitudes of the CCMO spectra can be effectively manipulated by reorienting the Neel vector with the help of an external electric field, and the spectral integrals are found to be proportional to magnetocrystalline anisotropy energy.
Deep Portfolio Optimization via Distributional Prediction of Residual Factors

Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.
Characterizing Universal Reconfigurability of Modular Pivoting Robots

We give both efficient algorithms and hardness results for reconfiguring between two connected configurations of modules in the hexagonal grid. The reconfiguration moves that we consider are "pivots", where a hexagonal module rotates around a vertex shared with another module. Following prior work on modular robots, we define two natural sets of hexagon pivoting moves of increasing power: restricted and monkey moves. When we allow both moves, we present the first universal reconfiguration algorithm, which transforms between any two connected configurations using $O(n^3)$ monkey moves. This result strongly contrasts the analogous problem for squares, where there are rigid examples that do not have a single pivoting move preserving connectivity. On the other hand, if we only allow restricted moves, we prove that the reconfiguration problem becomes PSPACE-complete. Moreover, we show that, in contrast to hexagons, the reconfiguration problem for pivoting squares is PSPACE-complete regardless of the set of pivoting moves allowed. In the process, we strengthen the reduction framework of Demaine et al. [FUN'18] that we consider of independent interest.
Learning how to approve updates to machine learning algorithms in non-stationary settings

Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts. As such, the FDA is looking to design policies that can autonomously approve modifications to machine learning algorithms while maintaining or improving the safety and effectiveness of the deployed models. However, selecting a fixed approval strategy, a priori, can be difficult because its performance depends on the stationarity of the data and the quality of the proposed modifications. To this end, we investigate a learning-to-approve approach (L2A) that uses accumulating monitoring data to learn how to approve modifications. L2A defines a family of strategies that vary in their "optimism''---where more optimistic policies have faster approval rates---and searches over this family using an exponentially weighted average forecaster. To control the cumulative risk of the deployed model, we give L2A the option to abstain from making a prediction and incur some fixed abstention cost instead. We derive bounds on the average risk of the model deployed by L2A, assuming the distributional shifts are smooth. In simulation studies and empirical analyses, L2A tailors the level of optimism for each problem-setting: It learns to abstain when performance drops are common and approve beneficial modifications quickly when the distribution is stable.
Concerning Iterative Graph Normalization and Maximum Weight Independent Sets

We consider a very simple dynamical system on weighted graphs which we call Iterative Graph Normalization (IGN) and a variant in which we apply a non-linear activation function to the weights after each normalization. We show that the indicator vectors of the Maximal Independent Sets of the graph are the only binary fixed points of IGN, that they are attractive under simple conditions on the activation function and we characterize their basins of attraction. We enumerate a number of other fixed points and we prove repulsivity for some classes. Based on extensive experiments and different theoretical arguments we conjecture that IGN always converges and converges to a binary solution for non-linear activations. If our conjectures are correct, IGN would thus be a differentiable approximation algorithm for the Maximum Weight Independent Set problem (MWIS), a central NP-hard optimization problem with numerous applications. IGN is closely related to a greedy approximation algorithm of MWIS by Kako et al. which has a proven approximation ratio. Experimental results show that IGN provides solutions of very similar quality. In the context of the Assignment Problem, IGN corresponds to an iterative matrix normalization scheme which is closely related to the Sinkhorn-Knopp algorithm except that it projects to a permutation matrix instead of a doubly stochastic matrix. We relate our scheme to the Softassign algorithm and provide comparative results. As Graph Normalization is differentiable, its iterations can be embedded into a machine learning framework and used to train end-to-end any model which includes a graphical optimization step which can be cast as a maximum weight independent set problem. This includes problems such as graph and hypergraph matching, sequence alignment, clustering, ranking, etc. with applications in multiple domains.
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

Compressed sensing (CS) computed tomography (CT) has been proven to be important for several clinical applications, such as sparse-view CT, digital tomosynthesis, and interior tomography. Traditional CS focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in the data consistency layer, the proposed LEARN++ model integrates two parallel and interactive subnetworks to perform image restoration and sinogram inpainting operations on both the image and projection domains simultaneously, which can fully explore the latent relations between projection data and reconstructed images. The experimental results demonstrate that the proposed LEARN++ model achieves competitive qualitative and quantitative results compared to several state-of-the-art methods in terms of both artifact reduction and detail preservation.
Comparison Chosen DFT Methods with Six Basis Sets on Harmine Molecule

The effect of the DFT methods and basis sets on harmine molecule has been investigated. 26 DFT methods with 6 basis sets were used. Two main comparison chosen DFT methods in this work has been investigated. It is concluded that this contribution is very important and cannot be neglected. In the following analysis, changes in energy levels were investigated by two different methods. Considering a specific basis set, changes in energy levels were obtained using different DFT methods. A specific DFT method is chosen and changes in energy levels have been investigated by means of various basis sets. Effect of the choice of basis sets on geometrical parameters on harmine molecule has also been investigated.
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces

This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches.
Cross Layer Attacks and How to Use Them (for DNS Cache Poisoning, Device Tracking and More)

We analyze the prandom pseudo random number generator (PRNG) in use in the Linux kernel (which is the kernel of the Linux operating system, as well as of Android) and demonstrate that this PRNG is weak. The prandom PRNG is in use by many "consumers" in the Linux kernel. We focused on three consumers at the network level – the UDP source port generation algorithm, the IPv6 flow label generation algorithm and the IPv4 ID generation algorithm. The flawed prandom PRNG is shared by all these consumers, which enables us to mount "cross layer attacks" against the Linux kernel. In these attacks, we infer the internal state of the prandom PRNG from one OSI layer, and use it to either predict the values of the PRNG employed by the other OSI layer, or to correlate it to an internal state of the PRNG inferred from the other protocol.Using this approach we can mount a very efficient DNS cache poisoning attack against Linux. We collect TCP/IPv6 flow label values, or UDP source ports, or TCP/IPv4 IP ID values, reconstruct the internal PRNG state, then predict an outbound DNS query UDP source port, which speeds up the attack by a factor of x3000 to x6000. This attack works remotely, but can also be mounted locally, across Linux users and across containers, and (depending on the stub resolver) can poison the cache with an arbitrary DNS record. Additionally, we can identify and track Linux and Android devices – we collect TCP/IPv6 flow label values and/or UDP source port values and/or TCP/IPv4 ID fields, reconstruct the PRNG internal state and correlate this new state to previously extracted PRNG states to identify the same device.