Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitigating Gender Bias Amplification in Distribution by Posterior Regularization

Advanced machine learning techniques have boosted the performance of natural language processing. Nevertheless, recent studies, e.g., (CITATION) show that these techniques inadvertently capture the societal bias hidden in the corpus and further amplify it. However, their analysis is conducted only on models’ top predictions. In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels. We further propose a bias mitigation approach based on posterior regularization. With little performance loss, our method can almost remove the bias amplification in the distribution. Our study sheds the light on understanding the bias amplification.
Boosting on the shoulders of giants in quantum device calibration

Traditional machine learning applications, such as optical character recognition, arose from the inability to explicitly program a computer to perform a routine task. In this context, learning algorithms usually derive a model exclusively from the evidence present in a massive dataset. Yet in some scientific disciplines, obtaining an abundance of data is an impractical luxury, however; there is an explicit model of the domain based upon previous scientific discoveries. Here we introduce a new approach to machine learning that is able to leverage prior scientific discoveries in order to improve generalizability over a scientific model. We show its efficacy in predicting the entire energy spectrum of a Hamiltonian on a superconducting quantum device, a key task in present quantum computer calibration. Our accuracy surpasses the current state-of-the-art by over $20\%. $ Our approach thus demonstrates how artificial intelligence can be further enhanced by "standing on the shoulders of giants. "
The infinite-dimensional HaPPY code: entanglement wedge reconstruction and dynamics

We construct an infinite-dimensional analog of the HaPPY code as a growing series of stabilizer codes defined respective to their Hilbert spaces. The Hilbert spaces are related by isometric maps, which we define explicitly. We construct a Hamiltonian that is compatible with the infinite-dimensional HaPPY code and further study the stabilizer of our code, which has an inherent fractal structure. We use this result to study the dynamics of the code and map a nontrivial bulk Hamiltonian to the boundary. We find that the image of the mapping is scale invariant, but does not create any long-range entanglement in the boundary, therefore failing to reproduce the features of a CFT. This result shows the limits of the HaPPY code as a model of the AdS/CFT correspondence, but also hints that the relevance of quantum error correction in quantum gravity may not be limited to the CFT context.
$L_f^p$ harmonic 1-forms on complete non-compact smooth metric measure spaces

This paper studies complete non-compact smooth metric measure space $(M^n,g,\mathrm{e}^{-f}\mathrm{d}v)$ with positive first spectrum $\lambda_1(\Delta_f)$ or satisfying a weighted Poincare inequality with weight function $\rho$. We establish two splitting and vanishing theorems for $L_f^p$ harmonic $1$-forms under the assumption that $m$-Bakry-Emery Ricci curvature $\mathrm{Ric}_{m,n}\geq -a\lambda_1(\Delta_f)$ or $\mathrm{Ric}_{m,n}\geq -a\rho-b$ for particular constants $a$ and $b>0$. These results are inspired by the work of Han-Lin and are $L_f^p$ generalizations of previous works by Dung-Sung and Vieira for $L^2$ harmonic $1$-forms.
What is the potential of spread in invasive bryophytes

Although the number of invasive bryophytes is much lower than that of higher plants, they threaten habitats that are often species rich and of high conservation relevance. Their potential of spread has, however, never been determined. Here, we assess whether the three most invasive bryophyte species shifted their niche during the invasion process and whether the extent of the study area defined to calibrate the model (geographic background, GB) affects model transferability. We then determine whether ecological niche models (ENMs) developed in their native range can be projected in other areas to assess their invasive potential. The macroclimatic niches of Campylopus introflexus, Orthodontium lineare and Lophocolea semiteres were compared in their native range (Southern Hemisphere) and in their invasion range (Northern Hemisphere) using ordination techniques. ENMs from an ensemble model were calibrated in the native range and projected onto the Northern Hemisphere using different GBs. No evidence for niche expansion in the invaded range was found and the species occur in the invaded range under climate conditions that are similar to those in the native range. The performance of the models to predict occurrences in the invaded range increased with the extent of the GB. The potential range of all species included entire regions on continents where they are still absent. The expansion of the investigated species appears to be constrained by climate conditions that are similar to those currently prevailing in their native range, which is consistent with our failure to demonstrate macroclimatic niche shift in the invaded range. The use of large GBs is recommended in such vagile organisms with large, disjunct distributions. The models indicated that invasive bryophyte species might become a threat in central and eastern Europe, North America and eastern Asia if accidentally introduced or naturally dispersed.
A Time Splitting Method for the Three-Dimensional Linear Pauli Equation

