Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives

We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. 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.2021.0333 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0333 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github. io/ .
Body image in patients with different types of cancer

Background Cancer can cause physical changes and affect satisfaction with a persons’ physical appearance, which in turn can affect overall quality of life. Previous studies have primarily focused on women with breast cancer and few is known about body image in patients with other cancers and especially men. The present study compares satisfaction with body image of patients with different types of cancer with the general population and across sexes and identifies risk factors for diminished body image. Additionally, patients that were diagnosed within the last year and those living with cancer for longer are compared. Methods In this cross-sectional study, N = 531 cancer patients answered the German Self-Image Scale to assess body image. One sample t-tests are utilized to compare the body image of cancer patients with the general population. Stepwise regression analyses were used to identify factors associated with body image and ANOVAs with posthoc tests as well as t-tests were used to examine group differences. Results Cancer patients showed diminished body image compared to the general population. For men, higher relationship satisfaction and lower cancer-specific distress were associated with more positive body self-acceptance (SA), whereas younger age, higher relationship satisfaction, and lower cancer-specific distress resulted in better perceived partner-acceptance of one’s body (PA). In women, higher education, lower anxiety and cancer-specific distress were associated with more positive SA. Female cancer patients with breast/gynecological cancer reported better SA than those with visceral cancers. Higher relationship satisfaction and lower cancer-specific distress were found to be associated with more satisfactory PA in females. Time since diagnosis did not affect body image in this study. Conclusions Results indicate that cancer patients regardless of sex tend to have decreased body image satisfaction. Future research directions include examination of additional entities of cancer, deeper research in men and the role of time since diagnosis.
Molecular Attributes Transfer from Non-Parallel Data

Optimizing chemical molecules for desired properties lies at the core of drug development. Despite initial successes made by deep generative models and reinforcement learning methods, these methods were mostly limited by the requirement of predefined attribute functions or parallel data with manually pre-compiled pairs of original and optimized molecules. In this paper, for the first time, we formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data through adversarial training strategies. Our model further enables both preservation of molecular contents and optimization of molecular properties through combining auxiliary guided-variational autoencoders and generative flow techniques. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
Digital redesign and performance evaluation using the energy resemblance index (ERI)

The paper proposes a new performance index, the energy resemblance index (ERI), to address the performance evaluation and design issues in the digital redesign of analogue control systems. The ERI is defined as the ratio of the integral of the square error (ISE) of the continuous-time system over the error sequence energy (ESE) of the redesigned digital system. By directly transforming the analogue controller into a digital controller as a function of sampling time, ESEs using different discretisation methods for the redesigned system can be obtained via a recursive formula. The authors prove that the ESE will approach the ISE as the sampling time approaches zero for discretisation methods which satisfy a simple condition. Performance of the redesigned digital control system using different discretisation methods at different sampling times can be evaluated based on this index. As for the design, the proper sampling time can be determined based on the user-specified ERI percentage, so that the redesigned digital system performs accordingly. It is found that performance of the redesigned digital system depends on the discretisation method used, the controlled process and the sampling time chosen. There is no particular discretisation method which outperforms the others in all cases.
Semi-Supervised 3D Hand Shape and Pose Estimation with Label Propagation

To obtain 3D annotations, we are restricted to controlled environments or synthetic datasets, leading us to 3D datasets with less generalizability to real-world scenarios. To tackle this issue in the context of semi-supervised 3D hand shape and pose estimation, we propose the Pose Alignment network to propagate 3D annotations from labelled frames to nearby unlabelled frames in sparsely annotated videos. We show that incorporating the alignment supervision on pairs of labelled-unlabelled frames allows us to improve the pose estimation accuracy. Besides, we show that the proposed Pose Alignment network can effectively propagate annotations on unseen sparsely labelled videos without fine-tuning.
Phase stability of the argon crystal: first-principles study based on random phase approximation plus renormalized single excitation corrections

