Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Lipids on Planetary Surfaces with Laser Desorption Ionization Mass Spectrometry

In the search for extraterrestrial life, biosignatures (e.g., organic molecules) play an important role, of which lipids are one considerable class. If detected, these molecules can be strong indicators of the presence of life, past or present, as they are ubiquitous in life on Earth. However, their detection is challenging, depending on, e.g., instrument performance, as well as the selected site. In this contribution, we demonstrate that, using laser desorption ionization mass spectrometry, detection of lipids is feasible. Using our space prototype instrument designed and built in-house, six representative lipids were successfully detected: cholecalciferol, phylloquinone, menadione, 17α-ethynylestradiol, α-tocopherol, and retinol, both as pure substances and as mixtures additionally containing amino acids or polycyclic aromatic hydrocarbons. Observed limits of detection for lipids already meet the requirements stated in the Enceladus Orbilander mission concept. The current performance of our LDI-MS system allows for the simultaneous identification of lipids, amino acids, and polycyclic aromatic hydrocarbons, using a single instrument. We therefore believe that the LDI-MS system is a promising candidate for future space exploration missions devoted to life detection.
Optimization and self-dispatching of hydro-power companies in the electricity market

The deepening reform of the power system has resulted in increased competition between electricity market participants. The ADP method is applied to solve the optimal self-dispatch problem of hydro-power companies under uncertain conditions. Firstly, according to the characteristics of hydropower generation technology and self-dispatching, the operation model of hydropower is given. Then, according to the organization and trading behavior of the forward contract market and day-ahead market, a medium and long-term contract quantity decision framework based on stochastic programming is given. Based on the stochastic programming decision framework, the ADP method is used to solve the generation and water decisions in each stage, and the distribution strategy of the electricity quantity of forward transactions and day-trading electricity quantity is obtained. Finally, an example is given to demonstrate the effectiveness of the proposed model and ADP method.
The structure and statistics of interstellar turbulence

We explore the structure and statistics of multiphase, magnetized ISM turbulence in the local Milky Way by means of driven periodic box numerical MHD simulations. Using the higher order-accurate piecewise-parabolic method on a local stencil (PPML), we carry out a small parameter survey varying the mean magnetic field strength and density while fixing the rms velocity to observed values. We quantify numerous characteristics of the transient and steady-state turbulence, including its thermodynamics and phase structure, kinetic and magnetic energy power spectra, structure functions, and distribution functions of density, column density, pressure, and magnetic field strength. The simulations reproduce many observables of the local ISM, including molecular clouds, such as the ratio of turbulent to mean magnetic field at 100 pc scale, the mass and volume fractions of thermally stable Hi, the lognormal distribution of column densities, the mass-weighted distribution of thermal pressure, and the linewidth-size relationship for molecular clouds. Our models predict the shape of magnetic field probability density functions (PDFs), which are strongly non-Gaussian, and the relative alignment of magnetic field and density structures. Finally, our models show how the observed low rates of star formation per free-fall time are controlled by the multiphase thermodynamics and large-scale turbulence.
Understanding the general feature of microvariability in Kepler blazar W2R 1926+42

