Miracle-dz/UniFAR-FTUs huggingface dataset:
Raising your voice: evolution of narrow-band high-frequency signals in toothed whales (Odontoceti)

Cetaceans use sound for communication, navigation and finding prey. Most extant odontocetes produce broadband (BB) biosonar clicks covering frequency ranges from tens of kilohertz to 150–170 kHz. In contrast, the biosonar clicks of some odontocetes are unique, being narrow in bandwidth with high centroid frequency (NBHF), peak frequencies being at 125–140 kHz and bandwidths of 11–20 kHz. Thirteen species within four families (Phocoenidae, Pontoporiidae, Kogiidae, Delphinidae) are known to produce these signals, implying convergent evolution under strong selective drivers. Several hypotheses have been proposed, including acoustic crypsis to escape predation by killer whales, but none has provided comprehensive explanation of the timing of NBHF evolution and the pressures driving sound production to such extremes. Using molecular phylogenetics and the cochlea anatomy of extinct and extant taxa, we demonstrate that early NBHF adaptations occurred at least 10 Mya, and possibly up to 18 Mya, indicating that killer whales cannot have been the sole driving force of NBHF signals, but that now extinct odontocetes may have provided similar pressures. Using palaeoclimate modelling, we further demonstrate that the upper advantageous spectral window for NBHF signals at around 130 kHz has persisted throughout most of the global sea area since the mid-Miocene, covering all known instances of NBHF evolution. applyparastyle “fig//caption/p[1]” parastyle “FigCapt”
The Metallicity Dependence of the High-mass X-Ray Binary Luminosity Function

In this work, we present detailed constraints on the metallicity dependence of the high-mass X-ray binary (HMXB) X-ray luminosity function (XLF). We analyze ≈5 Ms of Chandra data for 55 actively star-forming galaxies at D ≲ 30 Mpc, with gas-phase metallicities spanning ≈ 7–9.2. Within the galactic footprints, our sample contains a total of 1311 X-ray point sources, of which ≈49% are expected to be HMXBs, with the remaining sources likely to be low-mass X-ray binaries (LMXBs; ≈22%) and unrelated background sources (≈29%). We construct a model that successfully characterizes the average HMXB XLF over the full metallicity range. We demonstrate that the SFR-normalized HMXB XLF shows clear trends with metallicity, showing steadily increasing numbers of luminous and ultraluminous X-ray sources ( (erg s−1) = 38–40.5) with declining metallicity. However, we find that the low-luminosity ( (erg s−1) = 36–38) HMXB XLF appears to show a nearly constant SFR scaling and slope with metallicity. Our model provides a revised scaling relation of integrated LX/SFR versus , and a new characterization of its SFR-dependent stochastic scatter. The general trend of this relation is broadly consistent with past studies based on integrated galaxy emission; however, our model suggests that this relation is driven primarily by the high-luminosity end of the HMXB XLF. Our results have implications for binary population synthesis models, the nature of super-Eddington accreting objects (e.g., ultraluminous X-ray sources), recent efforts to identify active galactic nucleus candidates in dwarf galaxies, and the X-ray radiation fields in the early universe during the epoch of cosmic heating at z ≳ 10.
Topology-free optimal power dispatch for distribution network considering security constraints and flexible building thermal inertia

With the increasing integration of distributed PV generation, the distribution network requires more and more flexibility to achieve the security-constrained optimal power dispatch. However, the conventional flexibility sources usually require additional investment cost for equipment. Moreover, involving the security constraints is very challenging due to the requirements of accurate network model that may be unavailable in practice. This paper addresses the aforementioned challenge by proposing a topology-free optimal power dispatch framework for distribution networks. It utilizes building thermal inertia to provide flexibility to avoid additional investment. To guarantee the operation safety, a multi-layer perception (MLP) is trained based on historical operational data and then reformulated as mixed-integer constraints to replace the inexplicit original security constraints. Numerical results confirm that the proposed framework can derive a feasible and optimal strategy without any topology information.
Vector Embeddings with Subvector Permutation Invariance using a Triplet Enhanced Autoencoder

