Miracle-dz/UniFAR-FTUs huggingface dataset:
Race to idle: New algorithms for speed scaling with a sleep state

We study an energy conservation problem where a variable-speed processor is equipped with a sleep state. Executing jobs at high speeds and then setting the processor asleep is an approach that can lead to further energy savings compared to standard dynamic speed scaling. We consider classical deadline-based scheduling, that is, each job is specified by a release time, a deadline and a processing volume. For general convex power functions, Irani et al. [2007] devised an offline 2-approximation algorithm. Roughly speaking, the algorithm schedules jobs at a critical speed <i>s</i><sub><i>crit</i></sub> that yields the smallest energy consumption while jobs are processed. For power functions <i>P</i>(<i>s</i>) = <i>s</i><sup>α</sup> & γγ, where <i>s</i> is the processor speed, Han et al. [2010] gave an α<sup>α</sup> + 2)-competitive online algorithm. We investigate the offline setting of speed scaling with a sleep state. First, we prove NP-hardness of the optimization problem. Additionally, we develop lower bounds, for general convex power functions: No algorithm that constructs <i>s</i><sub><i>crit</i></sub>-schedules, which execute jobs at speeds of at least <i>s</i><sub><i>crit</i></sub>, can achieve an approximation factor smaller than 2. Furthermore, no algorithm that minimizes the energy expended for processing jobs can attain an approximation ratio smaller than 2. We then present an algorithmic framework for designing good approximation algorithms. For general convex power functions, we derive an approximation factor of 4/3. For power functions <i>P</i>(<i>s</i>) = β <i>s</i><sup>α</sup> + γ, we obtain an approximation of 137/117 > 1.171. We finally show that our framework yields the best approximation guarantees for the class of <i>s</i><sub><i>crit</i></sub>-schedules. For general convex power functions, we give another 2-approximation algorithm. For functions <i>P</i>(<i>s</i>) = β<i>s</i><sup>α</sup> + γ, we present tight upper and lower bounds on the best possible approximation factor. The ratio is exactly <i>eW</i><sub>−1</sub>(−<i>e</i><sup>−1−1/<i>e</i></sup>)/(<i>eW</i><sub>−1</sub>(−<i>e</i><sup>−1−1/<i>e</i>)+1)</sup> > 1.211, where <i>W</i><sub>-1</sub> is the lower branch of the Lambert <i>W</i> function.
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation

In the image acquisition process, various forms of degradation, including noise, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on different image restoration tasks, including denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. Our code is available at https://github.com/c-yn/AdaIR.
The Dynamic Effect of Disability on Work and Subjective Wellbeing in Australia

Using longitudinal data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey (2001-2013) we examine the relationship between the dynamics of work-limiting disability and employment, hours of work, earnings and life satisfaction. We employ two alternative classifications of the dynamic trajectories of disability and, in doing so, are able to explicitly consider the influence of disability exit in addition to examining onset by chronicity and severity. After controlling for unobserved individual heterogeneity, we find that the positive impact of disability exit is smaller in magnitude and shorter-lived than the negative impact of onset. Further, while individuals are found to recover from a one period disability within three years, there is no sign of adaptation even after ten years for those whose disability is chronic, defined as evident for three or more years post-onset, and severe.
Cytokine-Induced Modulation of Cardiac Function

