Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepPseudo: Deep Pseudo-code Generation via Transformer and Code Feature Extraction

Pseudo-code written by natural language is helpful for novice de-velopers’ program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via Transformer and code feature extraction. In particular, DeepPseudo utilizes both Transformer encoder and code feature extractor to perform encoding for source code. Then it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose corpus gathered from a web application framework Django, which contains 18,805 pairs of Python statements and corresponding pseudo-code. We ﬁrst compare DeepPseudo with seven base-lines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo . Later, we analyze the rationality of the component settings (i.e.,
Dynamic Precision Analog Computing for Neural Networks

Analog electronic and optical computing exhibit tremendous advantages over digital computing for accelerating deep learning when operations are executed at low precision. Although digital architectures support programmable precision to increase efficiency, analog computing architectures today only support a single, static precision. In this work, we characterize the relationship between the effective number of bits (ENOB) of precision of analog processors, which is limited by noise, and digital bit precision for quantized neural networks. We propose extending analog computing architectures to support dynamic levels of precision by repeating operations and averaging the result, decreasing the impact of noise. To utilize dynamic precision, we propose a method for learning the precision of each layer of a pre-trained model without retraining network weights. We evaluate this method on analog architectures subject to shot noise, thermal noise, and weight noise and find that employing dynamic precision reduces energy consumption by up to 89% for computer vision models such as Resnet50 and by 24% for natural language processing models such as BERT. In one example, we apply dynamic precision to a shot-noise limited homodyne optical neural network and simulate inference at an optical energy consumption of 2.7 aJ/MAC for Resnet50 and 1.6 aJ/MAC for BERT with ${< }2\%$ accuracy degradation, implying that the optical energy consumption is unlikely to be the dominant cost.
Echo State Networks for Reinforcement Learning

An Echo State Network (ESN) is a type of single-layer recurrent neural network with randomly-chosen internal weights and a trainable output layer. We prove under mild conditions that a suﬃciently large Echo State Network can approximate the value function of a broad class of stochastic and deterministic control problems. Such control problems are generally non-Markovian. We describe how the ESN can form the basis for novel and computationally eﬃcient reinforcement learning algorithms in a non-Markovian framework. We demonstrate this theory with two examples. In the ﬁrst, we use an ESN to solve a deterministic, partially observed, control problem which is a simple game we call ‘Bee World’. In the second example, we consider a stochastic control problem inspired by a market making problem in mathematical ﬁnance. In both cases we can compare the dynamics of the algorithms with analytic solutions to show that even after only a single reinforcement policy iteration the algorithms perform with reasonable skill
SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs

Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects. Mod - elling interactions among agents is essential for accurately forecasting other agents' behaviour and achieving safe and comfortable motion planning. In this work, we propose SCOUT, a novel Attention-based Graph Neural Network that uses a flexible and generic representation of the scene as a graph for modelling interactions, and predicts socially - consistent trajec - tories of vehicles and Vulnerable Road Users (VRU s) under mixed traffic conditions. We explore three different attention mechanisms and test our scheme with both bird-eye - view and on-vehicle urban data, achieving superior performance than existing state-of - the-art approaches on InD and ApolloScape Trajectory benchmarks. Additionally, we evaluate our model's flexibility and transferability by testing it under completely new scenarios on RounD dataset. The importance and influence of each interaction in the final prediction is explored by means of Integrated Gradients technique and the visualization of the attention learned.
Probing within the Bondi radius of the ultramassive black hole in NGC 1600

We present deep (250 ks) Chandra observations of the nearby galaxy group NGC 1600, which has at its centre an ultramassive black hole (17±1.5 billion M ). The exceptionally large mass of the black hole coupled with its low redshift makes it one of only a handful of black holes for which spatially resolved temperature and density profiles can be obtained within the Bondi radius with the high spatial resolution of Chandra. We analyzed the hot gas properties within the Bondi accretion radius RB = 1. ′′2− 1. ′′7 = 0.38− 0.54 kpc. Within a ∼3 kpc radius, we find two temperature components with statistical significance. Both the single temperature and two temperature models show only a very slight rise in temperature towards the centre, and are consistent with being flat. This is in contrast with the expectation from Bondi accretion for a temperature profile which increases towards the centre, and appears to indicate that the dynamics of the gas are not being determined by the central black hole. The density profile follows a relatively shallow ρ ∝ r−[0.61±0.13] relationship within the Bondi radius, which suggests that the true accretion rate on to the black hole may be lower than the classical Bondi accretion rate.
Acoustically propelled nano- and microcones: fast forward and backward motion

