Miracle-dz/UniFAR-FTUs huggingface dataset:
Phantom: A High-Performance Computational Core for Sparse Convolutional Neural Networks

Sparse convolutional neural networks (CNNs) have gained significant traction over the past few years as sparse CNNs can drastically decrease the model size and computations, if exploited befittingly, as compared to their dense counterparts. Sparse CNNs often introduce variations in the layer shapes and sizes, which can prevent dense accelerators from performing well on sparse CNN models. Recently proposed sparse accelerators like SCNN, Eyeriss v2, and SparTen, actively exploit the two-sided or full sparsity, that is, sparsity in both weights and activations, for performance gains. These accelerators, however, either have inefficient micro-architecture, which limits their performance, have no support for non-unit stride convolutions and fully-connected (FC) layers, or suffer massively from systematic load imbalance. To circumvent these issues and support both sparse and dense models, we propose Phantom, a multi-threaded, dynamic, and flexible neural computational core. Phantom uses sparse binary mask representation to actively lookahead into sparse computations, and dynamically schedule its computational threads to maximize the thread utilization and throughput. We also generate a two-dimensional (2D) mesh architecture of Phantom neural computational cores, which we refer to as Phantom-2D accelerator, and propose a novel dataflow that supports all layers of a CNN, including unit and non-unit stride convolutions, and FC layers. In addition, Phantom-2D uses a two-level load balancing strategy to minimize the computational idling, thereby, further improving the hardware utilization. To show support for different types of layers, we evaluate the performance of the Phantom architecture on VGG16 and MobileNet. Our simulations show that the Phantom-2D accelerator attains a performance gain of 12x, 4.1x, 1.98x, and 2.36x, over dense architectures, SCNN, SparTen, and Eyeriss v2, respectively.
Speaker Generation

This work explores the task of synthesizing speech in non-existent human-sounding voices. We call this task "speaker generation", and present TacoSpawn, a system that performs competitively at this task. TacoSpawn is a recurrent attention-based text-to-speech model that learns a distribution over a speaker embedding space, which enables sampling of novel and diverse speakers. Our method is easy to implement, and does not require transfer learning from speaker ID systems. We present objective and subjective metrics for evaluating performance on this task, and demonstrate that our proposed objective metrics correlate with human perception of speaker similarity. Audio samples are available on our demo page1.
Thermodynamics, morphology, mechanics, and thermal transport of PMMA-PLA blends

Debashish Mukherji, ∗ Tiago Espinosa de Oliveira, and Jörg Rottler 3 Quantum Matter Institute, University of British Columbia, Vancouver BC V6T 1Z4, Canada Departamento de Farmacociências, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre 90050-170, Brazil Department of Physics and Astronomy, University of British Columbia, Vancouver BC V6T 1Z1, Canada (Dated: November 10, 2021)
Analysis of Work-Stealing and Parallel Cache Complexity

Parallelism has become extremely popular over the past decade, and there have been a lot of new parallel algorithms and software. The randomized work-stealing (RWS) scheduler plays a crucial role in this ecosystem. In this paper, we study two important topics related to the randomized work-stealing scheduler. Our first contribution is a simplified, classroom-ready version of analysis for the RWS scheduler. The theoretical efficiency of the RWS scheduler has been analyzed for a variety of settings, but most of them are quite complicated. In this paper, we show a new analysis, which we believe is easy to understand, and can be especially useful in education. We avoid using the potential function in the analysis, and we assume a highly asynchronous setting, which is more realistic for today's parallel machines. Our second and main contribution is some new parallel cache complexity for algorithms using the RWS scheduler. Although the sequential I/O model has been well-studied over the past decades, so far very few results have extended it to the parallel setting. The parallel cache bounds of many existing algorithms are affected by a polynomial of the span, which causes a significant overhead for high-span algorithms. Our new analysis decouples the span from the analysis of the parallel cache complexity. This allows us to show new parallel cache bounds for a list of classic algorithms. Our results are only a polylogarithmic factor off the lower bounds, and significantly improve previous results.
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization

Bayesian Optimization (BO), the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years. In particular, much attention has been paid to improving its efficiency on problems with many parameters to optimize. This attention has trickled down to the workhorse of high-dimensional BO, high-dimensional Gaussian process regression, which is also of independent interest. The great flexibility that the Gaussian process prior implies is a boon when modeling complicated, low-dimensional surfaces but simply says too little when dimension grows too large. A variety of structural model assumptions have been tested to tame high dimensions, from variable selection and additive decomposition to low-dimensional embeddings and beyond. Most of these approaches in turn require modifications of the acquisition function optimization strategy as well. Here, we review the defining structural model assumptions and discuss the benefits and drawbacks of these approaches in practice.
Robust Walking Based on MPC With Viability Guarantees