Cytokines act in an autocrine and/or paracrine fashion to induce a diverse variety of biological responses. Several cardiac diseases are associated with cytokine activation, and such activation significantly influences several physiologic parameters, including cardiac mechanical function. This review summarizes the current concepts regarding the modulation of myocardial function by cytokines and provides rationale for the sometimes-conflicting results in the literature regarding underlying mechanisms and patterns of dysfunction. Although traditionally considered cardiodepressant mediators, contractile responses are complex and bimodal, with an early response (within minutes) of variable direction, stimulatory or depressant, depending on the ambient physiologic milieu and relative contributions of the underlying signaling pathways that are activated. These pathways include sphingomyelinase-, nitric oxide (NO)-, and phospholipase A2-dependent signaling with resultant combined effects on contraction and the Ca2+ transient. This is subsequently followed by a profoundly cardiodepressant late response lasting hours to days, depending on the production of secondary mediators and the combined influence of NO generated from inducible NO synthase, reactive oxygen species, and alterations in &bgr;-adrenergic receptor signaling. The interrelationships between these pathways and the time-dependence of their activation are important considerations in the evaluation of cytokine-dependent dysfunction during both acute cardiac injury and chronic cardiac pathologies.
A system capable of verifiably and privately screening global DNA synthesis

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be used to ignite a pandemic. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new viruses and other biological threats. Second, anti-counterfeiting specifications on a local printer can't be extracted and misused by malicious actors, unlike information on biological threats. Finally, any screening must keep the inspected DNA sequences private, as they may constitute valuable trade secrets. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ base pairs against an up-to-date database of hazards, and its operational performance and specificity when applied to 67 million base pairs of DNA synthesized by providers in the United States, Europe, and China.
Object-Centric Domain Randomization for 3D Shape Reconstruction in the Wild

One of the biggest challenges in single-view 3D shape reconstruction in the wild is the scarcity of<3D shape, 2D image>-paired data from real-world environments. Inspired by remarkable achievements via domain randomization, we propose ObjectDR which synthesizes such paired data via a random simulation of visual variations in object appearances and backgrounds. Our data synthesis framework exploits a conditional generative model (e.g., ControlNet) to generate images conforming to spatial conditions such as 2.5D sketches, which are obtainable through a rendering process of 3D shapes from object collections (e.g., Objaverse-XL). To simulate diverse variations while preserving object silhouettes embedded in spatial conditions, we also introduce a disentangled framework which leverages an initial object guidance. After synthesizing a wide range of data, we pre-train a model on them so that it learns to capture a domain-invariant geometry prior which is consistent across various domains. We validate its effectiveness by substantially improving 3D shape reconstruction models on a real-world benchmark. In a scale-up evaluation, our pre-training achieves 23.6% superior results compared with the pre-training on high-quality computer graphics renderings.
Challenges in Location-Aware Computing

The availability of massive amounts of high-quality data as well as advances in computing and communications suggest the possibility of powerful new applications in science, commerce, environment, and government-but many research challenges remain. Research in geospatial information systems is inherently multidisciplinary, and research efforts are likely to be most successful if conducted by teams that combine expertise in applications as well as information technology. Advances in location-aware computing, in particular, could have important implications not just for how geospatial data are acquired but also for how and with what quality they can be delivered and how mobile and geographically distributed systems are designed.
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization

We present a method for end-to-end learning of Koopman surrogate models for optimal performance in control. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms, we use a training algorithm that exploits the potential differentiability of environments based on mechanistic simulation models. We evaluate the performance of our method by comparing it to that of other controller type and training algorithm combinations on a literature known eNMPC case study. Our method exhibits superior performance on this problem, thereby constituting a promising avenue towards more capable controllers that employ dynamic surrogate models.
In Between 3 D Active Appearance Models and 3 D Morphable Models

In this paper we propose a novel method of generating 3D Morphable Models (3DMMs) from 2D images. We develop algorithms of 3D face reconstruction from a sparse set of points acquired from 2D images. In order to establish correspondence between images precisely, we Combined Active Shape Models (ASMs) and Active Appearance Models (AAMs)(CASAAMs) in an intelligent way, showing improved performance on pixel-level accuracy and generalization to unseen faces. The CASAAMs are applied to the images of different views of the same person to extract facial shapes across pose. These 2D shapes are combined for reconstructing a sparse 3D model. The point density of the model is increased by the Loop subdivision method, which generates new vertices by a weighted sum of the existing vertices. Then, the depth of the dense 3D model is modified with an average 3D depth-map in order to preserve facial structure more realistically. Finally, all 249 3D models with expression changes are combined to generate a 3DMM for a compact representation. The first session of the Multi-PIE database, consisting of 249 persons with expression and illumination changes, is used for the modeling. Unlike typical 3DMMs, our model can generate 3D human faces more realistically and efficiently (2-3 seconds on P4 machine) under diverse illumination
Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques

We present novel approaches involving generative adversarial networks and diffusion models in order to synthesize high quality, live and spoof fingerprint images while preserving features such as uniqueness and diversity. We generate live fingerprints from noise with a variety of methods, and we use image translation techniques to translate live fingerprint images to spoof. To generate different types of spoof images based on limited training data we incorporate style transfer techniques through a cycle autoencoder equipped with a Wasserstein metric along with Gradient Penalty (CycleWGAN-GP) in order to avoid mode collapse and instability. We find that when the spoof training data includes distinct spoof characteristics, it leads to improved live-to-spoof translation. We assess the diversity and realism of the generated live fingerprint images mainly through the Fr\'echet Inception Distance (FID) and the False Acceptance Rate (FAR). Our best diffusion model achieved a FID of 15.78. The comparable WGAN-GP model achieved slightly higher FID while performing better in the uniqueness assessment due to a slightly lower FAR when matched against the training data, indicating better creativity. Moreover, we give example images showing that a DDPM model clearly can generate realistic fingerprint images.
A Design Space for Intelligent and Interactive Writing Assistants

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a large community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants.
Maximum Likelihood Alternating Summation for Multistatic Angle-based Multitarget Localization

Recent advancements in Wi-Fi sensing have sparked interest in exploiting OFDM modulated communication signals for target detection and tracking. In this study, we address the angle-based localization of multiple targets using a multistatic OFDM radar. While the maximum likelihood approach optimally merges data from each radar pair comprised by the system, it entails a complex multi-dimensional search process. Leveraging pre-estimation of the targets' parameters obtained via the MUSIC algorithm, our method decouples this multi-dimensional search into a single two-dimensional estimator per target. The proposed alternating summation method allows the computation of a combined likelihood map aggregating contributions from each radar pair, enabling target detection via peak selection. Besides reducing computational complexity, the method effectively captures target interactions and accommodates varying radar pair localization abilities. Also, it requires transmitting only the estimated channel covariance matrices of each radar pair to the central processor. Numerical simulations demonstrate superior performance over existing approaches.
LeFusion: Synthesizing Myocardial Pathology on Cardiac MRI via Lesion-Focus Diffusion Models

Data generated in clinical practice often exhibits biases, such as long-tail imbalance and algorithmic unfairness. This study aims to mitigate these challenges through data synthesis. Previous efforts in medical imaging synthesis have struggled with separating lesion information from background context, leading to difficulties in generating high-quality backgrounds and limited control over the synthetic output. Inspired by diffusion-based image inpainting, we propose LeFusion, lesion-focused diffusion models. By redesigning the diffusion learning objectives to concentrate on lesion areas, it simplifies the model learning process and enhance the controllability of the synthetic output, while preserving background by integrating forward-diffused background contexts into the reverse diffusion process. Furthermore, we generalize it to jointly handle multi-class lesions, and further introduce a generative model for lesion masks to increase synthesis diversity. Validated on the DE-MRI cardiac lesion segmentation dataset (Emidec), our methodology employs the popular nnUNet to demonstrate that the synthetic data make it possible to effectively enhance a state-of-the-art model. Code and model are available at https://github.com/M3DV/LeFusion.
Probing Goldstino excitation through the tunneling transport in a Bose-Fermi mixture with explicitly broken supersymmetry