The energy differences between the face-centered cubic (fcc) and hexagonal closed packed (hcp) structures of the argon (Ar) crystal are studied using the first-principles electronic-structure approach at the level of random phase approximation (RPA) plus renormalized single excitation (rSE) correction. By treating both structures at equal footing (i.e., employing the same computational supercell and k grid sampling), our RPA+rSE calculations show that, at zero temperature, the fcc structure is lower in energy than the hcp structure over a wide pressure range. The influence of zero-point energy (ZPE) is also studied and it is found that ZPE only plays a secondary role in determining the relative stability of the two structures, whereas the electron correlation effect dominates. We further examine the equation of states in the high pressure regime, and our RPA+rSE results, complemented with phonon contributions, show excellent agreement with available experimental data. Finally, by computing the Gibbs free energies for both the fcc and hcp at different temperatures, we are able to generate a T–P phase diagram for the Ar crystal, disclosing the pressure–temperature range for each phase. Our calculations show that the fcc phase has a slightly larger entropy and volume than hcp phase at the temperature and pressure condition.
From a Blind Spot to a Nexus: Building on Existing Trends in Knowledge Production to Study the Copresence of Ecotourism and Extraction

Ecotourism is primarily perceived and studied as an alternative to resource extraction, even though increasingly the two coexist side by side in a nexus. Th is article investigates how such instances of copresence are marginalized in literatures about ecotourism and extraction, constituting a “blind spot” in academic literature. An extensive literature review focuses on the existing knowledge trends and paradigms in the production of knowledge about ecotourism and extraction, and analyzes whether they contribute to the “blind spot” or can be mobilized by the nexus perspective. Finally, the article briefl y outlines two methodological approaches for studying ecotourism and extraction as a nexus.
Analytical and experimental evaluation of a novel wideband digital beamformer with on-site coding

In this paper, we present a review of the state-of-the-art analog and digital beamformer architectures. In addition, an analytical and experimental evaluation of a novel transceiver architecture with on-site coding is conducted. The architecture features agile beam forming, jamming mitigation, and MIMO capability. The proposed architecture departs from traditional digital beamforming approaches. Specifically, a single analog to digital converter (ADC) is assigned to a group of array elements instead of having one ADC per element. To facilitate this approach, code division multiplexing is applied to each received antenna signal prior to combining them for digitization using a single ADC. In this paper, a detailed systematic evaluation of the proposed on-site coding receiver is carried out. We examine the trade-offs using non-ideal components (e.g. filters), including finite resolution ADCs and their impact on the system’s noise floor. It is concluded that the bit error rate shows minimal SNR degradation using filters of order 4 and ADCs with 10 bit resolution along with Walsh–Hadamard (WH) codes to realize on-site coding. We also present a two-channel implementation with components off-the-shelf assuming a modulated binary phase shift keying input signal and WH codes of length 8. Measurements show full signal recovery with minimal degradation.
Towards automatic identification of linguistic politeness in Hindi texts

In this paper I present a classifier for automatic identification of linguistic politeness in Hindi texts. I have used the manually annotated corpus of over 25,000 blog comments to train an SVM. Making use of the discursive and interactional approaches to politeness the paper gives an exposition of the normative, conventionalised politeness structures of Hindi. It is seen that using these manually recognised structures as features in training the SVM significantly improves the performance of the classifier on the test set. The trained system gives a significantly high accuracy of over 77% which is within 2% of human accuracy.
SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches

Sketch-based image manipulation is an interactive image editing task to modify an image based on input sketches from users. Existing methods typically formulate this task as a conditional inpainting problem, which requires users to draw an extra mask indicating the region to modify in addition to sketches. The masked regions are regarded as holes and filled by an inpainting model conditioned on the sketch. With this formulation, paired training data can be easily obtained by randomly creating masks and extracting edges or contours. Although this setup simplifies data preparation and model design, it complicates user interaction and discards useful information in masked regions. To this end, we investigate a new paradigm of sketch-based image manipulation: mask-free local image manipulation, which only requires sketch inputs from users and utilizes the entire original image. Given an image and sketch, our model automatically predicts the target modification region and encodes it into a structure agnostic style vector. A generator then synthesizes the new image content based on the style vector and sketch. The manipulated image is finally produced by blending the generator output into the modification region of the original image. Our model can be trained in a self-supervised fashion by learning the reconstruction of an image region from the style vector and sketch. The proposed method offers simpler and more intuitive user workflows for sketch-based image manipulation and provides better results than previous approaches. More results, code and interactive demo will be available at https://zengxianyu.github. io/sketchedit.
Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems

In this paper, we develop a robust 3D garment digitization solution that can generalize well on real-world fashion catalog images with cloth texture occlusions and large body pose variations. We assumed fixed topology parametric template mesh models for known types of garments (e.g., T-shirts, Trousers) and perform mapping of high-quality texture from an input catalog image to UV map panels corresponding to the parametric mesh model of the garment. We achieve this by first predicting a sparse set of 2D landmarks on the boundary of the garments. Subsequently, we use these landmarks to perform Thin-Plate-Spline-based texture transfer on UV map panels. Subsequently, we employ a deep texture inpainting network to fill the large holes (due to view variations & self-occlusions) in TPS output to generate consistent UV maps. Furthermore, to train the supervised deep networks for landmark prediction & texture inpainting tasks, we generated a large set of synthetic data with varying texture and lighting imaged from various views with the human present in a wide variety of poses. Additionally, we manually annotated a small set of fashion catalog images crawled from online fashion e-commerce platforms to finetune. We conduct thorough empirical evaluations and show impressive qualitative results of our proposed 3D garment texture solution on fashion catalog images. Such 3D garment digitization helps us solve the challenging task of enabling 3D Virtual Try-on.
AugLiChem: data augmentation library of chemical structures for machine learning

Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and graph neural networks (GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem.
Particle configurations for branching Brownian motion with an inhomogeneous branching rate

Aiming to understand the distribution of fitness levels of individuals in a large population undergoing selection, we study the particle configurations of branching Brownian motion where each particle independently moves as Brownian motion with negative drift, particles can die or undergo dyadic fission, and the difference between the birth rate and the death rate is proportional to the particle's location. Under some assumptions, we obtain the limit in probability of the number of particles in any given interval and an explicit formula for the asymptotic empirical density of the fitness distribution. We show that after a sufficiently long time, the fitness distribution from the lowest to the highest fitness levels approximately evolves as a traveling wave with a profile which is asymptotically related the the Airy function. Our work complements the results in Roberts and Schweinsberg (2021), giving a fuller picture of the fitness distribution.
Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional Variational AutoEncoders for Adversary Detection in the Presence of Noisy Images

With the rapid advancement and increased use of deep learning models in image identification, security becomes a major concern to their deployment in safety-critical systems. Since the accuracy and robustness of deep learning models are primarily attributed from the purity of the training samples, therefore the deep learning architectures are often susceptible to adversarial attacks. Adversarial attacks are often obtained by making subtle perturbations to normal images, which are mostly imperceptible to humans, but can seriously confuse the state-of-the-art machine learning models. What is so special in the slightest intelligent perturbations or noise additions over normal images that it leads to catastrophic classifications by the deep neural networks? Using statistical hypothesis testing, we find that Conditional Variational AutoEncoders (CVAE) are surprisingly good at detecting imperceptible image perturbations. In this paper, we show how CVAEs can be effectively used to detect adversarial attacks on image classification networks. We demonstrate our results over MNIST, CIFAR-10 dataset and show how our method gives comparable performance to the state-of-the-art methods in detecting adversaries while not getting confused with noisy images, where most of the existing methods falter.
PERCIVAL: Open-Source Posit RISC-V Core With Quire Capability