We focus on cone-shaped nano- and microparticles, which have recently been found to show particularly strong propulsion when they are exposed to a traveling ultrasound wave, and study based on direct acoustofluidic computer simulations how their propulsion depends on the cones' aspect ratio. The simulations reveal that the propulsion velocity and even its sign are very sensitive to the aspect ratio, where short particles move forward whereas elongated particles move backward. Furthermore, we identify a cone shape that allows for a particularly large propulsion speed. Our results contribute to the understanding of the propulsion of ultrasound-propelled colloidal particles, suggest a method for separation and sorting of nano- and microcones concerning their aspect ratio, and provide useful guidance for future experiments and applications.
PAC-BUS: Meta-Learning Bounds via PAC-Bayes and Uniform Stability

We are motivated by the problem of providing strong generalization guarantees in the context of meta-learning. Existing generalization bounds are either challenging to evaluate or provide vacuous guarantees in even relatively simple settings. We derive a probably approximately correct (PAC) bound for gradient-based meta-learning using two different generalization frameworks in order to deal with the qualitatively different challenges of generalization at the “base” and “meta” levels. We employ bounds for uniformly stable algorithms at the base level and bounds from the PAC-Bayes framework at the meta level. The result is a PAC-bound that is tighter when the base learner adapts quickly, which is precisely the goal of meta-learning. We show that our bound provides a tighter guarantee than other bounds on a toy non-convex problem on the unit sphere and a text-based classiﬁcation example. We also present a practical regularization scheme motivated by the bound in settings where the bound is loose and demonstrate improved performance over baseline techniques.
Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks

Rate-Splitting Multiple Access (RSMA) is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA under the important setup of imperfect Channel State Information at the Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We derive a lower bound on the ergodic sum-rate of RSMA for an arbitrary number of transmit antennas, number of users, user speed and transmit power. Then, we study the power allocation between common and private streams and obtain a closed-form solution for optimal power allocation that maximizes the obtained lower bound. The proposed power allocation greatly reduces precoder design complexity for RSMA. By Link-Level Simulations (LLS), we demonstrate that RSMA with the proposed power allocation is robust to the degrading effects of user mobility and has significantly higher performance compared to conventional multi-user (massive) Multiple-Input Multiple-Output (MIMO) strategies. The work has important practical significance as results demonstrate that, in contrast to conventional multi-user (massive) MIMO whose performance collapse under mobility, RSMA can maintain reliable multi-user connectivity in mobile deployments.
Regret, stability & fairness in matching markets with bandit learners

Making an informed decision -- for example, when choosing a career or housing -- requires knowledge about the available options. Such knowledge is generally acquired through costly trial and error, but this learning process can be disrupted by competition. In this work, we study how competition affects the long-term outcomes of individuals as they learn. We build on a line of work that models this setting as a two-sided matching market with bandit learners. A recent result in this area states that it is impossible to simultaneously guarantee two natural desiderata: stability and low optimal regret for all agents. Resource-allocating platforms can point to this result as a justification for assigning good long-term outcomes to some agents and poor ones to others. We show that this impossibility need not hold true. In particular, by modeling two additional components of competition -- namely, costs and transfers -- we prove that it is possible to simultaneously guarantee four desiderata: stability, low optimal regret, fairness in the distribution of regret, and high social welfare.
Bayesian Neural Network Priors Revisited

Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs about the weight distributions or give optimal performance. To find better priors, we study summary statistics of neural network weights in networks trained using stochastic gradient descent (SGD). We find that convolutional neural network (CNN) and ResNet weights display strong spatial correlations, while fully connected networks (FCNNs) display heavy-tailed weight distributions. We show that building these observations into priors can lead to improved performance on a variety of image classification datasets. Surprisingly, these priors mitigate the cold posterior effect in FCNNs, but slightly increase the cold posterior effect in ResNets.
A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension.