Abstract We analyze a numerical method to solve the time-dependent linear Pauli equation in three space dimensions. The Pauli equation is a semi-relativistic generalization of the Schrödinger equation for 2-spinors which accounts both for magnetic fields and for spin, with the latter missing in preceding numerical work on the linear magnetic Schrödinger equation. We use a four term operator splitting in time, prove stability and convergence of the method and derive error estimates as well as meshing strategies for the case of given time-independent electromagnetic potentials, thus providing a generalization of previous results for the magnetic Schrödinger equation.
Activity rhythms of barnacles in relation to respiration and feeding

Records of cirral activity have been made over periods of 24–48 h for individual specimens of eight species of barnacles from three different localities. The effects of movement, gas content, and hydrogen-ion concentration of the water have been studied. In still water most species eventually settle down to a more or less steady rhythm (activity rhythm) of short bursts of cirral beating interspersed with inactive periods. Two exceptions are the surf- or current-loving species Balanus cariosus and Tetraclita squamosa which remain inactive or eventually cease activity. In moving water all species show practically continuous activity, the velocity of the flow needed to produce this response varying according to the species, habitat, and previous history of the specimen. Increased oxygen content has less effect on activity than other factors studied, but eventually appears to reduce activity. A gradual reduction in oxygen content first leads to increased activity and then signs of distress attributable to anoxia.
Automatic Estimation of Inteligibility Measure for Consonants in Speech

In this article, we provide a model to estimate a real-valued measure of the intelligibility of individual speech segments. We trained regression models based on Convolutional Neural Networks (CNN) for stop consonants \textipa{/p,t,k,b,d,g/} associated with vowel \textipa{/A/}, to estimate the corresponding Signal to Noise Ratio (SNR) at which the Consonant-Vowel (CV) sound becomes intelligible for Normal Hearing (NH) ears. The intelligibility measure for each sound is called SNR$_{90}$, and is defined to be the SNR level at which human participants are able to recognize the consonant at least 90\% correctly, on average, as determined in prior experiments with NH subjects. Performance of the CNN is compared to a baseline prediction based on automatic speech recognition (ASR), specifically, a constant offset subtracted from the SNR at which the ASR becomes capable of correctly labeling the consonant. Compared to baseline, our models were able to accurately estimate the SNR$_{90}$~intelligibility measure with less than 2 [dB$^2$] Mean Squared Error (MSE) on average, while the baseline ASR-defined measure computes SNR$_{90}$~with a variance of 5.2 to 26.6 [dB$^2$], depending on the consonant.
Detecting and Analyzing Collusive Entities on YouTube

