Miracle-dz/UniFAR-FTUs huggingface dataset:
Interval Privacy: A Framework for Data Collection

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data transparent and acceptable to data owners. We present a new concept of privacy and corresponding data formats, mechanisms, and tradeoffs for privatizing data during data collection. The privacy, named Interval Privacy, enforces the raw data conditional distribution on the privatized data to be the same as its unconditional distribution over a nontrivial support set. Correspondingly, the proposed privacy mechanism will record each data value as a random interval containing it. The proposed interval privacy mechanisms can be easily deployed through most existing survey-based data collection paradigms, e.g., by asking a respondent whether its data value is within a randomly generated range. Another unique feature of interval mechanisms is that they obfuscate the truth but not distort it. The way of using narrowed range to convey information is complementary to the popular paradigm of perturbing data. Also, the interval mechanisms can generate progressively reﬁned information at the discretion of individual respondents. We study different theoretical aspects of the proposed privacy. In the context of supervised learning, we also offer a method such that existing supervised learning algorithms designed for point-valued data could be directly applied to learning from interval-valued data.
Layer Pruning on Demand with Intermediate CTC

Deploying an end-to-end automatic speech recognition (ASR) model on mobile/embedded devices is a challenging task, since the device computational power and energy consumption requirements are dynamically changed in practice. To overcome the issue, we present a training and pruning method for ASR based on the connectionist temporal classification (CTC) which allows reduction of model depth at run-time without any extra fine-tuning. To achieve the goal, we adopt two regularization methods, intermediate CTC and stochastic depth, to train a model whose performance does not degrade much after pruning. We present an in-depth analysis of layer behaviors using singular vector canonical correlation analysis (SVCCA), and efficient strategies for finding layers which are safe to prune. Using the proposed method, we show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU, while each pruned sub-model maintains the accuracy of individually trained model of the same depth.
Improving On-Screen Sound Separation for Open Domain Videos with Audio-Visual Self-attention

We introduce a state-of-the-art audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking at in-the-wild videos. We identify limitations of previous work on audio-visual on-screen sound separation, including the simplicity and coarse resolution of spatio-temporal attention, and poor convergence of the audio separation model. Our proposed model addresses these issues using cross-modal and self-attention modules that capture audio-visual dependencies at a finer resolution over time, and by unsupervised pre-training of audio separation model. These improvements allow the model to generalize to a much wider set of unseen videos. We also show a robust way to further improve the generalization capability of our models by calibrating the probabilities of our audio-visual on-screen classifier, using only a small amount of in-domain videos labeled for their on-screen presence. For evaluation and semi-supervised training, we collected human annotations of on-screen audio from a large database of in-the-wild videos (YFCC100m). Our results show marked improvements in on-screen separation performance, in more general conditions than previous methods.
Emergence of near-boundary segregation zones in face-centered cubic multiprincipal element alloys

Grain boundaries have been shown to dramatically inﬂuence the behavior of relatively simple materials such as monatomic metals and binary alloys. The increased chemical complexity associated with multiprincipal element alloys is hypothesized to lead to unique grain boundary phenomena. To explore the relationship between grain boundary structure and chemistry in these materials, hybrid molecular dynamics/Monte Carlo simulations of a faceted (cid:2) 11 (cid:2) 110 (cid:3) tilt boundary, chosen to sample both high- and low-energy boundary conﬁgurations, are performed in face-centered cubic (fcc) CrFeCoNiCu and CrFeCoNi equiatomic alloys. Unexpected enrichment of Fe is discovered in the fcc regions adjacent to the interface and found to be correlated with a structurally distinct region of reduced atomic volume. Comparison with the boundary of the same type in monatomic Cu demonstrates that altered near-boundary regions exist in simpler systems as well, with the chemical complexity of the multiprincipal element alloys highlighting its existence and importance.
Generic Models for Disk-resolved and Disk-integrated Phase-dependent Linear Polarization of Light Reflected from Exoplanets

