Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual spectrometer for radicals vibrations. 1. Polyacenes and fullerenes

A virtual vibrational spectrometer, based on efficient software exploring unrestricted Hartree-Fock approximation, has been proposed to perform computational spectroscopy of radicals. The computational device allows obtaining spectra of IR absorption and Raman scattering of large molecules. A number of test cases involving naphthalene and pentacene as well as fullerenes C60 and C70 will be discussed in order to point out strengths, limitations, and ongoing developments of the spectrometer.
Lite Training Strategies for Portuguese-English and English-Portuguese Translation

Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models. In this work, we investigate the use of pre-trained models, such as T5 for Portuguese-English and English-Portuguese translation tasks using low-cost hardware. We explore the use of Portuguese and English pre-trained language models and propose an adaptation of the English tokenizer to represent Portuguese characters, such as diaeresis, acute and grave accents. We compare our models to the Google Translate API and MarianMT on a subset of the ParaCrawl dataset, as well as to the winning submission to the WMT19 Biomedical Translation Shared Task. We also describe our submission to the WMT20 Biomedical Translation Shared Task. Our results show that our models have a competitive performance to state-of-the-art models while being trained on modest hardware (a single 8GB gaming GPU for nine days). Our data, models and code are available in our GitHub repository.
Emergent particles and gauge fields in quantum matter

ABSTRACT I give a pedagogical introduction to some of the many particles and gauge fields that can emerge in correlated matter. The standard model of materials is built on Landau's foundational principles: adiabatic continuity and spontaneous symmetry breaking. These ideas lead to quasiparticles that inherit their quantum numbers from fundamental particles, Nambu-Goldstone bosons, the Anderson-Higgs mechanism, and topological defects in order parameters. I then describe the modern discovery of physics beyond the standard model. Here, quantum correlations (entanglement) and topology play key roles in defining the properties of matter. This can lead to fractionalised quasiparticles that carry only a fraction of the quantum numbers that define fundamental particles. These particles can have exotic properties: for example Majorana fermions are their own antiparticles, anyons have exchange statistics that are neither bosonic nor fermionic, and magnetic monopoles do not occur in the vacuum. Gauge fields emerge naturally in the description of highly correlated matter and can lead to gauge bosons. Relationships to the standard model of particle physics are discussed.
Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes

As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a field of study that focuses on people's descriptive judgments rather than theoretical prescriptions on morality, we investigate a novel, data-driven approach to machine ethics. We introduce SCRUPLES, the first large-scale dataset with 625,000 ethical judgments over 32,000 real-life anecdotes. Each anecdote recounts a complex ethical situation, often posing moral dilemmas, paired with a distribution of judgments contributed by the community members. Our dataset presents a major challenge to state-of-the-art neural language models, leaving significant room for improvement. However, when presented with simplified moral situations, the results are considerably more promising, suggesting that neural models can effectively learn simpler ethical building blocks. A key take-away of our empirical analysis is that norms are not always clean-cut; many situations are naturally divisive. We present a new method to estimate the best possible performance on such tasks with inherently diverse label distributions, and explore likelihood functions that separate intrinsic from model uncertainty. Data and code are available at https://github.com/allenai/scruples.
Generative View Synthesis: From Single-view Semantics to Novel-view Images

Content creation, central to applications such as virtual reality, can be a tedious and time-consuming. Recent image synthesis methods simplify this task by offering tools to generate new views from as little as a single input image, or by converting a semantic map into a photorealistic image. We propose to push the envelope further, and introduce Generative View Synthesis (GVS), which can synthesize multiple photorealistic views of a scene given a single semantic map. We show that the sequential application of existing techniques, e.g., semantics-to-image translation followed by monocular view synthesis, fail at capturing the scene's structure. In contrast, we solve the semantics-to-image translation in concert with the estimation of the 3D layout of the scene, thus producing geometrically consistent novel views that preserve semantic structures. We first lift the input 2D semantic map onto a 3D layered representation of the scene in feature space, thereby preserving the semantic labels of 3D geometric structures. We then project the layered features onto the target views to generate the final novel-view images. We verify the strengths of our method and compare it with several advanced baselines on three different datasets. Our approach also allows for style manipulation and image editing operations, such as the addition or removal of objects, with simple manipulations of the input style images and semantic maps respectively. Visit the project page at this https URL.
Chirality induced topological nature of electrons in DNA-like materials