The use of deep neural network (DNN) autoencoders (AEs) has recently exploded due to their wide applicability. However, the embedding representation produced by a standard DNN AE that is trained to minimize only the reconstruction error does not always reveal more subtle patterns in the data. Sometimes, the autoencoder needs further direction in the form of one or more additional loss functions. In this paper, we use an autoencoder enhanced with triplet loss to promote the clustering of vectors that are related through permutations of constituent subvectors. With this approach, we can create an embedding of the vector that is nearly invariant to such permutations. We can then use these invariant embeddings as inputs to other problems, like classification and clustering, and improve detection accuracy in those problems.
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which hinders the development of video text-related applications. In this paper, we systematically introduce a new large-scale benchmark, named as STVText4, a well-designed spatial-temporal detection metric (STDM), and a novel clustering-based baseline method, referred to as Temporal Clustering (TC). STVText4 opens a challenging yet promising direction of VSTD, termed as ST-VSTD, which targets at simultaneously detecting video scene texts in both spatial and temporal domains. STVText4 contains more than 1.4 million text instances from 161,347 video frames of 106 videos, where each instance is annotated with not only spatial bounding box and temporal range but also four intrinsic attributes, including legibility, density, scale, and lifecycle, to facilitate the community. With continuous propagation of identical texts in the video sequence, TC can accurately output the spatial quadrilateral and temporal range of the texts, which sets a strong baseline for ST-VSTD. Experiments demonstrate the efficacy of our method and the great academic and practical value of the STVText4. The dataset and code will be available soon.
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification

Among the many ways of quantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluation. A model that predicts the true conditional quantiles for each input, at all quantile levels, presents a correct and efficient representation of the underlying uncertainty. To achieve this, many current quantile-based methods focus on optimizing the so-called pinball loss. However, this loss restricts the scope of applicable regression models, limits the ability to target many desirable properties (e.g. calibration, sharpness, centered intervals), and may produce poor conditional quantiles. In this work, we develop new quantile methods that address these shortcomings. In particular, we propose methods that can apply to any class of regression model, allow for selecting a Pareto-optimal trade-off between calibration and sharpness, optimize for calibration of centered intervals, and produce more accurate conditional quantiles. We provide a thorough experimental evaluation of our methods, which includes a high dimensional uncertainty quantification task in nuclear fusion.
Approximate Bipartite Vertex Cover in the CONGEST Model

We give efficient distributed algorithms for the minimum vertex cover problem in bipartite graphs in the CONGEST model. From K\H{o}nig's theorem, it is well known that in bipartite graphs the size of a minimum vertex cover is equal to the size of a maximum matching. We first show that together with an existing $O(n\log n)$-round algorithm for computing a maximum matching, the constructive proof of K\H{o}nig's theorem directly leads to a deterministic $O(n\log n)$-round CONGEST algorithm for computing a minimum vertex cover. We then show that by adapting the construction, we can also convert an \emph{approximate} maximum matching into an \emph{approximate} minimum vertex cover. Given a $(1-\delta)$-approximate matching for some $\delta>1$, we show that a $(1+O(\delta))$-approximate vertex cover can be computed in time $O(D+\mathrm{poly}(\frac{\log n}{\delta}))$, where $D$ is the diameter of the graph. When combining with known graph clustering techniques, for any $\varepsilon\in(0,1]$, this leads to a $\mathrm{poly}(\frac{\log n}{\varepsilon})$-time deterministic and also to a slightly faster and simpler randomized $O(\frac{\log n}{\varepsilon^3})$-round CONGEST algorithm for computing a $(1+\varepsilon)$-approximate vertex cover in bipartite graphs. For constant $\varepsilon$, the randomized time complexity matches the $\Omega(\log n)$ lower bound for computing a $(1+\varepsilon)$-approximate vertex cover in bipartite graphs even in the LOCAL model. Our results are also in contrast to the situation in general graphs, where it is known that computing an optimal vertex cover requires $\tilde{\Omega}(n^2)$ rounds in the CONGEST model and where it is not even known how to compute any $(2-\varepsilon)$-approximation in time $o(n^2)$.
Ground-state cooling of mechanical resonators by hot thermal light

We propose a scheme to cool down a mechanical resonator to its quantum ground-state, which is interacting with a cavity mode via the optomechanical coupling. As opposed to standard laser cooling schemes where coherence renders the state of the resonator to its ground-state, here we use incoherent thermal light to achieve the same aim. We show that simultaneous cooling of two degenerate or near-degenerate mechanical resonators is possible in our scheme, which is otherwise a challenging goal to achieve in optomechanics. The generalization of this method to the simultaneous cooling of multiple resonators is straightforward. The underlying physical mechanism of cooling is explained by revealing a direct connection between the laser sideband cooling and “cooling by heating” in a standard optomechanical setting.
Contextual Fusion For Adversarial Robustness

