Miracle-dz/UniFAR-FTUs huggingface dataset:
Rewriting History: Repurposing Domain-Specific CGRAs

Coarse-grained reconfigurable arrays (CGRAs) are domain-specific devices promising both the flexibility of FPGAs and the performance of ASICs. However, with restricted domains comes a danger: designing chips that cannot accelerate enough current and future software to justify the hardware cost. We introduce FlexC, the first flexible CGRA compiler, which allows CGRAs to be adapted to operations they do not natively support. FlexC uses dataflow rewriting, replacing unsupported regions of code with equivalent operations that are supported by the CGRA. We use equality saturation, a technique enabling efficient exploration of a large space of rewrite rules, to effectively search through the program-space for supported programs. We applied FlexC to over 2,000 loop kernels, compiling to four different research CGRAs and 300 generated CGRAs and demonstrate a 2.2$\times$ increase in the number of loop kernels accelerated leading to 3$\times$ speedup compared to an Arm A5 CPU on kernels that would otherwise be unsupported by the accelerator.
Preliminary characterization of an active spiral galaxy near m57

M57 is one of the most observed and popular planetary nebulae. Recently, the James Webb Space Telescope photographed it, capturing fascinating images (NASA, 2023). As is the case with other well-known and popular objects, there are other nearby astronomical objects of interest that can sometimes go unnoticed. Very close to this nebula (at coordinates 18:53:18.81 +33:03:59.89 -in j2000-), there is an intriguing spiral galaxy of type SBbc, known as IC 1296. At a greater angular distance from M57 and IC 1296, at coordinates 18:53:09.625 +33:05:38.85, there is a very faint object called WISEA J185309.64+330538.7. The author presents preliminary results of the spectral analyses of both galaxies, obtained at the Skinakas Observatory (Greece), providing some conclusions about their types and, in the case of WISEA J185309.64+330538.7, its distance. Both galaxies exhibit traces of having active galactic nuclei, with WISEA J185309.64+330538.7 being a radio galaxy with Seyfert 1 characteristics, while IC 1296 shows some lines that could indicate its membership in the intermediate category Seyfert 1.5 (between Seyfert 1 and 2), in addition to being a known X-ray emitter.
Stochastic Approximation MCMC, Online Inference, and Applications in Optimization of Queueing Systems

Stochastic approximation Markov Chain Monte Carlo (SAMCMC) algorithms are a class of online algorithms having wide-ranging applications, particularly within Markovian systems. In this work, we study the optimization of steady-state queueing systems via the general perspective of SAMCMC. Under a practical and verifiable assumption framework motivated by queueing systems, we establish a key characteristic of SAMCMC, namely the Lipschitz continuity of the solution of Poisson equations. This property helps us derive a finite-step convergence rate and a regret bound for SAMCMC's final output. Leveraging this rate, we lay the foundation for a functional central limit theory (FCLT) pertaining to the partial-sum process of the SAMCMC trajectory. This FCLT, in turn, inspires an online inference approach designed to furnish consistent confidence intervals for the true solution and to quantify the uncertainties surrounding SAMCMC outputs. To validate our methodologies, we conduct extensive numerical experiments on the efficient application of SAMCMC and its inference techniques in the optimization of GI/GI/1 queues in steady-state conditions.
Calibrating baryonic feedback with weak lensing and fast radio bursts

One of the key limitations of large-scale structure surveys of the current and future generation, such as Euclid, LSST-Rubin or Roman, is the influence of feedback processes on the distribution of matter in the Universe. This effect, called baryonic feedback, modifies the matter power spectrum on non-linear scales much stronger than any cosmological parameter of interest. Constraining these modifications is therefore key to unlock the full potential of the upcoming surveys, and we propose to do so with the help of Fast Radio Bursts (FRBs). FRBs are short, astrophysical radio transients of extragalactic origin. Their burst signal is dispersed by the free electrons in the large-scale-structure, leading to delayed arrival times at different frequencies characterised by the dispersion measure (DM). Since the dispersion measure is sensitive to the integrated line-of-sight electron density, it is a direct probe of the baryonic content of the Universe. We investigate how FRBs can break the degeneracies between cosmological and feedback parameters by correlating the observed Dispersion Measure with the weak gravitational lensing signal of a Euclid-like survey. In particular we use a simple one-parameter model controlling baryonic feedback, but we expect similar findings for more complex models. Within this model we find that $\sim 10^4$ FRBs are sufficient to constrain the baryonic feedback 10 times better than cosmic shear alone. Breaking this degeneracy will tighten the constraints considerably, for example we expect a factor of two improvement on the sum of neutrino masses
MAPLE: Mobile App Prediction Leveraging Large Language model Embeddings