Model predictive control (MPC) has shown great success for controlling complex systems, such as legged robots. However, when closing the loop, the performance and feasibility of the finite horizon optimal control problem (OCP) solved at each control cycle are not guaranteed anymore. This is due to model discrepancies, the effect of low-level controllers, uncertainties, and sensor noise. To address these issues, we propose a modified version of a standard MPC approach used in legged locomotion with viability (weak forward invariance) guarantees. In this approach, instead of adding a (conservative) terminal constraint to the problem, we propose to use the measured state projected to the viability kernel in the OCP solved at each control cycle. Moreover, we use past experimental data to find the best cost weights, which measure a combination of performance, constraint satisfaction robustness, or stability (invariance). These interpretable costs measure the tradeoff between robustness and performance. For this purpose, we use Bayesian optimization to systematically design experiments that help efficiently collect data to learn a cost function leading to robust performance. Our simulation results with different realistic disturbances (i.e., external pushes, unmodeled actuator dynamics, and computational delay) show the effectiveness of our approach to create robust controllers for humanoid robots.
Forwarding-Lyapunov design for the stabilization of coupled ODEs and exponentially stable PDEs

This paper is about the stabilization of a cascade system composed by an infinite-dimensional system, that we suppose to be exponentially stable, and an ordinary differential equation (ODE), that we suppose to be marginally stable. The system is controlled through the infinite-dimensional system. Such a structure is particularly useful when applying the internal model approach on infinite-dimensional systems. Our strategy relies on the forwarding method, which uses a Lyapunov functional and a Sylvester equation to build a feedback-law. Under some classical assumptions in the output regulation theory, we prove that the closed-loop system is globally exponentially stable.
Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography

The limited-view issue can cause a low-quality image in photoacoustic (PA) computed tomography due to the limitation of geometric condition. The model-based method is used to resolve this problem, which contains different regularization. To adapt fast and high-quality reconstruction of limited-view PA data, in this Letter, a model-based method that combines the mathematical variational model with deep learning is proposed to speed up and regularize the unrolled procedure of reconstruction. A deep neural network is designed to adapt the step of the gradient updated term of data consistency in the gradient descent procedure, which can obtain a high-quality PA image with only a few iterations. A comparison of different model-based methods shows that our proposed scheme has superior performances (over 0.05 for SSIM) with the same iteration (three times) steps. Finally, we find that our method obtains superior results (0.94 value of SSIM for in vivo) with a high robustness and accelerated reconstruction.
Real2Sim2Real: Self-Supervised Learning of Physical Single-Step Dynamic Actions for Planar Robot Casting

This paper introduces the task of Planar Robot Casting (PRC): where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane toward a desired target. PRC allows the cable to reach points beyond the robot workspace and has applications for cable management in homes, warehouses, and factories. To efficiently learn a PRC policy for a given cable, we propose Real2Sim2Real, a self-supervised framework that automatically collects physical trajectory examples to tune parameters of a dynamics simulator using Differential Evolution, generates many simulated examples, and then learns a policy using a weighted combination of simulated and physical data. We evaluate Real2Sim2Real with three simulators, Isaac Gym-segmented, Isaac Gym-hybrid, and PyBullet, two function approximators, Gaussian Processes and Neural Networks (NNs), and three cables with differing stiffness, torsion, and friction. Results with 240 physical trials suggest that the PRC policies can attain median error distance (as % of cable length) ranging from 8 % to 14 %, outperforming baselines and policies trained on only real or only simulated examples. Code, data, and videos are available at https://tinyurl.com/robotcast.
E(2) Equivariant Self-Attention for Radio Astronomy

In this work we introduce group-equivariant self-attention models to address the problem of explainable radio galaxy classification in astronomy. We evaluate various orders of both cyclic and dihedral equivariance, and show that including equivariance as a prior both reduces the number of epochs required to fit the data and results in improved performance. We highlight the benefits of equivariance when using self-attention as an explainable model and illustrate how equivariant models statistically attend the same features in their classifications as human astronomers.
Approximating Fair Clustering with Cascaded Norm Objectives