Mammalian brains handle complex reasoning tasks in a gestalt manner by integrating information from regions of the brain that are specialised to individual sensory modalities. This allows for improved robustness and better generalisation ability. In contrast, deep neural networks are usually designed to process one particular information stream and susceptible to various types of adversarial perturbations. While many methods exist for detecting and defending against adversarial attacks, they do not generalise across a range of attacks and negatively affect performance on clean, unperturbed data. We developed a fusion model using a combination of background and foreground features extracted in parallel from Places-CNN and Imagenet-CNN. We tested the benefits of the fusion approach on preserving adversarial robustness for human perceivable (e.g., Gaussian blur) and network perceivable (e.g., gradient-based) attacks for CIFAR-10 and MS COCO data sets. For gradient based attacks, our results show that fusion allows for significant improvements in classification without decreasing performance on unperturbed data and without need to perform adversarial retraining. Our fused model revealed improvements for Gaussian blur type perturbations as well. The increase in performance from fusion approach depended on the variability of the image contexts; larger increases were seen for classes of images with larger differences in their contexts. We also demonstrate the effect of regularization to bias the classifier decision in the presence of a known adversary. We propose that this biologically inspired approach to integrate information across multiple modalities provides a new way to improve adversarial robustness that can be complementary to current state of the art approaches.
Preparing Weather Data for Real-Time Building Energy Simulation

This study introduces a framework for quality control of measured weather data, including anomaly detection, and infilling missing values. Weather data is a fundamental input to building performance simulations, in which anomalous values defect the results while missing data lead to an unexpected termination of the simulation process. Traditionally, infilling missing values in weather data is performed through periodic or linear interpolations. However, when missing values exceed many consecutive hours, the accuracy of traditional methods is subject to debate. This study demonstrates how Neural Networks can increase the accuracy of data imputation when compared to other supervised learning methods. The framework is validated by predicting missing temperature and relative humidity data for an observation site, through a network of nearby weather stations in Milan, Italy. Results show that the proposed method can facilitate real-time building simulations with accurate and rapid quality control.
KeyGuard: Using Selective Encryption to Mitigate Keylogging in Third-Party IME

As mobile devices become ubiquitous, people around the world have enjoyed the convenience they have brought to our lives. At the same time, the increasing security threats that rise from using mobile devices not only have caught attention from cyber security agencies but also have become a valid concern for mobile users. Keylogging is one of the mobile security threats caused by using insecure third-party IME (input method editor) applications. Keylogging, as the name suggests, keeps track of user\rq s key events performed on the device and stores all the events in a log. The log could include highly sensitive data such as credit card number, social security number, and passwords. This paper presents a novel solution by intercepting the keystroke events triggered by a user and encrypting them before sending them to the third-party IME, making the third-party IME unable to log what the users actually entered on the screen. Input will be decrypted when showing on text view on the underlying app. This solution addresses the fundamental reason why an IME may leak sensitive information since an IME will no longer have access to the user\rq s actual sensitive information, which will greatly reduce the chance of leaking sensitive information by using a third-party IME while maintaining the functionalities of the third-party IME at the same time.
Towards Emotion-Aware User Simulator for Task-Oriented Dialogue

The performance of a task-completion dialogue agent usually affects the user experience: when the conversation system yields an unreasonable response, users may feel dissatisfied. Besides, early termination often occurs in disappointing conversations. However, existing off-the-shelf user simulators generally assume an ideal and cooperative user, which is somewhat different from a real user, and inevitably lead to a sub-optimal dialogue policy. In this paper, we propose an emotion-aware user simulation framework for task-oriented dialogue, which is based on the OCC emotion model to update user emotions and drive user actions, to generate simulated behaviors that more similar to real users. We present a linear implementation (The source code will be released soon.) that is easy to understand and extend, and evaluate it on two domain-specific datasets. The experimental results show that the emotional simulation results of our proposed framework conform to common sense and have good versatility for different domains. Meanwhile, our framework provides us with another perspective to understand the improvement process of the dialogue policy model based on reinforcement learning.
From Protocol to Screening: A Hybrid Learning Approach for Technology-Assisted Systematic Literature Reviews

