Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstruction

ABSTRACT Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a real-time hyperspectral imaging system for surgical decision-making possible. However, optimal exploitation of the captured data requires solving an ill-posed demosaicking problem and applying additional spectral corrections. In this work, we propose a supervised learning-based image demosaicking algorithm for snapshot hyperspectral images. Due to the lack of publicly available medical images acquired with snapshot mosaic cameras, a synthetic image generation approach is proposed to simulate snapshot images from existing medical image datasets captured by high-resolution, but slow, hyperspectral imaging devices. Image reconstruction is achieved using convolutional neural networks for hyperspectral image super-resolution, followed by spectral correction using a sensor-specific calibration matrix. The results are evaluated both quantitatively and qualitatively, showing clear improvements in image quality compared to a baseline demosaicking method using linear interpolation. Moreover, the fast processing time of 45 ms of our algorithm to obtain super-resolved RGB or oxygenation saturation maps per image for a state-of-the-art snapshot mosaic camera demonstrates the potential for its seamless integration into real-time surgical hyperspectral imaging applications.
Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

Motion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges. In this paper, we study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map that indicates the earliest time when each location can be occupied by either seen and unseen vehicles. The ability to predict unseen vehicles is critical for safety in autonomous driving. To tackle this challenging task, we propose a safety-aware deep learning model with three new loss functions to predict the earliest occupancy map. Experiments on the large-scale autonomous driving nuScenes dataset show that our proposed model significantly outperforms the state-of-the-art baselines on the safety-aware motion prediction task. To the best of our knowledge, our approach is the first one that can predict the existence of unseen vehicles in most cases. Project page at https://github.com/xrenaa/Safety-Aware-Motion-Prediction.
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation

Interpreting model knowledge is an essential topic to improve human understanding of deep black-box models. Traditional methods contribute to providing intuitive instance-wise explanations which allocating importance scores for low-level features (e.g, pixels for images). To adapt to the human way of thinking, one strand of recent researches has shifted its spotlight to mining important concepts. However, these concept-based interpretation methods focus on computing the contribution of each discovered concept on the class level and can not precisely give instance-wise explanations. Besides, they consider each concept as an independent unit, and ignore the interactions among concepts. To this end, in this paper, we propose a novel COncept-based NEighbor Shapley approach (dubbed as CONE-SHAP) to evaluate the importance of each concept by considering its physical and semantic neighbors, and interpret model knowledge with both instance-wise and class-wise explanations. Thanks to this design, the interactions among concepts in the same image are fully considered. Meanwhile, the computational complexity of Shapley Value is reduced from exponential to polynomial. Moreover, for a more comprehensive evaluation, we further propose three criteria to quantify the rationality of the allocated contributions for the concepts, including coherency, complexity, and faithfulness. Extensive experiments and ablations have demonstrated that our CONE-SHAP algorithm outperforms existing concept-based methods and simultaneously provides precise explanations for each instance and class.
Remote Multilinear Compressive Learning With Adaptive Compression

Multilinear compressive learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. The level of signal compression affects the detection or classification performance of an MCL model, with higher compression rates often associated with lower inference accuracy. However, higher compression rates are more amenable to a wider range of applications, especially those that require low operating bandwidth and minimal energy consumption such as Internet of Things (IoT) applications. Many communication protocols provide support for adaptive data transmission to maximize the throughput and minimize energy consumption. By developing compressive sensing and learning models that can operate with an adaptive compression rate, we can maximize the informational content throughput of the whole application. In this article, we propose a novel optimization scheme that enables such a feature for MCL models. Our proposal enables the practical implementation of adaptive compressive signal acquisition and inference systems. Experimental results demonstrated that the proposed approach can significantly reduce the amount of computations required during the training phase of remote learning systems but also improve the informational content throughput via adaptive-rate sensing.
The Typical Set and Entropy in Stochastic Systems with Arbitrary Phase Space Growth

The existence of the typical set is key for data compression strategies and for the emergence of robust statistical observables in macroscopic physical systems. Standard approaches derive its existence from a restricted set of dynamical constraints. However, given its central role underlying the emergence of stable, almost deterministic statistical patterns, a question arises whether typical sets exist in much more general scenarios. We demonstrate here that the typical set can be defined and characterized from general forms of entropy for a much wider class of stochastic processes than was previously thought. This includes processes showing arbitrary path dependence, long range correlations or dynamic sampling spaces, suggesting that typicality is a generic property of stochastic processes, regardless of their complexity. We argue that the potential emergence of robust properties in complex stochastic systems provided by the existence of typical sets has special relevance to biological systems.
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems

This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i.e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i.e., robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.
NRG: A Network Perspective on Applications' Performance

—Running an application in a data center, users have an important goal: performance. Even though mismatched application requirements and network resource assignments can lead to signiﬁcant performance loss, the understanding of dynamic networking effects on data center applications’ performance is still limited. In this paper we present NRG , an open source toolset that enables reproducible experimentation and provides a networking perspective on applications’ performance. NRG is a portable and programmable solution operating at line rate, and enabling users to recreate data center network conditions as a black box within controlled, small-scale experimentation environments. We demonstrate the potential of NRG to solve performance issues and provide insights on several applications.
Overview of Traditional Use, Phytochemical, and Pharmacological Activities of Chinese Petai (Leucaena leucocephala)

Various herbal remedies have been used in multiple medical systems for the treatment and management of numerous diseases. One of the herbs found in Indonesia is Chinese petai. This article aims to seek information about the traditional uses, phytochemicals, and the Chinese petai plant (Leucaena leucocephala). The way to find information is through Google Scholar with the keywords "Leucaena leucocephala," "traditional use," "phytochemicals," "pharmacological activity. " The Chinese petai plant (Leucaena leucocephala) has been used in various traditional medicinal systems to treat multiple human diseases. Phytochemically, this plant has been reported to contain numerous alkaloids, flavonoids, saponins, tannins, and triterpenoids. Pharmacologically this plant has been reported as antibacterial, antidiabetic, anti-inflammatory, anticancer, anthelmintic, antioxidant, and larvicide. There are also reports available for this plant's traditional use as useful as a worm medicine, smooth bowel movements, treating fractures, insomnia, swelling, kidney inflammation, diabetes, and smooth menstruation. Many of the constituents isolated from (Leucaena leucocephala) have pharmacological activity, supporting further pharmacological studies.
The Singular Angle of Nonlinear Systems

In this paper, we introduce an angle notion, called the singular angle, for stable nonlinear systems from an input-output perspective. The proposed system singular angle, based on the angle between $\mathcal{L}_2$-signals, describes an upper bound for the "rotating effect" from the system input to output signals. It is, thus, different from the recently appeared nonlinear system phase which adopts the complexification of real-valued signals using the Hilbert transform. It can quantify the passivity and serve as an angular counterpart to the system $\mathcal{L}_2$-gain. It also provides an alternative to the nonlinear system phase. A nonlinear small angle theorem, which involves a comparison of the loop system angle with $\pi$, is established for feedback stability analysis. When dealing with multi-input multi-output linear time-invariant (LTI) systems, we further come up with the frequency-wise and $\mathcal{H}_\infty$ singular angle notions based on the matrix singular angle, and develop corresponding LTI small angle theorems.
Broad Generalization in Robotics Will Require Broad Datasets

: In order for robots to robustly perform a wide range of different tasks 1 in open-world settings, they must be able to generalize broadly, handling diverse 2 environments, objects, and situations. Machine learning, and deep learning in par-3 ticular, provides a powerful tool for enabling generalization. However, although 4 deep learning overtook other ﬁelds such as computer vision in just a few years, en-5
Iterative Imitation Policy Improvement for Interactive Autonomous Driving

We propose an imitation learning system for autonomous driving in urban traffic with interactions. We train a Behavioral Cloning~(BC) policy to imitate driving behavior collected from the real urban traffic, and apply the data aggregation algorithm to improve its performance iteratively. Applying data aggregation in this setting comes with two challenges. The first challenge is that it is expensive and dangerous to collect online rollout data in the real urban traffic. Creating similar traffic scenarios in simulator like CARLA for online rollout collection can also be difficult. Instead, we propose to create a weak simulator from the training dataset, in which all the surrounding vehicles follow the data trajectory provided by the dataset. We find that the collected online data in such a simulator can still be used to improve BC policy's performance. The second challenge is the tedious and time-consuming process of human labelling process during online rollout. To solve this problem, we use an A$^*$ planner as a pseudo-expert to provide expert-like demonstration. We validate our proposed imitation learning system in the real urban traffic scenarios. The experimental results show that our system can significantly improve the performance of baseline BC policy.
Causal optimization method for imaginary-time Green's functions in interacting electron systems

