Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrafaint Dwarfs in a Milky Way Context: Introducing the Mint Condition DC Justice League Simulations

We present results from the “Mint” resolution DC Justice League suite of Milky Way–like zoom-in cosmological simulations, which extend our study of nearby galaxies down into the ultrafaint dwarf (UFD) regime for the first time. The mass resolution of these simulations is the highest ever published for cosmological Milky Way zoom-in simulations run to z = 0, with initial star (dark matter) particle masses of 994 (17900) M⊙, and a force resolution of 87 pc. We study the surrounding dwarfs and UFDs, and find that the simulations match the observed dynamical properties of galaxies with −3 > MV > −19, and reproduce the scatter seen in the size–luminosity plane for rh ≳ 200 pc. We predict the vast majority of nearby galaxies will be observable by the Vera Rubin Observatory’s coadded Legacy Survey of Space and Time. We additionally show that faint dwarfs with velocity dispersions ≲5 km s−1 result from severe tidal stripping of the host halo. We investigate the quenching of UFDs in a hydrodynamical Milky Way context and find that the majority of UFDs are quenched prior to interactions with the Milky Way, though some of the quenched UFDs retain their gas until infall. Additionally, these simulations yield some unique dwarfs that are the first of their kind to be simulated, e.g., an H i-rich field UFD, a late-forming UFD that has structural properties similar to Crater 2, as well as a compact dwarf satellite that has no dark matter at z = 0.
How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms

SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters $C$ and $\gamma$ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others. There have also been proposals to decouple the selection of $\gamma$ and $C$. We empirically compare 18 of these proposed search algorithms (with different parameterizations for a total of 47 combinations) on 115 real-life binary data sets. We find (among other things) that trees of Parzen estimators and particle swarm optimization select better hyperparameters with only a slight increase in computation time with respect to a grid search with the same number of evaluations. We also find that spending too much computational effort searching the hyperparameters will not likely result in better performance for future data and that there are no significant differences among the different procedures to select the best set of hyperparameters when more than one is found by the search algorithms.
Learning Robust Node Representations on Graphs

Graph neural networks (GNN), as a popular methodology for node representation learning on graphs, currently mainly focus on preserving the smoothness and identifiability of node representations. A robust node representation on graphs should further hold the stability property which means a node representation is resistant to slight perturbations on the input. In this paper, we introduce the stability of node representations in addition to the smoothness and identifiability, and develop a novel method called contrastive graph neural networks (CGNN) that learns robust node representations in an unsupervised manner. Specifically, CGNN maintains the stability and identifiability by a contrastive learning objective, while preserving the smoothness with existing GNN models. Furthermore, the proposed method is a generic framework that can be equipped with many other backbone models (e.g. GCN, GraphSage and GAT). Extensive experiments on four benchmarks under both transductive and inductive learning setups demonstrate the effectiveness of our method in comparison with recent supervised and unsupervised models.
ANGUS: Real-time manipulation of vocal roughness for emotional speech transformations

Vocal arousal, the non-linear acoustic features taken on by human and animal vocalizations when highly aroused, has an important communicative function because it signals aversive states such as fear, pain or distress. In this work, we present a computationally-efficient, real-time voice transformation algorithm, ANGUS, which uses amplitude modulation and time-domain filtering to simulate roughness, an important component of vocal arousal, in arbitrary voice recordings. In a series of 4 studies, we show that ANGUS allows parametric control over the spectral features of roughness like the presence of sub-harmonics and noise; that ANGUS increases the emotional negativity perceived by listeners, to a comparable level as a non-real-time analysis/resynthesis algorithm from the state-of-the-art; that listeners cannot distinguish transformed and non-transformed sounds above chance level; and that ANGUS has a similar emotional effect on animal vocalizations and musical instrument sounds than on human vocalizations. A real-time implementation of ANGUS is made available as open-source software, for use in experimental emotion reseach and affective computing.
A kinetic model of Phanerozoic taxonomic diversity I. Analysis of marine orders