Similar to the case of solar system planets, reflected starlight from exoplanets is expected to be polarized due to atmospheric scattering, and the net disk-integrated polarization should be nonzero owing to the asymmetrical illumination of the planetary disk. The computation of the disk-integrated reflected flux and its state of polarization involves techniques for the calculation of the local reflection matrices, as well as the numerical recipes for integration over the planetary disks. In this paper, we present a novel approach to calculate the azimuth-dependent reflected intensity vectors at each location on the planetary disk divided into grids. We achieve this by solving the vector radiative transfer equations that describe linear polarization. Our calculations incorporate self-consistent atmospheric models of exoplanets over a wide range of equilibrium temperature, surface gravity, atmospheric composition, and cloud structure. A comparison of the flux and the amount of polarization calculated by considering both single and multiple scattering exhibits the effect of depolarization due to multiple scattering of light depending on the scattering albedo of the atmosphere. We have benchmarked our basic calculations against some of the existing models. We have also presented our models for the hot Jupiter HD 189733 b, indicating the level of precision required by future observations to detect the polarization of this planet in the optical and near-infrared wavelength region. The generic nature and the accuracy offered by our models make them an effective tool for modeling future observations of the polarized light reflected from exoplanets.
Statistical Query Lower Bounds for List-Decodable Linear Regression

We study the problem of list-decodable linear regression, where an adversary can corrupt a majority of the examples. Specifically, we are given a set $T$ of labeled examples $(x, y) \in \mathbb{R}^d \times \mathbb{R}$ and a parameter $0<\alpha<1/2$ such that an $\alpha$-fraction of the points in $T$ are i.i. d. samples from a linear regression model with Gaussian covariates, and the remaining $(1-\alpha)$-fraction of the points are drawn from an arbitrary noise distribution. The goal is to output a small list of hypothesis vectors such that at least one of them is close to the target regression vector. Our main result is a Statistical Query (SQ) lower bound of $d^{\mathrm{poly}(1/\alpha)}$ for this problem. Our SQ lower bound qualitatively matches the performance of previously developed algorithms, providing evidence that current upper bounds for this task are nearly best possible.
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation

Machine learning systems based on minimizing average error have been shown to perform inconsistently across notable subsets of the data, which is not exposed by a low average error for the entire dataset. In consequential social and economic applications, where data represent people, this can lead to discrimination of underrepresented gender and ethnic groups. Given the importance of bias mitigation in machine learning, the topic leads to contentious debates on how to ensure fairness in practice (data bias versus algorithmic bias). Distributionally Robust Optimization (DRO) seemingly addresses this problem by minimizing the worst expected risk across subpopulations. We establish theoretical results that clarify the relation between DRO and the optimization of the same loss averaged on an adequately weighted training dataset. The results cover finite and infinite number of training distributions, as well as convex and non-convex loss functions. We show that neither DRO nor curating the training set should be construed as a complete solution for bias mitigation: in the same way that there is no universally robust training set, there is no universal way to setup a DRO problem and ensure a socially acceptable set of results. We then leverage these insights to provide a mininal set of practical recommendations for addressing bias with DRO. Finally, we discuss ramifications of our results in other related applications of DRO, using an example of adversarial robustness. Our results show that there is merit to both the algorithm-focused and the data-focused side of the bias debate, as long as arguments in favor of these positions are precisely qualified and backed by relevant mathematics known today.
Element Intervention for Open Relation Extraction

Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. In this paper, we revisit the procedure of OpenRE from a causal view. By formulating OpenRE using a structural causal model, we identify that the above-mentioned problems stem from the spurious correlations from entities and context to the relation type. To address this issue, we conduct Element Intervention, which intervene on the context and entities respectively to obtain the underlying causal effects of them. We also provide two specific implementations of the interventions based on entity ranking and context contrasting. Experimental results on unsupervised relation extraction datasets show our method to outperform previous state-of-the-art methods and is robust across different datasets.
How Low Can We Go: Trading Memory for Error in Low-Precision Training

Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-off error and hence larger prediction error. As applications proliferate, users must choose which precision to use to train a new model, and chip manufacturers must decide which precisions to manufacture. We view these precision choices as a hyperparameter tuning problem, and borrow ideas from meta-learning to learn the tradeoff between memory and error. In this paper, we introduce Pareto Estimation to Pick the Perfect Precision (PEPPP). We use matrix factorization to find non-dominated configurations (the Pareto frontier) with a limited number of network evaluations. For any given memory budget, the precision that minimizes error is a point on this frontier. Practitioners can use the frontier to trade memory for error and choose the best precision for their goals.
Design of a prototypical platform for autonomous and connected vehicles

Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since simulated environments will never fully replicate all the aspects that can affect results in the real world. To this end, this paper presents our prototype platform for experimental research on connected and autonomous driving projects. In detail, the paper presents the overall architecture of the vehicle focusing both on mechanical aspects related to remote actuation and sensors set-up and software aspects by means of a comprehensive description of the main algorithms required for autonomous driving as ego-localization, environment perception, motion planning, and actuation. Finally, experimental tests conducted in an urban-like environment are reported to validate and assess the performances of the overall system.
Decentralised Intelligence, Surveillance, and Reconnaissance in Unknown Environments with Heterogeneous Multi-Robot Systems