In the medical domain, a Systematic Literature Review (SLR) attempts to collect all empirical evidence, that fit pre-specified eligibility criteria, in order to answer a specific research question. The process of preparing an SLR consists of multiple tasks that are labor-intensive and time-consuming, involving large monetary costs. Technology-assisted review (TAR) methods automate the different processes of creating an SLR and they are particularly focused on reducing the burden of screening for reviewers. We present a novel method for TAR that implements a full pipeline from the research protocol to the screening of the relevant papers. Our pipeline overcomes the need of a Boolean query constructed by specialists and consists of three different components: the primary retrieval engine, the inter-review ranker and the intra-review ranker, combining learning-to-rank techniques with a relevance feedback method. In addition, we contribute an updated version of the Task 2 of the CLEF 2019 eHealth Lab dataset, which we make publicly available. Empirical results on this dataset show that our approach can achieve state-of-the-art results.
Enhancing the predictive capabilities for high P/T fuel sprays; non-ideal thermodynamic modelling using PC-SAFT

The present work aims to investigate the complex phenomena occurring during high-pressure/high-temperature fuel injection of the Engine Combustion Network (ECN) Spray-A case. While commonly in the literature transcritical mixing cases are approached using traditional cubic equation-of-state models, such models can prove insufficient in the accurate prediction of liquid density and speed of sound. The purpose of the present investigation is to employ a general tabulated approach which can be applied to any type of thermodynamic closure. At the same time, a more advanced model based on the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) is employed to create the thermodynamic table, as it is proven superior to the traditional cubic models, while also having the capacity of predicting Vapor-Liquid-Equilibrium. The model has been used for a combination of dodecane and nitrogen mixing, corresponding to the well known Spray-A conditions. Vapor penetration and mixing both in terms of temperature and mass fraction are found in agreement to experiments, within the experimental errors. Also, the thermodynamic states correspond well with the adiabatic isobaric-mixing curve, demonstrating the energy-conservative nature of the approach.
Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP

Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as free text. We propose a novel task of exploring fairness on a multimodal clinical dataset, adopting equalized odds for the downstream medical prediction tasks. To this end, we investigate a modality-agnostic fairness algorithm - equalized odds post processing - and compare it to a text-specific fairness algorithm: debiased clinical word embeddings. Despite the fact that debiased word embeddings do not explicitly address equalized odds of protected groups, we show that a text-specific approach to fairness may simultaneously achieve a good balance of performance classical notions of fairness. Our work opens the door for future work at the critical intersection of clinical NLP and fairness.
Data Driven Modeling of Interfacial Traction Separation Relations using Thermodynamic Consistent Neural Network (TCNN)

For multilayer structures, interfacial failure is one of the most important elements related to device reliability. For cohesive zone modelling, traction-separation relations represent the adhesive interactions across interfaces. However, existing theoretical models do not currently capture traction-separation relations that have been extracted using direct methods, particularly under mixed-mode conditions. Given the complexity of the problem, models derived from the neural network approach are attractive. Although they can be trained to fit data along the loading paths taken in a particular set of mixed-mode fracture experiments, they may fail to obey physical laws for paths not covered by the training data sets. In this paper, a thermodynamically consistent neural network (TCNN) approach is established to model the constitutive behavior of interfaces when faced with sparse training data sets. Accordingly, three conditions are examined and implemented here: (i) thermodynamic consistency, (ii) maximum energy dissipation path control and (iii) J-integral conservation. These conditions are treated as constraints and are implemented as such in the loss function. The feasibility of this approach is demonstrated by comparing the modeling results with a range of physical constraints. Moreover, a Bayesian optimization algorithm is then adopted to optimize the weight factors associated with each of the constraints in order to overcome convergence issues that can arise when multiple constraints are present. The resultant numerical implementation of the ideas presented here produced well-behaved, mixed-mode traction separation surfaces that maintained the fidelity of the experimental data that was provided as input. The proposed approach heralds a new autonomous, point-to-point constitutive modeling concept for interface mechanics.
A systematic review of causal methods enabling predictions under hypothetical interventions

Background: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. Aims: We aimed to identify and compare published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and possible sources of bias. Finally, we aimed to highlight unresolved methodological challenges. Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used to evaluate predictions under hypothetical interventions. We included both methodology development studies and applied studies. Results: We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full text screening, of which 12 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.