We theoretically investigate the tunneling transport in a repulsively interacting ultracold Bose-Fermi mixture. A two-terminal model is applied to such a mixture and the supersymmetric-like tunneling current through the junction can be induced by the bias of fermion chemical potential between two reservoirs. The Goldstino, which is the Nambu-Goldstone fermionic mode associated with the spontaneous sypersymmetry breaking and appears as a gapped mode in the presence of the explicit supersymmetry breaking in existing Bose-Fermi mixtures, is found to contribute to the tunneling transport as a supercharge exchanging process. Our study provides a potential way to detect the Goldstino transport in cold atom experiments.
Augmented Reality Demonstrations for Scalable Robot Imitation Learning

Robot Imitation Learning (IL) is a widely used method for training robots to perform manipulation tasks that involve mimicking human demonstrations to acquire skills. However, its practicality has been limited due to its requirement that users be trained in operating real robot arms to provide demonstrations. This paper presents an innovative solution: an Augmented Reality (AR)-assisted framework for demonstration collection, empowering non-roboticist users to produce demonstrations for robot IL using devices like the HoloLens 2. Our framework facilitates scalable and diverse demonstration collection for real-world tasks. We validate our approach with experiments on three classical robotics tasks: reach, push, and pick-and-place. The real robot performs each task successfully while replaying demonstrations collected via AR.
Automatic Outlier Rectification via Optimal Transport

In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically use a two-stage procedure: first, outliers are detected and removed, and then estimation is performed on the cleaned data. However, this approach does not inform outlier removal with the estimation task, leaving room for improvement. To address this limitation, we propose an automatic outlier rectification mechanism that integrates rectification and estimation within a joint optimization framework. We take the first step to utilize an optimal transport distance with a concave cost function to construct a rectification set in the space of probability distributions. Then, we select the best distribution within the rectification set to perform the estimation task. Notably, the concave cost function we introduced in this paper is the key to making our estimator effectively identify the outlier during the optimization process. We discuss the fundamental differences between our estimator and optimal transport-based distributionally robust optimization estimator. finally, we demonstrate the effectiveness and superiority of our approach over conventional approaches in extensive simulation and empirical analyses for mean estimation, least absolute regression, and the fitting of option implied volatility surfaces.
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition

In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distinct modality gap between images and events. In particular, as only the source model is available, a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly, inspired by event-to-video reconstruction methods, we propose a reconstruction-based modality bridging (RMB) module, which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e., labels) from the source model. We then propose a multi-representation knowledge adaptation (MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.
Fuzzy c-Means Classifier for Relational Data

This paper proposes a relational version of the fuzzy c-means (FCM) classifier in which relational data instead of object data are used. The classifier based on the relational clustering is called "relational classifier". The classifier is useful when a feature space has an extremely high dimensionality that exceeds the number of objects and many of the feature values are missing, or when only relational data are available instead of the object data. The relational data is represented by a matrix in terms of distances (dissimilarity) between object data, and is not concerned with the relational database. The clustering algorithm used in the classifier includes, as a special case, the relational dual of FCM proposed by Hathaway, Davenport and Bezdek and can be seen as a simultaneous application of multidimensional scaling and clustering. The computational intensity of the classifier is comparable to Gaussian mixture classifier (GMC). The proposed classifier outperforms well established relational classifier known as k-nearest neighbor (k-NN) on several benchmark datasets from the UCI ML repository
Muscle phosphofructokinase deficiency

To the Editor: A late-onset myopathic variant of phosphofructokinase (PFK) deficiency has been described in rare patients of Ashkenazic descent1,2 and proposed as a possible unique disorder on the basis of ethnic and genetic considerations.3 A recent article published by Sivakumar et al.4 provides elements to question this hypothesis. These authors described three patients from two generations of an Ashkenazi Jewish family with a partial PFK deficiency and late-onset muscle weakness. A surprising feature of these patients was that, despite a partial (33%) muscle PFK activity detected in vitro, they presented with all the clinical features of complete PFK deficiency, including lifelong exercise intolerance, hyperuricemia, and hemolysis. …