In recent years, predicting mobile app usage has become increasingly important for areas like app recommendation, user behaviour analysis, and mobile resource management. Existing models, however, struggle with the heterogeneous nature of contextual data and the user cold start problem. This study introduces a novel prediction model, Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE), which employs Large Language Models (LLMs) and installed app similarity to overcome these challenges. MAPLE utilises the power of LLMs to process contextual data and discern intricate relationships within it effectively. Additionally, we explore the use of installed app similarity to address the cold start problem, facilitating the modelling of user preferences and habits, even for new users with limited historical data. In essence, our research presents MAPLE as a novel, potent, and practical approach to app usage prediction, making significant strides in resolving issues faced by existing models. MAPLE stands out as a comprehensive and effective solution, setting a new benchmark for more precise and personalised app usage predictions. In tests on two real-world datasets, MAPLE surpasses contemporary models in both standard and cold start scenarios. These outcomes validate MAPLE's capacity for precise app usage predictions and its resilience against the cold start problem. This enhanced performance stems from the model's proficiency in capturing complex temporal patterns and leveraging contextual information. As a result, MAPLE can potentially improve personalised mobile app usage predictions and user experiences markedly.
Differentiable Boustrophedon Paths That Enable Optimization Via Gradient Descent

This paper introduces a differentiable representation for the optimization of boustrophedon path plans in convex polygons, explores an additional parameter of these path plans that can be optimized, discusses the properties of this representation that can be leveraged during the optimization process and shows that the previously published attempt at optimization of these path plans was too coarse to be practically useful. Experiments were conducted to show that this differentiable representation can reproduce scores from traditional discrete representations of boustrophedon path plans with high fidelity. Finally, optimization via gradient descent was attempted but found to fail because the search space is far more non-convex than was previously considered in the literature. The wide range of applications for boustrophedon path plans means that this work has the potential to improve path planning efficiency in numerous areas of robotics, including mapping and search tasks using uncrewed aerial systems, environmental sampling tasks using uncrewed marine vehicles, and agricultural tasks using ground vehicles, among numerous others applications.
Sparse and Privacy-enhanced Representation for Human Pose Estimation

We propose a sparse and privacy-enhanced representation for Human Pose Estimation (HPE). Given a perspective camera, we use a proprietary motion vector sensor(MVS) to extract an edge image and a two-directional motion vector image at each time frame. Both edge and motion vector images are sparse and contain much less information (i.e., enhancing human privacy). We advocate that edge information is essential for HPE, and motion vectors complement edge information during fast movements. We propose a fusion network leveraging recent advances in sparse convolution used typically for 3D voxels to efficiently process our proposed sparse representation, which achieves about 13x speed-up and 96% reduction in FLOPs. We collect an in-house edge and motion vector dataset with 16 types of actions by 40 users using the proprietary MVS. Our method outperforms individual modalities using only edge or motion vector images. Finally, we validate the privacy-enhanced quality of our sparse representation through face recognition on CelebA (a large face dataset) and a user study on our in-house dataset.
Training dynamic models using early exits for automatic speech recognition on resource-constrained devices

The possibility of dynamically modifying the computational load of neural models at inference time is crucial for on-device processing, where computational power is limited and time-varying. Established approaches for neural model compression exist, but they provide ar-chitecturally static models. In this paper, we investigate the use of early-exit architectures, that rely on intermediate exit branches, applied to large-vocabulary speech recognition. This allows for the development of dynamic models that adjust their computational cost to the available resources and recognition performance. Unlike previous works, besides using pre-trained backbones we also train the model from scratch with an early-exit architecture. Experiments on public datasets show that early-exit architectures from scratch not only preserve performance levels when using fewer encoder layers, but also improve task accuracy as compared to using single-exit models or using pre-trained models. Additionally, we investigate an exit selection strategy based on posterior probabilities as an alternative to frame-based entropy.
Learning Inertial Parameter Identification of Unknown Object with Humanoid Robot using Sim-to-Real Adaptation

—Understanding the dynamics of unknown object is crucial for collaborative robots including humanoids to more safely and accurately interact with humans. Most relevant literature leverage a force/torque sensor, prior knowledge of object, vision system, and a long-horizon trajectory which are often impractical. Moreover, these methods often entail solving non-linear optimization problem, sometimes yielding physically inconsistent results. In this work, we propose a fast learning-based inertial parameter estimation as more practical manner. We acquire a reliable dataset in a high-fidelity simulation and train a time-series data-driven regression model (e.g., LSTM) to estimate the inertial parameter of unknown objects. We also introduce a novel sim-to-real adaptation method combining Robot System Identification and Gaussian Processes to directly transfer the trained model to real-world application. We demonstrate our method with a 4-DOF single manipulator of physical wheeled humanoid robot, SATYRR. Results show that our method can identify the inertial parameters of various unknown objects faster and more accurately than conventional methods.
Spoofing attack augmentation: can differently-trained attack models improve generalisation?