A simple equilibrial model for the growth and maintenance of Phanerozoic global marine taxonomic diversity can be constructed from considerations of the behavior of origination and extinction rates with respect to diversity. An initial postulate that total rate of diversification is proportional to number of taxa extant leads to an exponential model for early phases of diversification. This model appears to describe adequately the “explosive” diversification of known metazoan orders across the Precambrian-Cambrian Boundary, suggesting that no special event, other than the initial appearance of Metazoa, is necessary to explain this phenomenon. As numbers of taxa increase, the rate of diversification should become “diversity dependent. ” Ecological factors should cause the per taxon rate of origination to decline and the per taxon rate of extinction to increase. If these relationships are modeled as simple linear functions, a logistic description of the behavior of taxonomic diversity through time results. This model appears remarkably consistent with the known pattern of Phanerozoic marine ordinal diversity as a whole. Analysis of observed rates of ordinal origination also indicates these are to a large extent diversity dependent; however, diversity dependence is not immediately evident in rates of ordinal extinction. Possible explanations for this pattern are derived from considerations of the size of higher taxa and from simulations of their diversification. These suggest that both the standing diversity and the pattern of origination of orders may adequately reflect the behavior of species diversity through time; however, correspondence between rates of ordinal and species extinction may deteriorate with progressive loss of information resulting from incomplete sampling of the fossil record.
An Energy Efficient Authentication Scheme using Chebyshev Chaotic Map for Smart Grid Environment

As one of the important applications of Smart grid, charging between electric vehicles has attracted much attention. However, authentication between vehicle users and an aggregator may be vulnerable to various attacks due to the usage of wireless communications. In order to reduce the computational costs yet preserve required security, the Chebyshev chaotic map based authentication schemes are proposed. However, the security requirements of Chebyshev polynomials bring a new challenge to the design of authentication schemes based on Chebyshev chaotic maps. To solve this issue, we propose a practical Chebyshev polynomial algorithm by using a binary exponentiation algorithm based on square matrix to achieve secure and efficient Chebyshev polynomial computation. We further apply the proposed algorithm to construct an energy-efficient authentication and key agreement scheme for smart grid environments. Compared with state-of-the-art schemes, the proposed authentication scheme effectively reduces the computational costs and communication costs by adopting the proposed Chebyshev polynomial algorithm. Furthermore, the ProVerif tool is employed to analyze the security of the proposed authentication scheme. Our experimental results justified that our proposed authentication scheme can outperform state-of-the-art schemes in terms of the computational overhead while achieving privacy protection.
Building a large synthetic population from Australian census data

We present work on creating a synthetic population from census data for Australia, applied to the greater Melbourne region. We use a sample-free approach to population synthesis that does not rely on a disaggregate sample from the original population. The inputs for our algorithm are joint marginal distributions from census of desired person-level and household-level attributes, and outputs are a set of comma-separated-value (.csv) files containing the full synthetic population of unique individuals in households; with age, gender, relationship status, household type, and size, matched to census data. Our algorithm is efficient in that it can create the synthetic population for Melbourne comprising 4.5 million persons in 1.8 million households within three minutes on a modern computer. Code for the algorithm is hosted on GitHub.
"My Friend Wanted to Talk About It and I Didn't": Understanding Perceptions of Deletion Privacy in Social Platforms

There is a growing concern and awareness about the right-to-be-forgotten issues among regulators as well as users all over the world. To cope with these privacy concerns, social platforms offer deletion mechanisms that give the users the opportunity to remove their contents and in some cases the platforms remove them automatically. However, this leaves the users vulnerable to attacks by adversaries who specifically seek the damaging content of the users and exploit the act of deletion as a strong signal for it. In this paper, we conduct a user study on 191 participants to study their prior deletion experiences, their expectations of deletion privacy, and how effective do they find the current deletion mechanisms. We find that more than 80\% of the users have deleted at least a social media post and 35\% of the deletions happened after a week of posting. While the participants identified the irrelevancy of the content due to time passing as the main reason for removing their contents, a majority of the participant believe that deletions indicate that the deleted content includes some damaging information to the owner. Importantly, the participants are significantly more concerned about their deletions being noticed by large-scale data collectors (e.g., a third-party data collecting company or the government) than any other individual from their social circle. Further, a third of the participants think that they can be attacked by these large-scale data collectors. Finally, the participants find the current deletion mechanisms to be inadequate in protecting the privacy of their deletions and provide guidelines for the future of deletion mechanisms.
Language Models and Word Sense Disambiguation: An Overview and Analysis