YouTube sells advertisements on the posted videos, which in turn enables the content creators to monetize their videos. As an unintended consequence, this has proliferated various illegal activities such as artificial boosting of views, likes, comments, and subscriptions. We refer to such videos (gaining likes and comments artificially) and channels (gaining subscriptions artificially) as “collusive entities. ” Detecting such collusive entities is an important yet challenging task. Existing solutions mostly deal with the problem of spotting fake views, spam comments, fake content, and so on, and oftentimes ignore how such fake activities emerge via collusion. Here, we collect a large dataset consisting of two types of collusive entities on YouTube—videos submitted to gain collusive likes and comment requests and channels submitted to gain collusive subscriptions. We begin by providing an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services. Following this, we propose models to detect three types of collusive YouTube entities: videos seeking collusive likes, channels seeking collusive subscriptions, and videos seeking collusive comments. The third type of entity is associated with temporal information. To detect videos and channels for collusive likes and subscriptions, respectively, we utilize one-class classifiers trained on our curated collusive entities and a set of novel features. The SVM-based model shows significant performance with a true positive rate of 0.911 and 0.910 for detecting collusive videos and collusive channels, respectively. To detect videos seeking collusive comments, we propose CollATe, a novel end-to-end neural architecture that leverages time-series information of posted comments along with static metadata of videos. CollATe is composed of three components: metadata feature extractor (which derives metadata-based features from videos), anomaly feature extractor (which utilizes the time-series data to detect sudden changes in the commenting activity), and comment feature extractor (which utilizes the text of the comments posted during collusion and computes a similarity score between the comments). Extensive experiments show the effectiveness of CollATe (with a true positive rate of 0.905) over the baselines.
Impacts of Active Oil Pumps and Deer Feed Plots on Amphibian and Reptile Assemblages in a Floodplain

Abstract Floodplain forests are among the most threatened habitats in North America. We used the richness, abundance and diversity of amphibians and reptiles to assess the effects of habitat modifications at the Old Sabine Bottom Wildlife Management Area (OSBWMA) in Smith County, Texas, USA. Amphibians and reptiles were surveyed using artificial cover objects and minnow traps at nine ephemeral pools: three in undisturbed bottomland forest, three in deer food plots and three near active oil pumps. A total of 627 amphibians and reptiles representing 31 species was captured. Undisturbed forest plots sustained a higher amphibian richness compared to food and oil pump plots. Amphibian abundance differed significantly among habitats with food plots having the highest and oil pump plots the lowest. Reptile abundance was not significantly different among habitat types neither was reptile richness following rarefaction correction. Overall, each habitat type had similar Simpson's diversity indices, ranging from 0.852–0.894 for total amphibians and reptiles. The community compositions in the three habitat types were significantly different. In general, the food plots had little impact on the amphibians and reptiles in this floodplain however oil pump plots appeared to be avoided. Overall, species presence varied according to the type of habitat modification which should be considered before implementing management plans.
Realistic simulation of quantum computation using unitary and measurement channels

The implementation and practicality of quantum algorithms hinge largely on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing these algorithms. Existing classical simulation techniques of quantum information processing devices exhibit a tradeoff between scalability (the number of qubits that can be simulated) and accuracy (how close the simulation is to the target error model). In this paper, we introduce a simulation approach that relies on approximating the density matrix evolution with a stochastic sum of unitary and measurement channels within a pure-state simulation environment. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known stochastic approaches while allowing us to simulate a larger number of qubits compared to the exact density matrix simulation. Furthermore, we used this approach to realistically simulate Grover's algorithm and the surface code 17 using a gate set tomography characterization of quantum operations as a noise model.
Importance of the Fermi surface and magnetic interactions for the superconducting dome in electron-doped FeSe intercalates

The van-der-Waals gap of iron chalcogenide superconductors can be intercalated with a variety of inorganic and organic compounds that modify the electron doping level of the iron layers. In Lix(C3N2H10)0.37FeSe, a dome in the superconducting transition temperature Tc has been reported to occur in the doping range of x=0.06 to x=0.68. We use a combination of density functional theory and spin fluctuation theory to capture the evolution of superconducting transition temperatures theoretically. We clearly demonstrate how the changing electronic structure supports an increasing superconducting Tc. The suppression of Tc at high doping levels can, however, only be understood by analyzing the magnetic tendencies, which evolve from stripe-type at low doping to bicollinear at high doping.
Dimension Free Estimates for the Riesz Transforms Associated with some Fractional Operators