We develop a causal optimization method that ensures causality in numerical calculations of Green's functions in interacting electron systems. Our method removes noncausality of numerical data by finding causal functions closest to the data. By testing our method with an exactly calculable model and applying it to practical dynamical mean-field calculations, we find that intermediate-frequency behaviors of Green's functions are determined solely by causality, and noncausal statistical errors are removed very efficiently. Furthermore, we demonstrate that numerical calculations of the physical branch of the Luttinger-Ward functional can be stabilized by ensuring causality of the noninteracting Green's function. Our method and findings provide a basis for improving stability and efficiency of numerical simulations of quantum many-body systems.
Identification of additional young nearby runaway stars based on Gaia data release 2 observations and the lithium test

Funding Information NE 515/58-1. MU 2695/27-1. Runaway stars are characterised by their remarkably high space velocities, and the study of their formation mechanisms has attracted considerable interest. Young, nearby runaway stars are the most favorable for identifying their place of origin, and for searching for possible associated objects such as neutron stars. Usually the research field of runaway stars focuses on Oand B-type stars, because these objects are better detectable at larger distances than late-type stars. Early-type runaway stars have the advantage, that they evolve faster and can therefore better be confirmed to be young. In contrast to this, the catalogue of Young runaway stars within 3 kpc by Tetzlaff, Neuhäuser, & Hohle (2011) contains also stars of spectral type A and later. The objects in this catalogue were originally classified as young (≤ 50 Myr) runaway stars by using Hipparcos data to estimate the ages from their location in the Hertzsprung-Russell diagram and evolutionary models. In this article, we redetermine and/or constrain their ages not only by using the more precise second data release of the Gaia mission, but also by measuring the equivalent width of the lithium (6708Å) line, which is a youth indicator. Therefore, we searched for lithium absorption in the spectra of 51 target stars, taken at theUniversity Observatory Jena betweenMarch and September 2020 with the Échelle spectrograph FLECHAS, and within additional TRES-spectra from the Fred L. Whipple Observatory. The main part of this campaign with its 308 reduced spectra, accessible at VizieR, was already published. In this work, which is the continuation and completion of the in 2015 initiated observing campaign, we found three additional young runaway star candidates.
Summertime circumglobal Rossby waves in climate models: Small biases in upper-level circulation create substantial biases in surface imprint

Abstract. In boreal summer, circumglobal Rossby waves can promote stagnating weather systems that favor extreme events like heatwaves or droughts. Recent work highlighted the risks associated with amplified Rossby wavenumber 5 and 7 in triggering simultaneous warm anomalies in specific agricultural breadbaskets in the Northern Hemisphere. These type of wave patterns thus pose potential risks for food production, as well as human health, and other impacts. The representation of such summertime wave events and their surface imprints in general circulation models (GCMs) has not been systematically analyzed. Here we validate three state-of-the-art global climate models (EC-Earth, CESM, and MIROC), quantify their biases and provide insights into the underlying physical reasons for the biases. To do so, the ExtremeX experiments output data were used, which are (1) historic simulations (1979–2015/2016) of a freely running atmosphere with prescribed ocean, and experiments that additionally nudge toward the observed (2) upper-level horizontal winds in the atmosphere, (3) soil moisture conditions, or (4) both. The nudged experiments are used to trace the sources of the model biases to either the large-scale atmospheric circulation or surface feedback processes. We show that while the wave position and magnitude is represented well compared to ERA5 reanalysis data. During high amplitudes (> 1.5 s.d.) wave-5 and wave- 7 events, the imprint on surface variables temperature, precipitation and sea level pressure is substantially underestimated: typically, by a factor of 1.5 in correlation and normalized standard deviations (n.s.d.) for near-surface temperature and mean sea level pressure. As for the precipitation, it’s still a factor of 1.5 for n.s.d. but 2 for correlation. The correlations and n.s.d. for surface variables do not improve if only the soil moisture is prescribed, but considerably increased when the upper-level atmosphere circulation is nudged. The underestimation factors are corrected almost entirely. When applying both soil moisture prescription and the nudging of upper-level atmosphere, both the correlation and n.s.d. values are quite similar to only atmosphere component is nudged experiments. Hence, the near-surface biases can be substantially improved when nudging the upper-level circulation providing evidence that relatively small biases in the models’ representation of the upper-level waves can strongly affect associated temperature and rainfall anomalies.
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction

We investigate whether model extraction can be used to ‘steal’ the weights of sequential recommender systems, and the potential threats posed to victims of such attacks. This type of risk has attracted attention in image and text classification, but to our knowledge not in recommender systems. We argue that sequential recommender systems are subject to unique vulnerabilities due to the specific autoregressive regimes used to train them. Unlike many existing recommender attackers, which assume the dataset used to train the victim model is exposed to attackers, we consider a data-free setting, where training data are not accessible. Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation. We investigate state-of-the-art models for sequential recommendation and show their vulnerability under model extraction and downstream attacks. We perform attacks in two stages. (1) Model extraction: given different types of synthetic data and their labels retrieved from a black-box recommender, we extract the black-box model to a white-box model via distillation. (2) Downstream attacks: we attack the black-box model with adversarial samples generated by the white-box recommender. Experiments show the effectiveness of our data-free model extraction and downstream attacks on sequential recommenders in both profile pollution and data poisoning settings.
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services in both the research community and industry. When operating a datacenter, optimization of resource scheduling and management can bring significant financial benefits. Achieving this goal requires a deep understanding of the job features and user behaviors. We present a comprehensive study about the characteristics of DL jobs and resource management. First, we perform a large-scale analysis of real-world job traces from SenseTime. We uncover some interesting conclusions from the perspectives of clusters, jobs and users, which can facilitate the cluster system designs. Second, we introduce a general-purpose framework, which manages resources based on historical data. As case studies, we design (1) a Quasi-Shortest-Service-First scheduling service, which can minimize the cluster-wide average job completion time by up to 6.5×; (2) a Cluster Energy Saving service, which improves overall cluster utilization by up to 13%.
Downcomer modification in the Jameson cell and its effects on coarse particle flotation

ABSTRACT Jameson cell is a flotation device that has unique characteristics for fine grained particles, and thus, it has more than three hundred applications in worldwide. On the contrary, the flotation performance of the cell decreases by coarser feeding, which limits the application of the Jameson cell. It has been figured out that the main reason for the performance decrease for coarse particle is turbulence which occurs at the outlet of the downcomer in the Jameson cell. Therefore, the aim of this study was to prevent the performance decrease in the Jameson cell by modifying the downcomer which was implemented by taking into account the fluid mechanics law and mathematical modelling. The modified downcomer, which can be called as diffuser, can provide better aggregate stability and less turbulence at the end of the downcomer. Thus, theoretically the better performance was obtained by the use of the new form of downcomer. In addition, a series of experiments were carried out to verify the success of the modified downcomer. According to the results, velocity gradient (G) decreased to 640 s−1 from 671 s−1 by using the modified downcomer, and the maximum floatable particle size increased to 255 µm from 225 µm.
Detection of acute myocarditis using T1 and T2 mapping cardiovascular magnetic resonance: A systematic review and meta‐analysis

Abstract Objectives This study was aimed to systematically review the existing literature and explore more the diagnostic value of T1 and T2 mapping in acute myocarditis. Methods Studies were searched from five electronic databases. Sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic curves (SROC) were calculated to present diagnostic performance. A meta‐regression and subgroup analysis was performed based on validation (endomyocardial biopsy [EMB] vs. clinical criteria). Results A total of 10 studies were included, with 400 myocarditis patients and 266 controls. Native T1, T2, and extracellular volume (ECV) values were significantly increased in the myocarditis group. Pooled sensitivities for T1, T2 mapping, and ECV were 0.84 (0.78–0.88), 0.77 (0.69–0.83), and 0.69 (0.50–0.83), respectively. Pooled specificities were 0.86 (0.69–0.95), 0.83 (0.73–0.89), and 0.77 (0.63–0.87), respectively. The DORs were 32 (12–87), 16 (8–30), and 7 (4–14), respectively. The areas under the curve (AUC) of SROC were 0.87 (0.84–0.90), 0.86 (0.82–0.89), and 0.80 (0.76–0.83), respectively. In the meta‐regression and subgroup analysis, significantly lower specificities of T1 and T2 mapping were observed in EMB studies (p < 0.01). Conclusion The currently available evidence shows that T1 and T2 mapping including ECV alone offer comparably good diagnostic performance for the detection of acute myocarditis. The reason for the observed mismatch with EMB findings should be further investigated.