Chronic thromboembolic pulmonary hypertension (CTEPH) is caused by recurrent or unresolved pulmonary thromboemboli, leading to perfusion defects and increased arterial wave reflections. CTEPH treatment aims to reduce pulmonary arterial pressure and reestablish adequate lung perfusion, yet patients with distal lesions are inoperable by standard surgical intervention. Instead, these patients undergo balloon pulmonary angioplasty (BPA), a multi-session, minimally invasive surgery that disrupts the thromboembolic material within the vessel lumen using a catheter balloon. However, there still lacks an integrative, holistic tool for identifying optimal target lesions for treatment. To address this insufficiency, we simulate CTEPH hemodynamics and BPA therapy using a multiscale fluid dynamics model. The large pulmonary arterial geometry is derived from a computed tomography (CT) image, whereas a fractal tree represents the small vessels. We model ring- and web-like lesions, common in CTEPH, and simulate normotensive conditions and four CTEPH disease scenarios; the latter includes both large artery lesions and vascular remodeling. BPA therapy is simulated by simultaneously reducing lesion severity in three locations. Our predictions mimic severe CTEPH, manifested by an increase in mean proximal pulmonary arterial pressure above 20 mmHg and prominent wave reflections. Both flow and pressure decrease in vessels distal to the lesions and increase in unobstructed vascular regions. We use the main pulmonary artery (MPA) pressure, a wave reflection index, and a measure of flow heterogeneity to select optimal target lesions for BPA. In summary, this study provides a multiscale, image-to-hemodynamics pipeline for BPA therapy planning for inoperable CTEPH patients.
On the stickiness of CO$_{2}$ and H$_{2}$O ice particles

Laboratory experiments revealed that CO2 ice particles stick less efficiently than H2O ice particles, and there is an order of magnitude difference in the threshold velocity for sticking. However, the surface energies and elastic moduli of CO2 and H2O ices are comparable, and the reason why CO2 ice particles were poorly sticky compared to H2O ice particles was unclear. Here we investigate the effects of viscoelastic dissipation on the threshold velocity for sticking of ice particles using the viscoelastic contact model derived by Krijt et al. We find that the threshold velocity for sticking of CO2 ice particles reported in experimental studies is comparable to that predicted for perfectly elastic spheres. In contrast, the threshold velocity for sticking of H2O ice particles is an order of magnitude higher than that predicted for perfectly elastic spheres. Therefore, we conclude that the large difference in stickiness between CO2 and H2O ice particles would mainly originate from the difference in the strength of viscoelastic dissipation, which is controlled by the viscoelastic relaxation time.
CrossStack: A 3-D Reconfigurable RRAM Crossbar Inference Engine

Deep neural network inference accelerators are rapidly growing in importance as we turn to massively parallelized processing beyond GPUs and ASICs. The dominant operation in feedforward inference is the multiply-and-accumlate process, where each column in a crossbar generates the current response of a single neuron. As a result, memristor crossbar arrays parallelize inference and image processing tasks very efficiently. In this brief, we present a 3-D active memristor crossbar array `CrossStack', which adopts stacked pairs of Al/TiO2/TiO2-x/Al devices with common middle electrodes. By designing CMOS-memristor hybrid cells used in the layout of the array, CrossStack can operate in one of two user-configurable modes as a reconfigurable inference engine: 1) expansion mode and 2) deep-net mode. In expansion mode, the resolution of the network is doubled by increasing the number of inputs for a given chip area, reducing IR drop by 22%. In deep-net mode, inference speed per-10-bit convolution is improved by 29\% by simultaneously using one TiO2/TiO2-x layer for read processes, and the other for write processes. We experimentally verify both modes on our $10\times10\times2$ array.
Neutron scattering studies on spin fluctuations in 
Sr2RuO4

K. Jenni, ∗ S. Kunkemöller, P. Steffens, Y. Sidis, R. Bewley, Z. Q. Mao, 6, 7 Y. Maeno, and M. Braden † II. Physikalisches Institut, Universität zu Köln, Zülpicher Str. 77, D-50937 Köln, Germany Institut Laue Langevin,71 avenue des Martyrs, 38000 Grenoble, France Laboratoire Léon Brillouin, C.E. A. /C.N. R.S. , F-91191 Gif-sur-Yvette CEDEX, France ISIS, United Kingdom Department of Physics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan Department of Physics, Tulane University, New Orleans, LA 70118, USA Department of Physics, Pennsylvania State University, University Park, PA 16802, USA (Dated: February 15, 2021)
Learning Gaussian–Bernoulli RBMs Using Difference of Convex Functions Optimization

