Miracle-dz/UniFAR-FTUs huggingface dataset:
EdgeBERT: Optimizing On-Chip Inference for Multi-Task NLP

Transformer-based language models such as BERT provide significant accuracy improvement to a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-constrained edge platforms with strict latency requirements. We present EdgeBERT an in-depth and principled algorithm and hardware design methodology to achieve minimal latency and energy consumption on multi-task NLP inference. Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latency and memory savings, respectively, with less than 1%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization. Furthermore, in order to maximize the benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory. Altogether, EdgeBERT enables fully on-chip inference acceleration of NLP workloads with 5.2x, and 157x lower energy than that of an un-optimized accelerator and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Tilting objects in periodic triangulated categories.

A triangulated category $\mathcal{T}$ whose suspension functor $\Sigma$ satisfies $\Sigma^m \simeq \mathrm{Id}_{\mathcal{T}}$ is called an $m$-periodic triangulated category. Such a category does not have a tilting object by the periodicity. In this paper, we introduce the notion of an $m$-periodic tilting object in an $m$-periodic triangulated category, which is a periodic analogue of a tilting object in a triangulated category, and prove that an $m$-periodic triangulated category having an $m$-periodic tilting object is triangulated equivalent to the $m$-periodic derived category of an algebra under some homological assumptions. As an application, we construct a triangulated equivalence between the stable category of a self-injective algebra and the $m$-periodic derived category of a hereditary algebra.
Application Value of Triton X-100 to Modified Hodge Test and Carbapenem Inactivation Method in the Detection of Acinetobacter baumannii Carbapenemase

Aim To compare the sensitivity and specificity before and after the addition of Triton X-100 in the modified Hodge test (MHT) and carbapenem inactivation method (CIM) for the detection of carbapenemase in Acinetobacter baumannii. Materials and Methods A total of 135 isolates of A. baumannii (83 carbapenem-resistant and 52 carbapenem-sensitive) were selected and the carbapenemase genotypes were detected using PCR. Carbapenemase phenotypes were tested using the MHT, Triton-MHT (THT), CIM, modified CIM (mCIM), and Triton-CIM (TCIM). Different concentrations (0.05, 0.1, 0.25, and 0.5% v/v) of Triton X-100 were used in the TCIM. Results The sensitivity was determined to be 59.03% (MHT), 100% (THT), 6.02% (CIM), 8.43% (mCIM), 71.08% (TCIM 0.05%), 100% (TCIM 0.1%), 97.59% (TCIM 0.25%), and 96.38% (TCIM 0.5%) in 83 carbapenemase-producing isolates, and the specificity for each of these methods was 100%. Conclusion The addition of Triton X-100 while using the MHT and CIM could significantly improve the sensitivity in the detection of A. baumannii carbapenemase with a specificity of 100%. A concentration of 0.1% v/v Triton X-100 showed the best results in TCIM.
Learning geometry-image representation for 3D point cloud generation

We study the problem of generating point clouds of 3D objects. Instead of discretizing the object into 3D voxels with huge computational cost and resolution limitations, we propose a novel geometry image based generator (GIG) to convert the 3D point cloud generation problem to a 2D geometry image generation problem. Since the geometry image is a completely regular 2D array that contains the surface points of the 3D object, it leverages both the regularity of the 2D array and the geodesic neighborhood of the 3D surface. Thus, one significant benefit of our GIG is that it allows us to directly generate the 3D point clouds using efficient 2D image generation networks. Experiments on both rigid and non-rigid 3D object datasets have demonstrated the promising performance of our method to not only create plausible and novel 3D objects, but also learn a probabilistic latent space that well supports the shape editing like interpolation and arithmetic.
Connecting turbulent velocities and magnetic fields in galaxy cluster simulations with active galactic nuclei jets