We analyze the ${\it Kepler}$ monitoring light curve of a blazar W2R 1926$+$42 to examine features of microvariability by means of the "shot analysis" technique. We select 195 intra-day, flare-like variations (shots) for the continuous light curve of Quarter 14 with a duration of 100 d. In the application of the shot analysis, an averaged profile of variations is assumed to converge with a universal profile which reflects a physical mechanism generating the microvariability in a blazar jet, although light-variation profiles of selected shots show a variety. A mean profile, which is obtained by aligning the peaks of the 195 shots, is composed of a spiky-shape shot component at $\pm$0.1 d (with respect to the time of the peak), and two slow varying components ranging from $-$0.50 d to $-$0.15 d and from 0.10 d to 0.45 d of the peak time. The former spiky feature is well represented by an exponential rise of 0.043$\pm$0.001 d and an exponential decay of 0.061$\pm$0.002 d. These timescales are consistent with that corresponding to a break frequency of a power spectrum density calculated from the obtained light curve. After verification with the Monte-Carlo method, the exponential shape, but not the observed asymmetry, of the shot component can be explained by noise variation. The asymmetry is difficult to explain through a geometrical effect (i.e. changes of the geometry of the emitting region), but is more likely to be caused by the production and dissipation of high-energy accelerated particles in the jet. Additionally, durations of the detected shots show a systematic variation with a dispersion caused by a statistical randomness. A comparison with the variability of Cygnus X-1 is also briefly discussed.
A high order accurate finite element algorithm for high Reynolds number flow prediction

A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification

Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to deploy in resource-constrained environments. These issues are exacerbated for prediction tasks with sequential classification on progressively transitioned stages with ''happens-before'' relation between them.We argue that it is possible to ''unfold'' a monolithic single multi-class classifier, typically trained for all stages using all data, into a series of single-stage classifiers. Each single-stage classifier can be cascaded gradually from cheaper to more expensive binary classifiers that are trained using only the necessary data modalities or features required for that stage. UnfoldML is a cost-aware and uncertainty-based dynamic 2D prediction pipeline for multi-stage classification that enables (1) navigation of the accuracy/cost tradeoff space, (2) reducing the spatio-temporal cost of inference by orders of magnitude, and (3) early prediction on proceeding stages. UnfoldML achieves orders of magnitude better cost in clinical settings, while detecting multi-stage disease development in real time. It achieves within 0.1% accuracy from the highest-performing multi-class baseline, while saving close to 20X on spatio-temporal cost of inference and earlier (3.5hrs) disease onset prediction. We also show that UnfoldML generalizes to image classification, where it can predict different level of labels (from coarse to fine) given different level of abstractions of a image, saving close to 5X cost with as little as 0.4% accuracy reduction.
Spectrophotometric studies of nebulae. XXI. The remarkable planetary NGC 6778

The relatively faint planetary NGC 6778 is distinguished by an extraordinarily strong spectrum of permitted lines of C II, N II, N III, O II, O IIl, etc., and pronounced stratification effects. It is suggested that these permitted lines arise from direct excitation from the nucleus rather than by recombination in dense filaments. The STAO III! and other forbidden lines seem weaker than in other planetaries; possibly this is indicative of lower abundances of the elements of the oxygen group. (auth)
Predicting Visual Attention and Distraction During Visual Search Using Convolutional Neural Networks

—Most studies in computational modeling of visual at- tention encompass task-free observation of images. Free-viewing saliency considers limited scenarios of daily life. Most visual activities are goal-oriented and demand a great amount of top-down attention control. Visual search task demands more top- down control of attention, compared to free-viewing. In this paper, we present two approaches to model visual attention and distraction of observers during visual search. Our ﬁrst approach adapts a light-weight free-viewing saliency model to predict eye ﬁxation density(probability) maps of human observers over pixels of search images, using a two-stream convolutional encoder-decoder network, trained and evaluated on COCOSearch18 dataset. This method predicts which locations are more distracting when searching for a particular target. Our network achieves good results on standard saliency metrics ( AUC- Judd =0.95, AUC-Borji =0.85, sAUC =0.84, NSS =4.64, KLD =0.93, CC =0.72, SIM =0.54, and IG =2.59). Our second approach is object-based and predicts the distractor and target objects during visual search. Distractors are all objects except the target that ob- servers ﬁxate on during search. This method uses a Mask-RCNN segmentation network pre-trained on MS-COCO and ﬁne-tuned on COCO-Search18 dataset. We release our segmentation annotations of targets and distractors in COCO-Search18 for three tar- get categories: bottle, bowl, and car. The average scores over the three categories are: F1-score =0.64, MAP 0.5 =0.57, MAR 0.5 =0.73. Our implementation code in Tensorﬂow is publicly available at
Full-Scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions

Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassing multi-scalar features acquired at different levels of the network. This helps segment target regions (lesions) of varying shape, size and contrast. Incorporation of the entire gamut of multi-scalar characteristics into the novel attention mechanism helps prioritize the selection of activation responses and locations containing useful information. Determining robust and discriminatory features along the decoder path is facilitated with deep supervision. Connections in the decoder arm are remodeled to handle the issue of vanishing gradient. As observed from the experimental results, FuDSA-Net surpasses other state-of-the-art architectures; especially, when it comes to characterizing complicated geometries of the lesions.
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies

We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to environmental variations. Specifically, we show little-to-no degradation of pose accuracy even with extremely poor depth estimates from a lightweight depth predictor. Our framework also maintains high pose accuracy in extreme lighting variations compared to standard deep learning, even without explicit domain adaptation. By openly representing the map and intermediate feature maps (such as depth estimates), our framework also allows for faster updates and reusing intermediate predictions for other tasks, such as obstacle avoidance, resulting in much higher resource efficiency.
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

The International Standards on Auditing require auditors to collect reasonable assurance that ﬁnancial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the ‘real-time’ assessment of digital accounting journal entries. Recently, driven by the advances in artiﬁcial intelligence, Deep Learning techniques have emerged in ﬁnancial auditing to examine vast quantities of accounting data. However, learning highly adaptive audit models in decentralised and dynamic settings remains challenging. It requires the study of data distribution shifts over multiple clients and time periods. In this work, we propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework’s ability to detect accounting anomalies in common scenarios of organizational activity. Our empirical results, using real-world datasets and combined federated-continual learning strategies, demonstrate the learned model’s ability to detect anomalies in audit settings of data distribution shifts.
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?

The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four pathologies: Parkinson's disease, laryngeal cancer, cleft lip and palate, and oral squamous cell carcinoma. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be effectively used to classify these types of pathological voices. We evaluate the robustness of our classifiers by adding room impulse responses to the test data and by applying them to unseen speech corpora. Our approach achieves unweighted average F1-Scores between 74.1% and 97.0%, depending on the model and the noise conditions used. The systems generalize and perform well on unseen data of healthy speakers sampled from a variety of different sources.
One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits

We address the problem of \emph{`Internal Regret'} in \emph{Sleeping Bandits} in the fully adversarial setup, as well as draw connections between different existing notions of sleeping regrets in the multiarmed bandits (MAB) literature and consequently analyze the implications: Our first contribution is to propose the new notion of \emph{Internal Regret} for sleeping MAB. We then proposed an algorithm that yields sublinear regret in that measure, even for a completely adversarial sequence of losses and availabilities. We further show that a low sleeping internal regret always implies a low external regret, and as well as a low policy regret for iid sequence of losses. The main contribution of this work precisely lies in unifying different notions of existing regret in sleeping bandits and understand the implication of one to another. Finally, we also extend our results to the setting of \emph{Dueling Bandits} (DB)--a preference feedback variant of MAB, and proposed a reduction to MAB idea to design a low regret algorithm for sleeping dueling bandits with stochastic preferences and adversarial availabilities. The efficacy of our algorithms is justified through empirical evaluations.
Complete gravitational-waveform amplitude modes for quasicircular compact binaries to the 3.5PN order

We complete the computation of the gravitational waveform amplitude for non-spinning compact binaries to the third and a half post-Newtonian (PN) order in the quasi-circular case. This is done by performing a spin-weighted spherical harmonics decomposition of the amplitude. This computation is achieved using the post-Newtonian-Multipolar-post-Minkowskian formalism. The amplitude modes are written in a PN expanded way as well as a factorized form suitable for EOB template building. Combining this work with previous ones, we provide in the supplemental materials the expressions of the modes including all non-spinning and spinning effects to the 3.5PN order for non-precessing quasi-circular binaries.
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation

Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples to build new training instances. Continued training with such augmented data is able to improve off-the-shelf Transformer and Conformer models that were optimized on the original data only. We demonstrate considerable improvements on the LibriSpeech-960h test sets (WER 2.83 and 6.87 for test-clean and test-other), which carry over to models combined with shallow fusion (WER 2.55 and 6.27). Our method of continued training also leads to improvements of up to 0.9 WER on the ASR part of CoVoST-2 for four non-English languages, and we observe that the gains are highly dependent on the size of the original training data. We compare different concatenation strategies and found that our method does not need speaker information to achieve its improvements. Finally, we demonstrate on two datasets that our methods also works for speech translation tasks.
On the Genus of Random Regular Graphs

The genus of a graph is a topological invariant that measures the minimum genus of a surface on which the graph can be embedded without any edges crossing. Graph genus plays a fundamental role in topological graph theory, used to classify and study different types of graphs and their properties. We show that, for any integer $d \geq 2$, the genus of a random $d$-regular graph on $n$ nodes is $\frac{(d - 2)}{4}n(1 - \varepsilon) $ with high probability for any $\varepsilon>0$.
Deep Convolutional Neural Networks for Multi-Target Tracking: A Transfer Learning Approach

Multi-target tracking (MTT) is a traditional signal processing task, where the goal is to estimate the states of an unknown number of moving targets from noisy sensor measurements. In this paper, we revisit MTT from a deep learning perspective and propose convolutional neural network (CNN) architectures to tackle it. We represent the target states and sensor measurements as images. Thereby we recast the problem as a image-to-image prediction task for which we train a fully convolutional model. This architecture is motivated by a novel theoretical bound on the transferability error of CNN. The proposed CNN architecture outperforms a GM-PHD ﬁlter on the MTT task with 10 targets. The CNN performance transfers without re-training to a larger MTT task with 250 targets with only a 13% increase in average OSPA. The code used for these numerical experiments is is available on GitHub: https://github.com/ Damowerko/mtt .
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs

Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks.
PERGAMO: Personalized 3D Garments from Monocular Video

Clothing plays a fundamental role in digital humans. Current approaches to animate 3D garments are mostly based on realistic physics simulation, however, they typically suffer from two main issues: high computational run‐time cost, which hinders their deployment; and simulation‐to‐real gap, which impedes the synthesis of specific real‐world cloth samples. To circumvent both issues we propose PERGAMO, a data‐driven approach to learn a deformable model for 3D garments from monocular images. To this end, we first introduce a novel method to reconstruct the 3D geometry of garments from a single image, and use it to build a dataset of clothing from monocular videos. We use these 3D reconstructions to train a regression model that accurately predicts how the garment deforms as a function of the underlying body pose. We show that our method is capable of producing garment animations that match the real‐world behavior, and generalizes to unseen body motions extracted from motion capture dataset.
Large-area photon calorimeter with Ir-Pt bilayer transition-edge sensor for the CUPID experiment

CUPID is a next-generation neutrinoless double-beta decay experiment that will require cryogenic light detectors to improve background suppression, via the simultaneous readout of heat and light channels from its scintillating crystals. In this work we showcase light detectors based on a novel Ir-Pt bilayer transition edge sensor. We have performed a systematic study to improve the thermal coupling between the photon absorber and the sensor, and thereby its responsivity. Our first devices meet CUPID's baseline noise requirement of<100~eV rms. Our detectors have risetimes of $\sim$180 $\mu$s and measured timing jitter of<20 $\mu$s for the expected signal-to-noise at the Q-value of the decay, which achieves the CUPID's criterion of rejecting two-neutrino double-beta decay pileup events. The current work will inform the fabrication of future devices, culminating in the final TES design and a scaleable readout scheme for CUPID.