The posit representation for real numbers is an alternative to the ubiquitous IEEE 754 floating-point standard. In this work, we present PERCIVAL, an application-level posit RISC-V core based on CVA6 that can execute all posit instructions, including the quire fused operations. This solves the obstacle encountered by previous works, which only included partial posit support or which had to emulate posits in software. In addition, Xposit, a RISC-V extension for posit instructions is incorporated into LLVM. Therefore, PERCIVAL is the first work that integrates the complete posit instruction set in hardware. These elements allow for the native execution of posit instructions as well as the standard floating-point ones, further permitting the comparison of these representations. FPGA and ASIC synthesis show the hardware cost of implementing 32-bit posits and highlight the significant overhead of including a quire accumulator. However, results show that the quire enables a more accurate execution of dot products. In general matrix multiplications, the accuracy error is reduced up to 4 orders of magnitude. Furthermore, performance comparisons show that these accuracy improvements do not hinder their execution, as posits run as fast as single-precision floats and exhibit better timing than double-precision floats, thus potentially providing an alternative representation.
A tale of two DIGs: The relative role of Hii regions and low-mass hot evolved stars in powering the diffuse ionised gas (DIG) in PHANGS-MUSE galaxies

, , ABSTRACT We use integral ﬁeld spectroscopy from the PHANGS–MUSE survey, which resolves the ionised interstellar medium structure at ∼ 50 pc resolution in 19 nearby spiral galaxies, to study the origin of the di ﬀ use ionised gas (DIG). We examine the physical conditions of the di ﬀ use gas by ﬁrst removing morphologically deﬁned H ii regions and then binning the low-surface-brightness areas to achieve signiﬁcant detections of the key nebular lines in the DIG. A simple model for the leakage and propagation of ionising radiation from H ii regions is able to reproduce the observed distribution of H α in the DIG. This model infers a typical mean free path for the ionising radiation of 1.9 kpc for photons propagating within the disc plane. Leaking radiation from H ii regions also explains the observed decrease in line ratios of low-ionisation species ([S ii ] / H α , [N ii ] / H α , and [O i ] / H α ) with increasing H α surface brightness ( Σ H α ). Emission from hot low-mass evolved stars, however, is required to explain: (1) the enhanced low-ionisation line ratios observed in the central regions of some of the galaxies in our sample; (2) the observed trends of a ﬂat or decreasing [O iii ] / H β with Σ H α ; and (3) the o ﬀ set of some DIG regions from the typical locus of H ii regions in the Baldwin–Phillips– Terlevich (BPT) diagram, extending into the area of low-ionisation (nuclear) emission-line regions (LI[N]ERs). Hot low-mass evolved stars make a small contribution to the energy budget of the DIG (2% of the galaxy-integrated H α emission), but their harder spectra make them fundamental contributors to [O iii ] emission. The DIG might result from a superposition of two components, an energetically dominant contribution from young stars and a more di ﬀ use background of harder ionising photons from old stars. This uniﬁed framework bridges observations of the Milky Way DIG with LI(N)ER-like emission observed in nearby galaxy bulges.
The AiiDA-Spirit Plugin for Automated Spin-Dynamics Simulations and Multi-Scale Modeling Based on First-Principles Calculations

Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic materials. LLG simulations allow to bridge the gap from expensive quantum mechanical calculations with small unit cells to large supercells where the collective behavior of millions of spins can be studied. In this work we present the AiiDA-Spirit plugin that connects the spin-dynamics code Spirit to the AiiDA framework. AiiDA provides a Python interface that facilitates performing high-throughput calculations while automatically augmenting the calculations with metadata describing the data provenance between calculations in a directed acyclic graph. The AiiDA-Spirit interface thus provides an easy way for high-throughput spin-dynamics calculations. The interface to the AiiDA infrastructure furthermore has the advantage that input parameters for the extended Heisenberg model can be extracted from high-throughput first-principles calculations including a proper treatment of the data provenance that ensures reproducibility of the calculation results in accordance to the FAIR principles. We describe the layout of the AiiDA-Spirit plugin and demonstrate its capabilities using selected examples for LLG spin-dynamics and Monte Carlo calculations. Furthermore, the integration with first-principles calculations through AiiDA is demonstrated at the example of γ–Fe, where the complex spin-spiral ground state is investigated.