The study of velocity fields of the hot gas in galaxy clusters can help to unravel details of microphysics on small-scales and to decipher the nature of feedback by active galactic nuclei (AGN). Likewise, magnetic fields as traced by Faraday rotation measurements (RMs) inform about their impact on gas dynamics as well as on cosmic ray production and transport. We investigate the inherent relationship between large-scale gas kinematics and magnetic fields through non-radiative magnetohydrodynamical simulations of the creation, evolution and disruption of AGN jet-inflated lobes in an isolated Perseus-like galaxy cluster, with and without pre-existing turbulence. In particular, we connect cluster velocity measurements with mock RM maps to highlight their underlying physical connection, which opens up the possibility of comparing turbulence levels in two different observables. For single jet outbursts, we find only a local impact on the velocity field, i.e. the associated increase in velocity dispersion is not volume-filling. Furthermore, in a setup with pre-existing turbulence, this increase in velocity dispersion is largely hidden. We use mock X-ray observations to show that at arcmin resolution, the velocity dispersion is therefore dominated by existing large-scale turbulence and is only minimally altered by the presence of a jet. For the velocity structure of central gas uplifted by buoyantly rising lobes, we find fast, coherent outflows with low velocity dispersion. Our results highlight that projected velocity distributions show complex structures which pose challenges for the interpretation of observations.
Red Blood Cell Distribution Width Is Associated with Deterioration of Renal Function and Cardiovascular Morbidity and Mortality in Patients with Diabetic Kidney Disease

We sought to investigate the possible association between Red Blood Cell Distribution Width (RDW), vascular calcification, oxidative stress and renal function and all-cause/cardiovascular (CV) mortality, CV events and progression of kidney disease in a cohort of patients with Diabetic Kidney Disease (DKD). Carotid intima media thickness (cIMT) and oxidized low-density cholesterol were measured in 104 Type 2 Diabetes Mellitus (T2DM) patients with established DKD, distributed in all five stages of kidney disease and 38 diabetics with normal renal function. All patients were followed for 7 years with end-points all-cause and CV mortality, CV events and progression to End-Stage Renal Disease (ESRD). RDW was positively correlated with diabetes duration (r = 0.19, p = 0.023) and albuminuria (r = 0.29, p = 0.002). Multivariate regression analysis revealed that RDW was a strong, independent predictor of cIMT value (β = 0.031, p = 0.012). Kaplan-Meier curves and Cox proportional hazard models revealed that after adjustment for several cofounders, RDW was a significant and independent predictor for all-cause mortality, CV mortality, CV event and progression to ESRD (HR 1.75, p = 0.001, HR 2.03, p = 0.001, HR = 1.66, p < 0.0001 and HR 2.14, p = 0.001 respectively). RDW predicts mortality, CV events and deterioration of renal function in DKD, probably reflecting atherosclerosis.
PCPs: Patient Cardiac Prototypes

Many clinical deep learning algorithms are population-based and difficult to interpret. Such properties limit their clinical utility as population-based findings may not generalize to individual patients and physicians are reluctant to incorporate opaque models into their clinical workflow. To overcome these obstacles, we propose to learn patient-specific embeddings, entitled patient cardiac prototypes (PCPs), that efficiently summarize the cardiac state of the patient. To do so, we attract representations of multiple cardiac signals from the same patient to the corresponding PCP via supervised contrastive learning. We show that the utility of PCPs is multifold. First, they allow for the discovery of similar patients both within and across datasets. Second, such similarity can be leveraged in conjunction with a hypernetwork to generate patient-specific parameters, and in turn, patient-specific diagnoses. Third, we find that PCPs act as a compact substitute for the original dataset, allowing for dataset distillation.
Three-dimensional segmentation of the scoliotic spine from MRI using unsupervised volume-based MR-CT synthesis

Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from computed tomography (CT) images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 ± 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.
Demography of galactic technosignatures