Topological aspects of DNA and similar chiral molecules are commonly discussed in geometry, while their electronic structure's topology is less explored. Recent experiments revealed that DNA could efficiently filter spin-polarized electrons, called the chiral-induced spin-selectivity (CISS). However, the underlying correlation between chiral structure and electronic spin remains elusive. In this work, we reveal an orbital texture in the band structure, a topological characteristic induced by the chirality. We find that the orbital texture enables the chiral molecule to polarize the quantum orbital, called the orbital polarization effect (OPE). The OPE induces spin polarization assisted by the spin-orbit interaction only from the metal contact and also leads to magnetism-dependent conductance and chiral separation. Beyond CISS, we predict that OPE can also induce spin-selective phenomena even in achiral but inversion-breaking materials.
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.
On $\ell_p$-norm Robustness of Ensemble Stumps and Trees

Recent papers have demonstrated that ensemble stumps and trees could be vulnerable to small input perturbations, so robustness verification and defense for those models have become an important research problem. However, due to the structure of decision trees, where each node makes decision purely based on one feature value, all the previous works only consider the $\ell_\infty$ norm perturbation. To study robustness with respect to a general $\ell_p$ norm perturbation, one has to consider the correlation between perturbations on different features, which has not been handled by previous algorithms. In this paper, we study the problem of robustness verification and certified defense with respect to general $\ell_p$ norm perturbations for ensemble decision stumps and trees. For robustness verification of ensemble stumps, we prove that complete verification is NP-complete for $p\in(0, \infty)$ while polynomial time algorithms exist for $p=0$ or $\infty$. For $p\in(0, \infty)$ we develop an efficient dynamic programming based algorithm for sound verification of ensemble stumps. For ensemble trees, we generalize the previous multi-level robustness verification algorithm to $\ell_p$ norm. We demonstrate the first certified defense method for training ensemble stumps and trees with respect to $\ell_p$ norm perturbations, and verify its effectiveness empirically on real datasets.
The Power of Hashing with Mersenne Primes

The classic way of computing a $k$-universal hash function is to use a random degree-$(k-1)$ polynomial over a prime field $\mathbb Z_p$. For a fast computation of the polynomial, the prime $p$ is often chosen as a Mersenne prime $p=2^b-1$. In this paper, we show that there are other nice advantages to using Mersenne primes. Our view is that the output of the hash function is a $b$-bit integer that is uniformly distributed in $[2^b]$, except that $p$ (the all \texttt1s value) is missing. Uniform bit strings have many nice properties, such as splitting into substrings which gives us two or more hash functions for the cost of one, while preserving strong theoretical qualities. We call this trick "Two for one" hashing, and we demonstrate it on 4-universal hashing in the classic Count Sketch algorithm for second moment estimation. We also provide a new fast branch-free code for division and modulus with Mersenne primes. Contrasting our analytic work, this code generalizes to Pseudo-Mersenne primes $p=2^b-c$ for small $c$, improving upon a classical algorithm of Crandall.
State Observation of LTV Systems with Delayed Measurements: A Parameter Estimation-based Approach

In this paper we address the problem of state observation of linear time-varying systems with delayed measurements, which has attracted the attention of many researchers|see [7] and references therein. We show that, adopting the parameter estimationbased approach proposed in [3,4], we can provide a very simple solution to the problem with reduced prior knowledge.
A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards

In recent years, machine learning has seen an increasing presencein a large variety of fields, especially in health care and bioinformatics.More specifically, the field where machine learning algorithms have found most applications is Genetic Algorithms.The objective of this paper is to conduct a survey of articles published from 2015 onwards that deal with Genetic Algorithms(GA) and how they are used in this http URL achieve the objective, a scoping review was conducted that utilized Google Scholar alongside Publish or Perish and the Scimago Journal & CountryRank to search for respectable sources. Upon analyzing 31 articles from the field of bioinformatics, it became apparent that genetic algorithms rarely form a full application, instead they rely on other vital algorithms such as support vector machines.Indeed, support vector machines were the most prevalent algorithms used alongside genetic algorithms; however, while the usage of such algorithms contributes to the heavy focus on accuracy by GA programs, it often sidelines computation times in the process. In fact, most applications employing GAs for classification and feature selectionare nearing or at 100% success rate, and the focus of future GA development should be directed elsewhere. Population-based searches, like GA, are often combined with other machine learning algorithms. In this scoping review, genetic algorithms combined with Support Vector Machines were found to perform best. The performance metric that was evaluated most often was accuracy. Measuring the accuracy avoids measuring the main weakness of GAs, which is computational time. The future of genetic algorithms could be open-ended evolutionary algorithms, which attempt to increase complexity and find diverse solutions, rather than optimize a fitness function and converge to a single best solution from the initial population of solutions.
High-Performance Simultaneous Multiprocessing for Heterogeneous System-on-Chip

This paper presents a methodology for simultaneous heterogeneous computing, named ENEAC, where a quad core ARM Cortex-A53 CPU works in tandem with a preprogrammed on-board FPGA accelerator. A heterogeneous scheduler distributes the tasks optimally among all the resources and all compute units run asynchronously, which allows for improved performance for irregular workloads. ENEAC achieves up to 17\% performance improvement \ignore{and 14\% energy usage reduction,} when using all platform resources compared to just using the FPGA accelerators and up to 865\% performance increase \ignore{and up to 89\% energy usage decrease} when using just the CPU. The workflow uses existing commercial tools and C/C++ as a single programming language for both accelerator design and CPU programming for improved productivity and ease of verification.
RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces

This paper presents the novel Riemannian Fusion Network (RFNet), a deep neural architecture for learning spatial and temporal information from Electroencephalogram (EEG) for a number of different EEG-based Brain Computer Interface (BCI) tasks and applications. The spatial information relies on Spatial Covariance Matrices (SCM) of multi-channel EEG, whose space form a Riemannian Manifold due to the Symmetric and Positive Definite structure. We exploit a Riemannian approach to map spatial information onto feature vectors in Euclidean space. The temporal information characterized by features based on differential entropy and logarithm power spectrum density is extracted from different windows through time. Our network then learns the temporal information by employing a deep long short-term memory network with a soft attention mechanism. The output of the attention mechanism is used as the temporal feature vector. To effectively fuse spatial and temporal information, we use an effective fusion strategy, which learns attention weights applied to embedding-specific features for decision making. We evaluate our proposed framework on four public datasets from three popular fields of BCI, notably emotion recognition, vigilance estimation, and motor imagery classification, containing various types of tasks such as binary classification, multi-class classification, and regression. RFNet approaches the state-of-the-art on one dataset (SEED) and outperforms other methods on the other three datasets (SEED-VIG, BCI-IV 2A, and BCI-IV 2B), setting new state-of-the-art values and showing the robustness of our framework in EEG representation learning.
The MOSDEF Survey: Neon as a Probe of ISM Physical Conditions at High Redshift