We introduce the $(p,q)$-Fair Clustering problem. In this problem, we are given a set of points $P$ and a collection of different weight functions $W$. We would like to find a clustering which minimizes the $\ell_q$-norm of the vector over $W$ of the $\ell_p$-norms of the weighted distances of points in $P$ from the centers. This generalizes various clustering problems, including Socially Fair $k$-Median and $k$-Means, and is closely connected to other problems such as Densest $k$-Subgraph and Min $k$-Union. We utilize convex programming techniques to approximate the $(p,q)$-Fair Clustering problem for different values of $p$ and $q$. When $p\geq q$, we get an $O(k^{(p-q)/(2pq)})$, which nearly matches a $k^{\Omega((p-q)/(pq))}$ lower bound based on conjectured hardness of Min $k$-Union and other problems. When $q\geq p$, we get an approximation which is independent of the size of the input for bounded $p,q$, and also matches the recent $O((\log n/(\log\log n))^{1/p})$-approximation for $(p, \infty)$-Fair Clustering by Makarychev and Vakilian (COLT 2021).
Anomalous phase separation in a correlated electron system: Machine-learning–enabled large-scale kinetic Monte Carlo simulations

Significance Phase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high-Tc superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dynamical simulations in a representative correlated electron system. We observe an unusual relaxation process that is beyond the framework of classical phase-ordering theories. We also uncover a correlation-induced freezing behavior, which could be a generic feature of phase separation in correlated electron systems.
Tensile Behavior of Embedded Steel Sections with End Plates

AbstractAccording to standards, the exposed steel column bases and other steel-concrete connections use anchor bolts to resist the tensile (or pullout) actions. The embedded steel sections can replace the anchor bolts in resisting pullout forces. This paper numerically studies the pullout behavior of embedded steel sections with end plates under various conditions. The numerical models are generated with a computer program. To support the numerical results, two I-sections with end plates are tested under pullout forces. Also, a number of test results on headed studs, available in the literature, are compared with the numerical results. One of the important conclusions of the study is that the shape of the embedded section does not seriously affect its pullout strength. The pullout strength is proportionally increased with the plan size of the end plate. However, the rate of increase in strength of headed I-sections is remarkably lower than that of the headed studs. Embedded sections with end plates can de.
IMPROBED: Multiple Problem-Solving Brain via Evolved Developmental Programs

Abstract Artificial neural networks (ANNs) were originally inspired by the brain; however, very few models use evolution and development, both of which are fundamental to the construction of the brain. We describe a simple neural model, called IMPROBED, in which two neural programs construct an artificial brain that can simultaneously solve multiple computational problems. One program represents the neuron soma and the other the dendrite. The soma program decides whether neurons move, change, die, or replicate. The dendrite program decides whether dendrites extend, change, die, or replicate. Since developmental programs build networks that change over time, it is necessary to define new problem classes that are suitable to evaluate such approaches. We show that the pair of evolved programs can build a single network from which multiple conventional ANNs can be extracted, each of which can solve a different computational problem. Our approach is quite general and it could be applied to a much wider variety of problems.
Optochemical Control of Bacterial Gene Expression: Novel Photocaged Compounds for Different Promoter Systems

Photocaged compounds are applied for implementing precise, optochemical control of gene expression in bacteria. To broaden the scope of UV‐light‐responsive inducer molecules, six photocaged carbohydrates were synthesized and photochemically characterized, with the absorption exhibiting a red‐shift. Their differing linkage through ether, carbonate, and carbamate bonds revealed that carbonate and carbamate bonds are convenient. Subsequently, those compounds were successfully applied in vivo for controlling gene expression in E. coli via blue light illumination. Furthermore, benzoate‐based expression systems were subjected to light control by establishing a novel photocaged salicylic acid derivative. Besides its synthesis and in vitro characterization, we demonstrate the challenging choice of a suitable promoter system for light‐controlled gene expression in E. coli. We illustrate various bottlenecks during both photocaged inducer synthesis and in vivo application and possibilities to overcome them. These findings pave the way towards novel caged inducer‐dependent systems for wavelength‐selective gene expression.
Insightful exploring of advanced nanocarriers for the topical/transdermal treatment of skin diseases

Abstract Dermatological products constitute a big segment of the pharmaceutical market. From conventional products to more advanced ones, a wide variety of dosage forms have been developed till current date. A representative of the advanced delivery means is carrier-based systems, which can load large number of drugs for treatment of dermatological diseases, or simply for cosmeceutical purposes. To make them more favorable for topical delivery, further incorporation of these carriers in a topical vehicle, such as gels or creams is made. Therefore in this review article, an overview is compiled of the most commonly encountered novel carrier based topical delivery systems; namely lipid based (nanoemulsions, microemulsions, solid lipid nanoparticles [SLNs] and nanostructured lipid carriers [NLCs]), and vesicular carriers (non-deformable, such as liposomes, niosomes, emulsomes and cerosomes, and deformable, such as transfersomes, ethosomes, transethosomes, and penetration enhancer vesicles), with special emphasis on those loaded in a secondary gel vehicle. A special focus was made on the commonly encountered dermatological diseases, such as bacterial and fungal infections, psoriasis, dermatitis, eczema, vitiligo, oxidative damage, aging, alopecia, and skin cancer.
Torque ripple minimization of a switched reluctance motor including mutual inductances via sliding mode control technique