In these notes, boundedness properties of the Riesz transforms associated with symmetric $\alpha$-stable probability measures, $\alpha \in (1,2)$, are investigated on appropriate $L^p$ spaces, for $p \in (1,+\infty)$. Our approach is based on Bismut-type formulae in order to obtain useful representations for the different Riesz transforms. In the Euclidean setting, the method of transference and one-dimensional multiplier theory combined with fine properties of stable distributions imply dimension free estimate for the fractional Laplacian. A limiting argument as $\alpha$ tends to $2^-$ then recovers the well-known result for the standard Laplacian. In the non-Euclidean setting, a regularization phenomenon, specific to the non-Gaussian stable case, provides the boundedness result as well as a dimension free estimate when the reference measure is the rotationally invariant $\alpha$-stable probability measure on $\mathbb{R}^d$.
Low-Resolution ADC Quantized Full-Duplex Massive MIMO-Enabled Wireless Backhaul in Heterogeneous Networks Over Rician Channels

This paper studies the spectral/energy efficiency (SE/EE) of a heterogeneous network with the backhaul enabled by low-resolution analog-to-digital converters (ADCs) quantized full-duplex massive multiple-input multiple-output (MIMO) over Rician channels. Backhaul communication is completed over two phases. During the first phase, the macro-cell (MC) base station (BS) deploys massive receive antennas and a few transmit antennas; the small-cell (SC) BSs employ large-scale receive antennas and a single transmit antenna. For the second phase, the roles of the transmit and receive antennas are switched. Due to the low-resolution ADCs, we account for quantization noise (QN). We characterize the joint impact of the number of antennas, self-interference, SC-to-SC interference, QN, and Rician <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-factor. For the first phase, the SE is enhanced with the massive receive antennas and the loss due to QN is limited. For the second phase, the desired signal and QN have the same order. Therefore, the SE saturates with the massive transmit antennas. As the Rician <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-factor increases, the SE converges. Power scaling laws are derived to demonstrate that the transmit power can be scaled down proportionally to the massive antennas. We investigate the EE/SE trade-offs. The envelope of the EE/SE region grows with increase in the Rician <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-factor.
Testing Positive Semi-Definiteness via Random Submatrices

We study the problem of testing whether a matrix $\mathrm{A}\in \mathbb{R}^{n\times n}$ with bounded entries ($\Vert \mathrm{A}\Vert_{\infty}\leq 1$) is positive semidefinite (PSD), or $\epsilon$-far in Euclidean distance from the PSD cone, meaning that $\min\nolimits_{\mathrm{B}\succeq 0}\Vert \mathrm{A}-\mathrm{B}\Vert_{F}^{2} > \epsilon n^{2}$, where $\mathrm{B}\succeq 0$ denotes that B is PSD. Our main algorithmic contribution is a non-adaptive tester which distinguishes between these cases using only $\tilde{O}(1/\epsilon^{4})$ queries to the entries of A.11Throughout the paper, $\tilde{O}(\cdot)$ hides $\log(1/\epsilon)$ factors. If instead of the Eucledian norm we considered the distance in spectral norm, we obtain the “$\ell_{\infty}$-gap problem”, where A is either PSD or satisfies $\min\nolimits_{\mathrm{B}\succ 0}\Vert \mathrm{A}-\mathrm{B}\Vert_{2} > \epsilon n$. For this related problem, we give a $\tilde{O}(1/\epsilon^{2})$ query tester, which we show is optimal up to $\log(1/\epsilon)$ factors. Both our testers randomly sample a collection of principal sub-matrices and check whether these sub-matrices are PSD. Consequentially, our algorithms achieve one-sided error: whenever they output that A is not PSD, they return a certificate that A has negative eigenvalues. We complement our upper bound for PSD testing with Eucledian norm distance by giving a $\tilde{\Omega}(1/\epsilon^{2})$ lower bound for any non-adaptive algorithm. Our lower bound construction is general, and can be used to derive lower bounds for a number of spectral testing problems. As an example of the applicability of our construction, we obtain a new $\tilde{\Omega}(1/\epsilon^{4})$ sampling lower bound for testing the Schatten-1 norm with a $\epsilon n^{1.5}$ gap, extending a result of Balcan, Li, Woodruff, and Zhang [11]. In addition, our hard instance results in new sampling lower bounds for estimating the Ky-Fan Norm, and the cost of rank-$k$ approximations, i.e. $\Vert\mathrm{A}-\mathrm{A}_{k}\Vert_{F}^{2}=\sum\nolimits_{i > k}\sigma_{i}^{2}(\mathrm{A})$.
Adversarial examples are useful too!