Transformer-based language models have taken many fields in NLP by storm. BERT and its derivatives dominate most of the existing evaluation benchmarks, including those for Word Sense Disambiguation (WSD), thanks to their ability in capturing context-sensitive semantic nuances. However, there is still little knowledge about their capabilities and potential limitations for encoding and recovering word senses. In this article, we provide an in-depth quantitative and qualitative analysis of the celebrated BERT model with respect to lexical ambiguity. One of the main conclusions of our analysis is that BERT performs a decent job in capturing high-level sense distinctions, even when a limited number of examples is available for each word sense. Our analysis also reveals that in some cases language models come close to solving coarse-grained noun disambiguation under ideal conditions in terms of availability of training data and computing resources. However, this scenario rarely occurs in real-world settings and, hence, many practical challenges remain even in the coarse-grained setting. We also perform an in-depth comparison of the two main language model based WSD strategies, i.e., fine-tuning and feature extraction, finding that the latter approach is more robust with respect to sense bias and it can better exploit limited available training data.
Monodromic model for Khovanov–Rozansky homology

Abstract We describe a new geometric model for the Hochschild cohomology of Soergel bimodules based on the monodromic Hecke category studied earlier by the first author and Yun. Moreover, we identify the objects representing individual Hochschild cohomology groups (for the zero and the top degree cohomology this reduces to an earlier result of Gorsky, Hogancamp, Mellit and Nakagane). These objects turn out to be closely related to explicit character sheaves corresponding to exterior powers of the reflection representation of the Weyl group. Applying the described functors to the images of braids in the Hecke category of type A we obtain a geometric description for Khovanov–Rozansky knot homology, essentially different from the one considered earlier by Webster and Williamson.
GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning

This paper proposes GuardNN, a secure deep neural network (DNN) accelerator, which provides strong hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a specific application to provide strong confidentiality and integrity protection with negligible overhead. The design of the GuardNN instruction set reduces the TCB to just the accelerator and enables confidentiality protection without the overhead of integrity protection. GuardNN also introduces a new application-specific memory protection scheme to minimize the overhead of memory encryption and integrity verification. The scheme shows that most of the off-chip meta-data in today's state-of-the-art memory protection can be removed by exploiting the known memory access patterns of a DNN accelerator. GuardNN is implemented as an FPGA prototype, which demonstrates effective protection with less than 2% performance overhead for inference over a variety of modern DNN models.
Status, Challenges and Directions in Indirect Dark Matter Searches

Indirect searches for dark matter are based on detecting an anomalous flux of photons, neutrinos or cosmic-rays produced in annihilations or decays of dark matter candidates gravitationally accumulated in heavy cosmological objects, like galaxies, the Sun or the Earth. Additionally, evidence for dark matter that can also be understood as indirect can be obtained from early universe probes, like fluctuations of the cosmic microwave background temperature, the primordial abundance of light elements or the Hydrogen 21-cm line. The techniques needed to detect these different signatures require very different types of detectors: Air shower arrays, gamma- and X-ray telescopes, neutrino telescopes, radio telescopes or particle detectors in balloons or satellites. While many of these detectors were not originally intended to search for dark matter, they have proven to be unique complementary tools for direct search efforts. In this review we summarize the current status of indirect searches for dark matter, mentioning also the challenges and limitations that these techniques encounter.
On certain Tannakian categories of integrable connections over Kähler manifolds

Abstract Given a compact Kähler manifold X, it is shown that pairs of the form $(E,\, D)$ , where E is a trivial holomorphic vector bundle on X, and D is an integrable holomorphic connection on E, produce a neutral Tannakian category. The corresponding pro-algebraic affine group scheme is studied. In particular, it is shown that this pro-algebraic affine group scheme for a compact Riemann surface determines uniquely the isomorphism class of the Riemann surface.
Effects of Some Plant Growth Regulators on Bud Burst and Rooting ofVitis ViniferaL. CV. Chaush Cuttings

This experiment was carried out to determine the effects of GA 3 , (50, 100, 200 and 400 ppm); CEPA (100, 200, 400 and 800 ppm); CCC and B-9 (500, 1000 and 2000 ppm) on bud burst and rooting of Chaush cv. cuttings taken during imposed dormancy (at the end of February under Central Anatolia conditions). GA 3 at 50 ppm, CEPA at all concentrations, and B-9 at 500 and 1000 ppm markedly delayed bud burst. CCC treatments hastened bud growth significantly. All growth regulators had no significant effect on % bud break. GA 3 treatments and B-9 at 1000 and 2000 ppm definitely inhibited root formation. CEPA treatments markedly delayed root formation, decreased % rooting and fresh root weight per cutting. CCC treatments had no effect on bud burst and % rooting but only 2000 ppm markedly increased fresh root weight per cutting.
Forward Modeling Populations of Flares from Tidal Disruptions of Stars by Supermassive Black Holes