The Gaussian–Bernoulli restricted Boltzmann machine (GB-RBM) is a useful generative model that captures meaningful features from the given $n$ -dimensional continuous data. The difficulties associated with learning GB-RBM are reported extensively in earlier studies. They indicate that the training of the GB-RBM using the current standard algorithms, namely contrastive divergence (CD) and persistent contrastive divergence (PCD), needs a carefully chosen small learning rate to avoid divergence which, in turn, results in slow learning. In this work, we alleviate such difficulties by showing that the negative log-likelihood for a GB-RBM can be expressed as a difference of convex functions if we keep the variance of the conditional distribution of visible units (given hidden unit states) and the biases of the visible units, constant. Using this, we propose a stochastic difference of convex (DC) functions programming (S-DCP) algorithm for learning the GB-RBM. We present extensive empirical studies on several benchmark data sets to validate the performance of this S-DCP algorithm. It is seen that S-DCP is better than the CD and PCD algorithms in terms of speed of learning and the quality of the generative model learned.
Radial Velocity Monitoring of the Young Star Hubble 4: Disentangling Star-spot Lifetimes from Orbital Motion

We studied the weak-lined T Tauri star Hubble 4, a known long-period binary, and its star-spot phenomena. We used optical radial velocity (RV) data taken over a span of 14 yr (2004–2010, 2017–2019) at the McDonald Observatory 2.7 m Harlan J. Smith Telescope and single epoch imaging from the Hubble Space Telescope (HST)/Wide Field Camera 3 instrument. The observed and apparent RV variations show contributions, respectively, from the binary motion as well as from a large spot group on one of the stars, presumed to be the primary. Fitting and removing the orbital signal from the RVs, we found the lower bound on the lifetime of a previously identified large spot group on the surface of the star to be at least 5.1 yr. An ∼5 yr lower limit is a long, but not unprecedented, duration for a single spot group. The later epoch data indicate significant spot evolution has occurred, placing an upper bound on the spot group lifetime at 12 yr. We find that pre-main-sequence evolutionary models for the age of Taurus (∼2 Myr), combined with component mass estimates from the literature, permit us to reproduce the HST relative photometry and the binary-induced contribution to the apparent RV variations. The long-lived star spot we find on Hubble 4 has significant implications for dynamo models in young stars, as it adds evidence for long lifetimes of magnetic field topologies. There are also significant implications for young star exoplanet searches, as long-lived coherent RV signals may be spot induced and not the result of planetary motion.
Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.
The Foundation Supernova Survey: Photospheric Velocity Correlations in Type Ia Supernovae

The ejecta velocities of Type Ia supernovae (SNe Ia), as measured by the Si ii λ6355 line, have been shown to correlate with other supernova properties, including color and standardized luminosity. We investigate these results using the Foundation Supernova Survey, with a spectroscopic data release presented here, and photometry analyzed with the SALT2 light-curve fitter. We find that the Foundation data do not show significant evidence for an offset in color between SNe Ia with high and normal photospheric velocities, with Δc = 0.004 ± 0.015. Our SALT2 analysis does show evidence for redder high-velocity SNe Ia in other samples, including objects from the Carnegie Supernova Project, with a combined sample yielding Δc = 0.018 ± 0.008. When split on velocity, the Foundation SNe Ia also do not show a significant difference in Hubble diagram residual, ΔHR = 0.015 ± 0.049 mag. Intriguingly, we find that SN Ia ejecta velocity information may be gleaned from photometry, particularly in redder optical bands. For high-redshift SNe Ia, these rest-frame red wavelengths will be observed by the Nancy Grace Roman Space Telescope. Our results are in line with previous work that suggests SN Ia host-galaxy stellar mass is correlated with ejecta velocity: high-velocity SNe Ia are found nearly exclusively in high-stellar-mass hosts. However, host-galaxy properties alone do not explain velocity-dependent differences in supernova colors and luminosities across samples. Measuring and understanding the connection between intrinsic explosion properties and supernova environments, across cosmic time, will be important for precision cosmology with SNe Ia.
Certified Defenses: Why Tighter Relaxations May Hurt Training?

Certiﬁed defenses based on convex relaxations are an established technique for training provably robust models. The key component is the choice of relaxation, varying from simple intervals to tight polyhedra. Paradoxically, however, training with tighter relaxations can often lead to worse certiﬁed robustness. The poor understanding of this paradox has forced recent state-of-the-art certiﬁed defenses to focus on designing various heuristics in order to mitigate its effects. In contrast, in this paper we study the underlying causes and show that tightness alone may not be the determining factor. Concretely, we identify two key properties of relaxations that impact training dynamics: continuity and sensitivity. Our extensive experimental evaluation demonstrates that these two factors, observed alongside tightness, explain the drop in certiﬁed robustness for popular relaxations. Further, we investigate the possibility of designing and training with relaxations that are tight, continuous and not sensitive. We believe the insights of this work can help drive the principled discovery of new and effective certiﬁed defense mechanisms.