Deep learning has come a long way and has enjoyed an unprecedented success. Despite high accuracy, however, deep models are brittle and are easily fooled by imperceptible adversarial perturbations. In contrast to common inference-time attacks, Backdoor (\aka Trojan) attacks target the training phase of model construction, and are extremely difficult to combat since a) the model behaves normally on a pristine testing set and b) the augmented perturbations can be minute and may only affect few training samples. Here, I propose a new method to tell whether a model has been subject to a backdoor attack. The idea is to generate adversarial examples, targeted or untargeted, using conventional attacks such as FGSM and then feed them back to the classifier. By computing the statistics (here simply mean maps) of the images in different categories and comparing them with the statistics of a reference model, it is possible to visually locate the perturbed regions and unveil the attack.
A Low Incidence of Mid-infrared Variability in Dwarf Galaxies

Using 8.4 yr of photometry from the AllWISE/NEOWISE multi-epoch catalogs, we compare the mid-infrared variability properties of a sample of 2197 dwarf galaxies (M⋆ < 2 × 109 h−2 M☉) to a sample of 6591 more massive galaxies (M⋆ ≥ 1010 h−2 M☉) matched in mid-infrared apparent magnitude. We find only two dwarf galaxies with mid-infrared variability, a factor of ∼10 less frequent than the more massive galaxies (p = 6 × 10−6), consistent with previous findings of optical variability in low-mass and dwarf galaxies using data with a similar baseline and cadence. Within the more massive control galaxy population, we see no evidence for a stellar mass dependence of mid-infrared variability, suggesting that this apparent reduction in the frequency of variable objects occurs below a stellar mass of ∼1010 h−2 M☉. Compared to the more massive galaxies, active galactic nuclei (AGNs) selected in dwarf galaxies using either their mid-infrared color or optical emission-line classification are systematically missed by variability selection. Our results suggest, in agreement with previous optical studies at similar cadence, that variability selection of AGNs in dwarf galaxies is ineffective unless higher-cadence data are used.
Photoionizing feedback in spiral arm molecular clouds

We present simulations of a 500 pc$^2$ region, containing gas of mass 4 $\times$ 10$^6$ M$_\odot$, extracted from an entire spiral galaxy simulation, scaled up in resolution, including photoionising feedback from stars of mass > 18 M$_\odot$. Our region is evolved for 10 Myr and shows clustered star formation along the arm generating $\approx$ 5000 cluster sink particles $\approx$ 5% of which contain at least one of the $\approx$ 4000 stars of mass > 18 M$_\odot$. Photoionisation has a noticeable effect on the gas in the region, producing ionised cavities and leading to dense features at the edge of the HII regions. Compared to the no-feedback case, photoionisation produces a larger total mass of clouds and clumps, with around twice as many such objects, which are individually smaller and more broken up. After this we see a rapid decrease in the total mass in clouds and the number of clouds. Unlike studies of isolated clouds, our simulations follow the long range effects of ionisation, with some already-dense gas becoming compressed from multiple sides by neighbouring HII regions. This causes star formation that is both accelerated and partially displaced throughout the spiral arm with up to 30% of our cluster sink particle mass forming at distances > 5 pc from sites of sink formation in the absence of feedback. At later times, the star formation rate decreases to below that of the no-feedback case.