Detections of the tidal disruption flares (TDFs) of stars by supermassive black holes (SMBHs) are rapidly accumulating as optical surveys improve. These detections may provide constraints on SMBH demographics, stellar dynamics, and stellar evolution in galaxies. To maximize this scientific impact, we require a better understanding of how astrophysical parameters interact with survey selection effects in setting the properties of detected flares. We develop a framework for modeling the distributions of optical TDF detections in surveys across attributes of the host galaxies and the flares themselves. This model folds in effects of the stellar disruption rate in each galaxy, the flare luminosity and temperature distributions, the effects of obscuration and reddening by dust in the host galaxy, and survey selection criteria. We directly apply this model to the sample of TDFs detected by the Zwicky Transient Facility, and find that the overall flare detection rate is in line with simple theoretical expectation. The model can also reproduce the distribution of total stellar mass and redshift of the host galaxies, but fails to match all details of the detected flares, such as their luminosity and temperature distributions. We also find that dust obscuration likely plays an important role in suppressing the TDF detection rate in star-forming galaxies. While we do not find that the unusual preference of TDFs to have hosts in post-starburst galaxies in the “green valley” can be entirely explained by selection effects, our model can help to quantify the true rate enhancement in those galaxies.
Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance

We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics, and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik’s wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in seven of nine metrics in three different languages using a single model compared with the common baselines and the best performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.
Risk factors for severe hepatic injury after introduction of highly active antiretroviral therapy.

OBJECTIVES Treatment of HIV infection with highly antiretroviral therapy (HAART) may be limited by liver toxicity. Its incidence and risk factors are not well known. PATIENTS AND METHODS Retrospective chart review. Naive patients beginning HAART between January 1997 and January 2000. Severe transaminase elevation was defined as fivefold or higher rise over upper normal limits, or as > or =3.5-fold rise above abnormal baseline values. RESULTS Of 222 study subjects, 38%, 5%, and 2% were coinfected with hepatitis C virus (HCV), hepatitis B virus, and hepatitis D virus, respectively. Besides two nucleoside reverse transcriptase inhibitors (NRTIs), 96 patients received protease inhibitors (PIs), 90 received nonnucleoside reverse transcriptase inhibitors (NNRTIs), and 35 received a PI + NNRTI combination. Severe hepatic injury developed in 21 (9%): 10% PI, 9%, and 9% PI + NNRTI. Both univariate and multivariate analyses identified alcohol abuse, HCV coinfection, and older age as independent risk factors. Predictor variables in the final multivariate model were: alcohol abuse (risk ratio [RR], 5.87; 95% confidence interval [CI], 1.49-23.15; p =.01], positive HCV serology (RR, 3.99; 95% CI, 1.32-12.10; p =.01], and older age (RR, 1.11; 95% CI, 1.04-1.18; p = 0.001). CONCLUSIONS Nearly 10% of study subjects who start HAART experience severe transaminase elevation, irrespective of the treatment. Avoidance of alcohol abuse, especially in study subjects coinfected with HCV, will reduce the risk of hepatic injury after HAART. When possible, prior treatment for chronic HCV infection should be considered.
Stock Returns and Investors’ Mood: Good Day Sunshine or Spurious Correlation?

This paper examines the validity of statistical significance reported in the seminal studies of the weather effect on stock return. It is found that their research design is statistically flawed and seriously biased against the null hypothesis of no effect. This, coupled with the test statistics inflated by massive sample sizes, strongly suggests that the statistical significance is spurious as an outcome of Type I error. The alternatives to the p-value criterion for statistical significance soundly support the null hypothesis of no weather effect. As an application, the effect of daily sunspot numbers on stock return is examined. Under the same research design as that of a seminal study, the number of sunspots is found to be highly statistically significant although its economic impact on stock return is negligible.
Basis and Application of Simple Interaction Formula for Steel Frames under Fire Conditions

For a long time, the Rankine method has been applied successfully to steel columns and frames subjected to increasing loads but maintained at constant ambient temperature. This paper extends the Rankine formula to frames under fire conditions. An original theoretical derivation of the formula, which is general and based on the virtual work principle, is provided in this paper. A simple expression has also been derived for the buckling coefficient of frames under fire conditions, which takes the deterioration of steel properties at elevated temperature into consideration. Comparison of test results shows that the Rankine formula yields very good predictions of critical temperatures of frames, with a mean value of 1.01 and a coefficient of variation of 9.2% for a total of 18 frames. Most importantly, it is shown that the fire resistance of steel frames can be obtained from the conventional frame analysis performed at ambient temperature.