Switched reluctance motor (SRM) drives offer the advantages of simple and robust motor construction, high speeds, high overall efficiencies over a wide operating range of torque and speed, simple power converter circuits with a reduced number of switches and excellent controllability. However, the switched reluctance motor has a drawback in that it generates acoustic noise. This study aims to minimize the acoustic noise of a 8/6 switched reluctance motor with sum of the square of the phase currents and variable structure system (VSS) with sliding mode control (SMC) methods in the linear region and in presence of mutual inductances. In this study, the torque ripple of switched reluctance motor has been greatly removed and the acoustic noise has also been reduced. Sliding mode control techniques have been applied to switched reluctance motors in this study and successful results has been obtained. The results show that the sliding mode control is effective in reducing the torque ripple of the motor, compensating for the nonlinear torque characteristics, and making the drive insensitive to parameter variations and disturbances. A mathematical model of a 8/6 switched reluctance motor, the design of the sliding mode controller and simulation results are given in this paper.
Vibration energy harvesting using a spherical permanent magnet

The authors recently reported on a hybrid rotary-translational vibration energy harvesting approach using a spherical permanent-magnet and employing cycloidal motion as a mechanical amplifier. The rotary-translational harvesting approach, which is resonant in nature, can yield approximately twice the e.m. f. compared with a similar translationalonly device. This paper explores the analytic and numerical modelling of the rotary-translational harvester with the goal of finding an efficient method for design optimisation.
Zero-impact emission limits of enterprise-scale air pollutants—a case study of a typical petrochemical enterprise in Shanghai Chemical Industry Park

ABSTRACT The implementation of pollutant emission control has made initial achievements in the plant power, iron, and steel industries in China. To further improve air quality, it is of great significance to carry out research on zero-impact emissions of the petrochemical industry. Based on the existing concept and practice of zero emissions, this study proposes the concept of zero-impact emissions, taking emission concentration as the constraint. A typical petrochemical enterprise (namely Enterprise A) in Shanghai Chemical Industry Park as the research object, and used the CALPUFF model to simulate the target pollutant emissions (i.e. sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter (PM), and volatile organic compounds (VOCs)). The current emission standard, spatial distributions, and emission heights of chimneys in Enterprise A was considered as the baseline emission scenario and taking the zero-impact emission as a target for simulation. The results show that the current emission standards of NOx and VOCs (benzene) exceeded the zero-impact emission limits, and needed to be reduced by 22% and 87.5%, respectively. Moreover, the areas that exceeded the zero-impact concentration limits were located northwest of the chimneys and Hangzhou Bay. In terms of seasonal effects, the wind conditions in spring were more adverse for the enterprise to achieve zero-impact emissions. Based on the simulation, the zero-impact emission limits of SO2, NOx, PM, and VOCs (benzene) for Enterprise A were 50 mg/m 3, 78 mg/m 3, 10 mg/m 3, and 0.32 mg/m 3, respectively. Implications: Through case study, this paper solves the environmental management issue which is of universal significance for chemical industry park. The concept of zero-impact emissions and the determination method of zero-impact concentration limits proposed in this study could be used as references for related research on cutting emissions. Although the conclusion of this study about the emission limits is not suitable for other enterprises to apply directly, the calculation method of zero-impact emission limit can be used by other enterprises. Furthermore, the zero-impact emission limits on park scale can be determined after comprehensive evaluation based on the calculation results of multiple enterprises.
Incorporation of Escorting Children to School in Modeling Individual Daily Activity Patterns of Household Members

Escorting children to school is a common travel arrangement in a household with schoolchildren. This escorting task affects travel patterns of the adult household members as accommodations are made for dropping children off at school or picking them up or doing both. Approaches to modeling joint travel arrangements between adults and children with respect to escorting have been previously suggested. However, examples of implementing such models in the framework of an operational activity-based model (ABM) are limited. This paper focuses on the explicit modeling of the escorting of children to school by adults and takes into account the possible bundling of escorting tasks in households with multiple children. The developed model is part of the regional ABM system currently being developed for the Maricopa Association of Governments in Arizona. Such a model allows for constraining the travel schedules of workers who tend to escort children on their way to and from work. Escorting has important policy implications because workers who escort children to and from school are very restricted in changing their departure times to and from work and in switching to transit; these restrictions are not evident otherwise. A choice model was formulated and estimated for each household by outbound (to school) and inbound (from school) escorting needs that were dependent on the number of schoolchildren, options of bundling children for escorting on one tour, and number of available chauffeurs in the household.