We present the design and implementation of a decentralised, heterogeneous multi-robot system for performing intelligence, surveillance and reconnaissance (ISR) in an unknown environment. The team consists of functionally specialised robots that gather information and others that perform a mission-specific task, and is coordinated to achieve simultaneous exploration and exploitation in the unknown environment. We present a practical implementation of such a system, including decentralised inter-robot localisation, mapping, data fusion and coordination. The system is demonstrated in an efficient distributed simulation. We also describe an UAS platform for hardware experiments, and the ongoing progress.
Excitonic effects on the third-order nonlinear optical properties of solids: Theory and application

We present a many-body Bethe-Salpeter equation eigenstates based sum-over-states method to calculate the linear and nonlinear optical properties of solids. Excitonic and local field effects are included in the calculations. As applications, we calculate the one-photon absorption, third harmonic generation, degenerate four-wave mixing spectra of solid C60 fullerene. The overall agreement between the theoretical and experimental results is very good for all three calculated spectra. By comparisons with the independent particle approximation based sum-over-states method, we show that excitonic effects mix the independent particle transition peaks to new excitonic ones. The position and intensity of spectral peaks are modified significantly. By tracing the sum-over-states progress, we determine the type of nonlinear polarization resonances for the characteristic peaks of third harmonic generation process, which may clear up a discrepancy in two experimental results.
A recursive Lovász theta number for simplex-avoiding sets

We recursively extend the Lovász theta number to geometric hypergraphs on the unit sphere and on Euclidean space, obtaining an upper bound for the independence ratio of these hypergraphs. As an application we reprove a result in Euclidean Ramsey theory in the measurable setting, namely that every k k -simplex is exponentially Ramsey, and we improve existing bounds for the base of the exponential.
A real-world Markov chain arising in recreational volleyball

Card shuffling models have provided simple motivating examples for the mathematical theory of mixing times for Markov chains. As a complement, we introduce a more intricate realistic model of a certain observable real-world scheme for mixing human players onto teams. We quantify numerically the effectiveness of this mixing scheme over the 7 or 8 steps performed in practice. We give a combinatorial proof of the non-trivial fact that the chain is indeed irreducible.
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression

A fundamental principle of learning theory is that there is a trade-off between the complexity of a prediction rule and its ability to generalize. Modern machine learning models do not obey this paradigm: They produce an accurate prediction even with a perfect fit to the training set. We investigate over-parameterized linear regression models focusing on the minimum norm solution: This is the solution with the minimal norm that attains a perfect fit to the training set. We utilize the recently proposed predictive normalized maximum likelihood (pNML) learner which is the min-max regret solution for the distribution-free setting. We derive an upper bound of this min-max regret which is associated with the prediction uncertainty. We show that if the test sample lies mostly in a subspace spanned by the eigenvectors associated with the large eigenvalues of the empirical correlation matrix of the training data, the model generalizes despite its over-parameterized nature. We demonstrate the use of the pNML regret as a point-wise learnability measure on synthetic data and successfully observe the double-decent phenomenon of the over-parameterized models on UCI datasets.
Assessment of a feed additive consisting of vitamin B6 (pyridoxine hydrochloride) for all animal species for the renewal of its authorisation (Kaesler Nutrition GmbH)

Abstract Following a request from the European Commission, the EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) was asked to deliver a scientific opinion on the assessment of the application for renewal of authorisation of vitamin B6 (pyridoxine hydrochloride) produced by chemical synthesis as a feed additive for all animal species. The applicant has provided data demonstrating that the additive in the market complies with the conditions of the authorisation. The FEEDAP Panel confirms that the use of pyridoxine hydrochloride under the current authorised conditions of use is safe for the target species, the consumers and the environment. Pyridoxine hydrochloride is non‐irritant to skin and eyes. In the absence of data, no conclusion can be drawn on skin sensitisation potential. Pyridoxine hydrochloride may cause photosensitisation. Despite the lack of data on inhalation toxicity, the additive is not dusty and exposure through inhalation is unlikely. There is no need for assessing the efficacy of the additive in the context of the renewal of the authorisation.
Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake

Abstract Genetic structuring of wild populations is dependent on environmental, ecological, and life‐history factors. The specific role environmental context plays in genetic structuring is important to conservation practitioners working with rare species across areas with varying degrees of fragmentation. We investigated fine‐scale genetic patterns of the federally threatened Eastern Massasauga Rattlesnake (Sistrurus catenatus) on a relatively undisturbed island in northern Michigan, USA. This species often persists in habitat islands throughout much of its distribution due to extensive habitat loss and distance‐limited dispersal. We found that the entire island population exhibited weak genetic structuring with spatially segregated variation in effective migration and genetic diversity. The low level of genetic structuring contrasts with previous studies in the southern part of the species’ range at comparable fine scales (~7 km), in which much higher levels of structuring were documented. The island population's genetic structuring more closely resembles that of populations from Ontario, Canada, that occupy similarly intact habitats. Intrapopulation variation in effective migration and genetic diversity likely corresponds to the presence of large inland lakes acting as barriers and more human activity in the southern portion of the island. The observed genetic structuring in this intact landscape suggests that the Eastern Massasauga is capable of sufficient interpatch movements to reduce overall genetic structuring and colonize new habitats. Landscape mosaics with multiple habitat patches and localized barriers (e.g., large water bodies or roads) will promote gene flow and natural colonization for this declining species.
Comparison of Effect of Nebivolol and Bisoprolol on Sexual Function of Hypertensive Female Patients

Introduction Male and female sexual dysfunction is frequently found in patients with hypertension. Many studies indicate that this is found more frequently in patients treated with beta-blockers rather than due to hypertension itself; however, almost all studies have been done on male population. This study aims to study the effect of two commonly used beta-blockers on sexual function of a hypertensive female patient. Methods This two-arm open-label randomized prospective study was conducted from April 1, 2019 to March 30, 2020 in a tertiary care hospital at Pakistan. One hundred and fifty participants randomized to group A were given nebivolol 5 mg once daily in addition to their current hypertensive treatment. Another 150 participants randomized to group B were given bisoprolol 5 mg once daily in addition to their hypertensive therapy. Sexual function was assessed on day 0 and day 90 using female sexual function index (FSFI). Results The mean sexual score in the nebivolol group significantly improved after day 90 in comparison to day 0 (24.16 ± 2.1 vs. 26.91 ± 2.6; p-value < 0.0001), while no difference in sexual score in bisoprolol group after day 90 was observed (24.14 ± 2.1 vs. 24.12 ± 2.0; p-value = 0.91). Conclusion In this study, nebivolol group was associated with a significant improvement in sexual function. This can be due to additional vasodilation properties and a low risk of sexual side effects associated with nebivolol.
Does stress mess with rodents’ heads? Influence of habitat amount and genetic factors in mandible fluctuating asymmetry in South American water rats (Nectomys squamipes, Sigmodontinae) from Brazilian Atlantic rainforest remnants

Abstract Loss of developmental stability can lead to deviations from bilateral symmetry (i.e. Fluctuating Asymmetry ‐ FA), and is thought to be caused by environmental and genetic factors associated with habitat loss and stress. Therefore, levels of FA might be a valuable tool to monitor wild populations if FA serves as an indicator of exposure to stress due to impacts of habitat loss and fragmentation. In studies examining FA and habitat fragmentation, FA levels are often explained by loss of genetic variation, though few studies have addressed FA’s use as indicator of environmental impact. Here, we investigated whether habitat loss, genetic variation, and/or inbreeding affect the developmental instability in Brazilian Atlantic forest populations of a Neotropical water rat (Nectomys squamipes). We sampled individuals from eight sites within Atlantic forest remnants with different amounts of available forest habitat and assessed FA levels with geometric morphometric techniques using adult mandibles. We used observed heterozygosity (Ho) and inbreeding coefficient (Fis), from seven microsatellite markers, as a proxy of genetic variation at individual and population levels. Populations were not significantly different for shape or size FA levels. Furthermore, interindividual variation in both shape and size FA levels and interpopulational differences in size FA levels were best explained by chance. However, habitat amount was negatively associated with both interpopulational variance and average shape FA levels. This association was stronger in populations living in areas with <28% of forest cover, which presented higher variance and higher average FA, suggesting that Nectomys squamipes might have a tolerance threshold to small availability of habitat. Our work is one of the first to use FA to address environmental stress caused by habitat loss in small mammal populations from a Neotropical biome. We suggest that shape FA might serve as a conservation tool to monitor human impact on natural animal populations.