We present results on the properties of neon emission in z ∼ 2 star-forming galaxies drawn from the MOSFIRE Deep Evolution Field (MOSDEF) survey. Doubly ionized neon ([Ne iii]λ3869) is detected at ≥3σ in 61 galaxies, representing ∼25% of the MOSDEF sample with Hα, Hβ, and [O iii]λ5007 detections at similar redshifts. We consider the neon emission-line properties of both individual galaxies with [Ne iii]λ3869 detections and composite z ∼ 2 spectra binned by stellar mass. With no requirement of [Ne iii]λ3869 detection, the latter provide a more representative picture of neon emission-line properties in the MOSDEF sample. The [Ne iii]λ3869/[O ii]λ3727 ratio (Ne3O2) is anticorrelated with stellar mass in z ∼ 2 galaxies, as expected based on the mass–metallicity relation. It is also positively correlated with the [O iii]λ5007/[O ii]λ3727 ratio (O32), but z ∼ 2 line ratios are offset toward higher Ne3O2 at fixed O32, compared with both local star-forming galaxies and individual H ii regions. Despite the offset toward higher Ne3O2 at fixed O32 at z ∼ 2, biases in inferred Ne3O2-based metallicity are small. Accordingly, Ne3O2 may serve as an important metallicity indicator deep into the reionization epoch. Analyzing additional rest-optical line ratios including [Ne iii]λ3869/[O iii]λ5007 (Ne3O3) and [O iii]λ5007/Hβ (O3Hβ), we conclude that the nebular emission-line ratios of z ∼ 2 star-forming galaxies suggest a harder ionizing spectrum (lower stellar metallicity, i.e., Fe/H) at fixed gas-phase oxygen abundance, compared to systems at z ∼ 0. These new results based on neon lend support to the physical picture painted by oxygen, nitrogen, hydrogen, and sulfur emission of an ionized interstellar medium in high-redshift star-forming galaxies irradiated by chemically young, α-enhanced massive stars.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.
Age Structure in a Population of Red-Spotted Newts from the Allegheny Plateau of Maryland

The red-spotted newt, Notophthalmus viridescens, is widely distributed in North America east of a line stretching from southern Quebec to the upper Gulf Coast of Texas (Mecham, 1967; Conant, 1975). The species exhibits a complex life history which includes an aquatic larva, a terrestrial juvenile (eft), and an aquatic adult. The duration of these life history stages is variable. In upland populations, larvae characteristically hatch during the spring and summer and engage in post-larval migrations during the late summer and early fall (Hurlbert, 1970; Healy, 1975a; Gill, 1978a, b). Juveniles spend from 2-7 years as terrestrial efts before returning to the aquatic habitat to spend 1-9 years as mature, sexually active adults (Hurlbert, 1969; Pough, 1971; Healy, 1975b; Gill, 1978a, b, 1985). In coastal plain populations, the terrestrial eft phase is rare and in some localities may be omitted altogether (Noble, 1926, 1929; Healy, 1975a). In these populations, larvae metamorphose directly into adults and reach sexual maturity after two years. Historically, estimations pertaining to the age structure of various populations of the red-spotted newt have been based on one of two techniques: (1) a comparison of size-frequency histograms derived from sequential field collections; or (2) multi-year mark and recapture techniques. In the first technique, accuracy is influenced by variability in individual growth rate. The growth rate of newts comprising each of the life history stages is directly proportional to the amount of food consumed (Springer, 1909). Food consumption depends upon resource availability and the intensity and duration of individual activity. The activity of terrestrial efts is positively correlated with humidity and temperature (Healy, 1973), while that of aquatic juveniles and adults is inversely correlated with temperature (Springer, 1909). The use of a mark and recapture technique to estimate age structure maximizes accuracy, but requires a prolonged and often impractical time commitment (see Gill, 1985). In this report we apply the technique of skeletochronology to estimate the age structure and relative duration of the eft and adult life history stages of redspotted newts taken during a single sampling event. Seventy-six red-spotted newts (10 larvae, 36 efts and 30 adults) were collected from the Blue Ridge Physiographic Province of western Maryland (elevation 800 m) during June, 1984. The site consisted of a eutrophied farm pond surrounded by steep slopes covered by a second-growth, mixed mesophytic forest. Larvae and adults were collected from the shal-
The Garrison-Wong quantum phase operator revisited

We revisit the quantum phase operator $\Phi$ introduced by Garrison and Wong. Denoting by $N$ the number operator, we provide a detailed proof of the Heisenberg commutation relation $\Phi N - N\Phi = iI$ on the natural maximal domain $D(\Phi N) \cap D(N \Phi)$ as well as the failure of the Weyl commutation relations, and discuss some further interesting properties of this pair.
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In this survey article, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in DL are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.