Probabilistic arguments about the existence of technological life beyond Earth traditionally refer to the Drake equation to draw possible estimates of the number of technologically advanced civilizations releasing, either intentionally or not, electromagnetic emissions in the Milky Way. Here, we introduce other indicators than Drake's number $N_D$ to develop a demography of artificial emissions populating the Galaxy. We focus on three main categories of statistically independent signals (isotropic, narrow beams, and rotating beacons) to calculate the average number $N_G$ of emission processes present in the Galaxy and the average number of them crossing Earth, $\bar{k}$, which is a quantity amenable to statistical estimation from direct observations. We show that $\bar{k}$ coincides with $N_D$ only for isotropic emissions, while $\bar{k}$ can be orders of magnitude smaller than $N_D$ in the case of highly directional signals. We further show that while $N_D$ gives the number of emissions being released at the present time, $N_G$ considers also the signals from no longer active emitters but whose emissions still occupy the Galaxy. We find that as long as the average longevity of the emissions is shorter than about $10^5$ yr, $N_G$ is fully determined by the rate of emissions alone, in contrast to $N_D$ and $\bar{k}$ which depend also on the emission longevity. Finally, using analytic formulas of $N_G$, $N_D$, and $\bar{k}$ determined for each type of emission processes here considered, we provide a comprehensive overview of the values these quantities can possibly achieve as functions of the emission birthrates, longevities, and directionality.
Lattice Fusion Networks for Image Denoising

A novel method for feature fusion in convolutional neural networks is proposed in this work. Different feature fusion techniques are suggested to facilitate the flow of information and improve the training of deep neural networks. Some of these techniques as well as the proposed model can be considered a type of Directed Acyclic Graph (DAG) Network, where a layer can receive inputs from other layers and have outputs to other layers. In the proposed general framework of Lattice Fusion Network (LFN), feature maps of each convolutional layer are passed to other layers based on a lattice graph structure, where nodes are convolutional layers. To investigate the performance of the model, a specific design based on the general framework of LFN is implemented for image denoising. Results are compared with state of the art methods. The proposed model produced competitive results with far fewer learnable parameters, which shows the effectiveness of LFNs for training of deep neural networks
A Review of Recent Advances of Binary Neural Networks for Edge Computing

Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains, such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This article reviews the recent advances on binary neural network (BNN) and 1-bit convolutional neural network technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner

Author disambiguation arises when different authors share the same name, which is a critical task in digital libraries, such as DBLP, CiteULike, CiteSeerX, etc. While the state-of-the-art methods have developed various paper embedding-based methods performing in a top-down manner, they primarily focus on the ego-network of a target name and overlook the low-quality collaborative relations existed in the ego-network. Thus, these methods can be suboptimal for disambiguating authors.In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner. Initially, we build a stable collaboration network based on stable collaborative relations. To further improve the recall, we build a probabilistic generative model to reconstruct the complete collaboration network. In addition, for newly published papers, we can incrementally judge who publish them via only computing the posterior probabilities. We have conducted extensive experiments on a large-scale DBLP dataset to evaluate IUAD. The experimental results demonstrate that IUAD not only achieves the promising performance, but also outperforms comparable baselines significantly. Codes are available at https://github.com/papergitgit/IUAD.
Overcoming Measurement Inconsistency In Deep Learning For Linear Inverse Problems: Applications In Medical Imaging

The remarkable performance of deep neural networks (DNNs) currently makes them the method of choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images. In these applications, DNNs invert a forward operator by finding, via training data, a map between the measurements and the input images. It is then expected that the map is still valid for the test data. This framework, however, introduces measurement inconsistency during testing. We show that such inconsistency, which can be critical in domains like medical imaging or defense, is intimately related to the generalization error. We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency. Experiments on MR images show that enforcing measurement consistency via our method can lead to large gains in reconstruction performance.
Semi-Supervised Learning of Mutually Accelerated Multi-Contrast MRI Synthesis without Fully-Sampled Ground-Truths

This study proposes a novel semi-supervised learning framework for mutually accelerated multi-contrast MRI synthesis that recovers high-quality images without demanding large training sets of costly fully-sampled source or ground-truth target images. The proposed method presents a selective loss function expressed only on a subset of the acquired k-space coefficients and further leverages randomized sampling patterns across training subjects to effectively learn relationships among acquired and nonacquired k-space coefficients at all locations. Comprehensive experiments performed on multi-contrast brain images clearly demonstrate that the proposed method maintains equivalent performance to the gold-standard method based on fully-supervised training while alleviating undesirable reliance of the current synthesis methods on large-scale fully-sampled MRI acquisitions.
Improvement of Gait after 4 Weeks of Wearable Focal Muscle Vibration Therapy for Individuals with Diabetic Peripheral Neuropathy