A reliable deepfake detector or spoofing countermeasure (CM) should be robust in the face of unpredictable spoofing attacks. To encourage the learning of more generaliseable artefacts, rather than those specific only to known attacks, CMs are usually exposed to a broad variety of different attacks during training. Even so, the performance of deep-learning-based CM solutions are known to vary, sometimes substantially, when they are retrained with different initialisations, hyper-parameters or training data partitions. We show in this paper that the potency of spoofing attacks, also deep-learning-based, can similarly vary according to training conditions, sometimes resulting in substantial degradations to detection performance. Nevertheless, while a RawNet2 CM model is vulnerable when only modest adjustments are made to the attack algorithm, those based upon graph attention networks and self-supervised learning are reassuringly robust. The focus upon training data generated with different attack algorithms might not be sufficient on its own to ensure generaliability; some form of spoofing attack augmentation at the algorithm level can be complementary.
Multi-dimensional domain generalization with low-rank structures

In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data. However, this assumption does not always hold, especially in applications where the target population are not well-represented in the training data. This is a notable issue in health-related studies, where specific ethnic populations may be underrepresented, posing a significant challenge for researchers aiming to make statistical inferences about these minority groups. In this work, we present a novel approach to addressing this challenge in linear regression models. We organize the model parameters for all the sub-populations into a tensor. By studying a structured tensor completion problem, we can achieve robust domain generalization, i.e., learning about sub-populations with limited or no available data. Our method novelly leverages the structure of group labels and it can produce more reliable and interpretable generalization results. We establish rigorous theoretical guarantees for the proposed method and demonstrate its minimax optimality. To validate the effectiveness of our approach, we conduct extensive numerical experiments and a real data study focused on education level prediction for multiple ethnic groups, comparing our results with those obtained using other existing methods.
CD81 represses NF-κB in HCV-expressing hepatoma cells

The tetraspanin CD81 is one of the main entry receptors for Hepatitis C virus, which is a major causative agent to develop liver cirrhosis and hepatocellular carcinoma (HCC). Here, we identify CD81 as one of few surface proteins that are downregulated in HCV expressing hepatoma cells, discovering a functional role of CD81 beyond mediating HCV entry. CD81 was downregulated at the mRNA level in hepatoma cells that replicate HCV. Kinetics of HCV protein expression were increased in CD81-knockout cells and accompanied by enhanced cellular growth. Furthermore, loss of CD81 compensated for inhibition of pro-survival TBK1-signaling in HCV expressing cells. Analysis of functional phenotypes that could be associated with pro-survival signaling revealed that CD81 is a negative regulator of NF-κB. Interaction of the NF-κB subunits p50 and p65 was increased in cells lacking CD81. Similarly, we witnessed an overall increase in the total levels of phosphorylated and cellular p65 upon CD81-knockout. Finally, translocation of p65 in CD81-negative hepatoma cells was markedly induced upon stimulation with TNFα or PMA. Altogether, CD81 emerges as aregulator of pro-survival NF-κB signaling. Considering the important and established role of NF-κB for HCV replication and tumorigenesis, the downregulation of CD81 by HCV and the associated increase in NF-κB signaling might serve as viral mechanism to maintain persistent infection, ultimately causing chronic inflammation and HCC. Highlights CD81 is downregulated and transcriptionally silenced upon HCV genome replication Loss of CD81 is associated with increased cell growth and HCV expression CD81 suppresses NF-κB signaling. CD81 interferes with p65 activation and nuclear translocation
Modifier-Adaptation for Real-Time Optimal Periodic Operation

In this paper, we present the periodic modifier-adaptation formulation of the dynamic real time optimization. The proposed formulation uses gradient information to update the problem with affine modifiers so that, upon convergence, its solution matches the optimal steady periodic trajectory. Unlike other state of the art modifier-adaptation techniques, the proposed approach is able to converge not only to optimal steady states, but also to optimal periodic trajectories. The full control scheme to take the system from its current state to the optimal periodic trajectory is detailed. The convergence of the computed reference to the optimal periodic behaviour is shown by means of a periodic version of the quadruple tank benchmark.
Affordance-Driven Next-Best-View Planning for Robotic Grasping

Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a feasible grasp for target object via continuously observing scenes from new viewpoints. This policy is motivated by the observation that the grasp affordances of an occluded object can be better-measured under the view when the view-direction are the same as the grasp view. Specifically, our method leverages the paradigm of novel view imagery to predict the grasps affordances under previously unobserved view, and select next observation view based on the gain of the highest imagined grasp quality of the target object. The experimental results in simulation and on the real robot demonstrate the effectiveness of the proposed affordance-driven next-best-view planning policy. Additional results, code, and videos of real robot experiments can be found in the supplementary materials.
Spectrum Sensing Based on Energy Detection Algorithms Using GNU Radio and USRP for Cognitive Radio

Cognitive Radio technology focuses on the efficient use of the radio frequency spectrum. One of the challenging tasks for secondary users (SUs) is Spectrum Sensing. , used to accurately detect primary users (PUs) and not to interfere with their signals. In this paper. , we design a CR system in a real environment and test three spectrum sensing algorithms based on Energy Detection (ED). The capability of sensing the radio spectrum is implemented using two Universal Software Radio Peripheral (USRP) platforms combined with the GNU Radio software toolkit. The three ED algorithms are compared in terms of performance. , taking into account the PU activity duty cycle in a low SNR scenario. We present the performance of these three algorithms for DPSK modulation as a primary signal to check the feasibility of these methods.
Convolutional Deep Kernel Machines

Standard infinite-width limits of neural networks sacrifice the ability for intermediate layers to learn representations from data. Recent work (A theory of representation learning gives a deep generalisation of kernel methods, Yang et al. 2023) modified the Neural Network Gaussian Process (NNGP) limit of Bayesian neural networks so that representation learning is retained. Furthermore, they found that applying this modified limit to a deep Gaussian process gives a practical learning algorithm which they dubbed the deep kernel machine (DKM). However, they only considered the simplest possible setting: regression in small, fully connected networks with e.g. 10 input features. Here, we introduce convolutional deep kernel machines. This required us to develop a novel inter-domain inducing point approximation, as well as introducing and experimentally assessing a number of techniques not previously seen in DKMs, including analogues to batch normalisation, different likelihoods, and different types of top-layer. The resulting model trains in roughly 77 GPU hours, achieving around 99% test accuracy on MNIST, 72% on CIFAR-100, and 92.7% on CIFAR-10, which is SOTA for kernel methods.
Impact of 14 Days of Bed Rest in Older Adults and an Exercise Countermeasure on Body Composition, Muscle Strength, and Cardiovascular Function: Canadian Space Agency Standard Measures

Introduction: Head-down bed rest (HDBR) has long been used as an analog to microgravity, and it also enables studying the changes occurring with aging. Exercise is the most effective countermeasure for the deleterious effects of inactivity. The aim of this study was to investigate the efficacy of an exercise countermeasure in healthy older participants on attenuating musculoskeletal deconditioning, cardiovascular fitness level, and muscle strength during 14 days of HDBR as part of the standard measures of the Canadian Space Agency. Methods: Twenty-three participants (12 males and 11 females), aged 55–65 years, were admitted for a 26-day inpatient stay at the McGill University Health Centre. After 5 days of baseline assessment tests, they underwent 14 days of continuous HDBR followed by 7 days of recovery with repeated tests. Participants were randomized to passive physiotherapy or an exercise countermeasure during the HDBR period consisting of 3 sessions per day of either high-intensity interval training (HIIT) or low-intensity cycling or strength exercises for the lower and upper body. Peak aerobic power (V̇O2peak) was determined using indirect calorimetry. Body composition was assessed by dual-energy X-ray absorptiometry, and several muscle group strengths were evaluated using an adjustable chair dynamometer. A vertical jump was used to assess whole-body power output, and a tilt test was used to measure cardiovascular and orthostatic challenges. Additionally, changes in various blood parameters were measured as well as the effects of exercise countermeasure on these measurements. Results: There were no differences at baseline in main characteristics between the control and exercise groups. The exercise group maintained V̇O2peak levels similar to baseline, whereas it decreased in the control group following 14 days of HDBR. Body weight significantly decreased in both groups. Total and leg lean masses decreased in both groups. However, total body fat mass decreased only in the exercise group. Isometric and isokinetic knee extension muscle strength were significantly reduced in both groups. Peak velocity, flight height, and flight time were significantly reduced in both groups with HDBR. Conclusion: In this first Canadian HDBR study in older adults, an exercise countermeasure helped maintain aerobic fitness and lean body mass without affecting the reduction of knee extension strength. However, it was ineffective in protecting against orthostatic intolerance. These results support HIIT as a promising approach to preserve astronaut health and functioning during space missions, and to prevent deconditioning as a result of hospitalization in older adults.