People with diabetic peripheral neuropathy (DPN) experience lower quality of life caused by associated balance, posture, and gait impairments. While focal muscle vibration (FMV) has been associated with improvements in gait performance in individuals with neurological disorders, little is known about its effectiveness in patients with DPN. The purpose of this study was to investigate the effect of FMV on gait outcomes in patients with DPN. The authors randomized 23 participants into three FMV intervention groups depending upon the delivery of vibration. Participants applied wearable FMV to the bilateral quadriceps, gastrocnemius, and tibialis anterior, 10 min per muscle, three times per week over a four-week period. Spatiotemporal, kinematic, and kinetic gait parameters at baseline and post-intervention were calculated and analyzed. Gait speed, cadence, stride time, left and right stance time, duration of double limb support, and left and right knee flexor moments significantly improved after four weeks of FMV. Trends toward significant improvements were noted in maximum left and right knee flexion. Results indicate that FMV therapy was associated with improvements in gait parameters in individuals with DPN, warranting expanded study of FMV therapy for long-term gait performance improvement in these individuals.
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In this paper we aim to extend this scope in three ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more accurate than the original DimeNet on the QM9 benchmark of equilibrium molecules. Second, we validate DimeNet++ on highly reactive molecules by developing the challenging COLL dataset, which contains distorted configurations of small molecules during collisions. Finally, we investigate ensembling and mean-variance estimation for uncertainty quantification with the goal of accelerating the exploration of the vast space of non-equilibrium structures. Our DimeNet++ implementation as well as the COLL dataset are available online.
Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels

The data-driven nature of deep learning models for semantic segmentation requires a large number of pixel-level annotations. However, large-scale and fully labeled medical datasets are often unavailable for practical tasks. Recently, partially supervised methods have been proposed to utilize images with incomplete labels to mitigate the data scarcity problem in the medical domain. As an emerging research area, the breakthroughs made by existing methods rely on either large-scale data or complex model design, which makes them 1) less practical for certain real-life tasks and 2) less robust for small-scale data. It is time to step back and think about the robustness of partially supervised methods and how to maximally utilize small-scale and partially labeled data for medical image segmentation tasks. To bridge the methodological gaps in label-efficient deep learning with partial supervision, we propose RAMP, a simple yet efficient data augmentation framework for partially supervised medical image segmentation by exploiting the assumption that patients share anatomical similarities. We systematically evaluate RAMP and the previous methods in various controlled multi-structure segmentation tasks. Compared to the mainstream approaches, RAMP consistently improves the performance of traditional segmentation networks on small-scale partially labeled data and utilize additional image-wise weak annotations.
A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint

In a teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multi-agent reinforcement learning (MARL), where agents must cooperate with one another, a student could fail to cooperate effectively with others even by following a teacher’s suggested actions, as the policies of all agents can change before convergence. When the number of times that agents communicate with one another is limited (i.e., there are budget constraints), an advising strategy that uses actions as advice could be less effective. We propose a partaker-sharer advising framework (PSAF) for cooperative MARL agents learning with budget constraints. In PSAF, each Q-learner can decide when to ask for and share its Q-values. We perform experiments in three typical multi-agent learning problems. The evaluation results indicate that the proposed PSAF approach outperforms existing advising methods under both constrained and unconstrained budgets. Moreover, we analyse the influence of advising actions and sharing Q-values on agent learning.
The Physics of Drying of Colloidal Dispersion: Pattern Formation and Desiccation Cracks

Drying of colloidal dispersion and their consolidation into a particulate deposit is a common phenomenon. This process involves various physical processes such as diffusion of liquid molecules into the ambient atmosphere and advection of dispersed particles via evaporation driven flow. The colloidal particles forming a dried deposit exhibits distinct patterns and frequently possess structural defects such as desiccation cracks. This chapter gives an introductory review of the drying of colloidal dispersion and various associated phenomena. In principle, the drying of colloid dispersion, the process of their consolidation, and fluid-flow dynamics are all studied in numerous drying configurations. Here we explain drying induced phenomena concerning sessile drop drying. We begin with an introduction to colloids, provide background on the physics of drying, and then explain the formation of pattern and the desiccation cracks. The role of evaporation driven flows and their influence on particle accumulation, the impact of various physical parameters on pattern formation and cracks are all briefly illustrated.