cssi/SciSciGPT-SciSciNet huggingface dataset:
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions. However, adding content can often be useful when difficult concepts and reasoning need to be explained. In this work, we present the first data-driven study of content addition in text simplification, which we call elaborative simplification. We introduce a new annotated dataset of 1.3K instances of elaborative simplification in the Newsela corpus, and analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. We establish baselines for elaboration generation using large-scale pre-trained language models, and demonstrate that considering contextual specificity during generation can improve performance. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
Aeroelastic Stability of Bearingless Rotors in Forward Flight

The finite element method was used to determine the dynamic stability of bearingless rotor blades (BR) in forward flight. The analysis was applied to four different BR configurations and the results were correlated with experimental data. The analysis was correlated with hover lag mode stability data for a simple three-blade BR rotor tested in various pitch link configurations. In addition, a more advanced BR which includes precone, blade twist, blade sweep, and a lag shear restraint is analyzed and compared to experimental data in both hover and forward flight.
Influence of Exit Impedance on Finite Difference Solutions of Transient Acoustic Mode Propagation in Ducts

The time-dependent governing acoustic-difference equations and boundary conditions are developed and solved for sound propagation in an axisymmetric (cylindrical) hard-wall duct without flow and with spinning acoustic modes. The analysis begins with a harmonic sound source radiating into a quiescent duct. This explicit iteration method then calculates stepwise in real time to obtain the steady solutions of the acoustic field. The transient method did not converge to the steady-state solution for cutoff acoustic duct modes. This has implications as to its use in a variable-area duct, where modes may become cutoff in the smal-area portion of the duct. For single cutoff mode propagation the steady-state impedance boundary condition produced acoustic reflections during the initial transient that caused finite instabilities in the numerical calculations. The stability problem is resolved by reformulating the exit boundary condition. Example calculations show good agreement with exact analytical and numerical results for forcing frequencies above, below, and nearly at the cutoff frequency.
On the Classification of Normal Stein Spaces and Finite Ball Quotients With Bergman–Einstein Metrics

In this paper, we study the Bergman metric of a finite ball quotient $\mathbb{B}^n/\Gamma$, where $\Gamma \subseteq \mathrm{Aut}(\mathbb{B}^n)$ is a finite, fixed point free, abelian group. We prove that this metric is Kahler--Einstein if and only if $\Gamma$ is trivial, i.e., when the ball quotient $\mathbb{B}^n/\Gamma$ is the unit ball $\mathbb{B}^n$ itself. As a consequence, we establish a characterization of the unit ball among normal Stein spaces with isolated singularities and abelian fundamental groups in terms of the existence of a Bergman-Einstein metric.
Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.

We show dense voxel embeddings learned via deep metric learning can be employed to produce a highly accurate segmentation of neurons from 3D electron microscopy images. A “metric graph” on a set of edges between voxels is constructed from the dense voxel embeddings generated by a convolutional network. Partitioning the metric graph with long-range edges as repulsive constraints yields an initial segmentation with high precision, with substantial accuracy gain for very thin objects. The convolutional embedding net is reused without any modification to agglomerate the systematic splits caused by complex “self-contact” motifs. Our proposed method achieves state-of-the-art accuracy on the challenging problem of 3D neuron reconstruction from the brain images acquired by serial section electron microscopy. Our alternative, object-centered representation could be more generally useful for other computational tasks in automated neural circuit reconstruction.
Flight Mechanics Modeling and Post-Flight Analysis of ADEPT SR-1

Sounding Rocket One (SR-1), the first flight test of the Adaptable Deployable Entry and Placement Technology (ADEPT), was performed on Sept. 12, 2018. ADEPT is a deployable aeroshell that can be stowed during launch and then opened after launch to increase the drag area of the spacecraft when entering into a planetary atmosphere. The main objectives of the SR-1 flight test were to demonstrate that the ADEPT vehicle can be opened exo-atmospherically and to characterize the stability of the vehicle during atmospheric flight. The SR-1 test vehicle was a 0.7 m diameter 70 degree half-angle, faceted, sphere-cone, which was the primary payload on an UP Aerospace Spaceloft (SL) launch vehicle from the White Sands Missile Range (WSMR). ADEPT successfully separated from the spent booster in its stowed configuration, opened above 100 km altitude, and then landed in the deployed configuration within WSMR. The flight mechanics of the vehicle was modeled pre-flight for performance and range safety predictions. This paper describes the pre-flight ADEPT trajectory simulation and how the flight data compared with the predictions from the simulations.
Hidden-nodes in coexisting LAA & Wi-Fi: a measurement study of real deployments

LTE-Licensed Assisted Access (LAA) networks are beginning to be deployed widely in major metropolitan areas in the US in the unlicensed 5 GHz bands, which have existing dense deployments of Wi-Fi. This provides a real-world opportunity to study the problems due to hidden-node scenarios between LAA and Wi-Fi. The hidden node problem has been well studied in the context of overlapping Wi-Fi APs. However, when Wi-Fi coexists with LAA, the hidden node problem is exacerbated since LAA cannot use the well known Request-to-Send (RTS)/Clearto-Send (CTS) mechanism to resolve contentions, resulting in throughput degradation for Wi-Fi. In this paper we describe detailed measurements and conclusions from experiments on the campus of the University of Chicago which presents a perfect hidden node scenario where Wi-Fi access points (APs) controlled by us and a LAA base-station (BS) deployed by ATT (ii) clients close to the Wi-Fi AP; and (iii) clients close to the LAA BS. Our results show that in a situation where LAA uses an aggregate of three unlicensed channels (60 MHz bandwidth) which overlap with a 80 MHz Wi-Fi transmission, the Wi-Fi throughput at client devices suffers considerably. Overall, Wi-Fi performance is impacted by the hidden node problem more severely than LAA. In the best outdoor conditions, the throughput of LAA and Wi-Fi is reduced by 35% and 97% respectively when coexisting with each other as compared when the other system is not present. Furthermore, we conclude that when both LAA and Wi-Fi use multiple 20 MHz channels, and there are multiple Wi-Fi APs coexisting with LAA on the same set of channels, the choice of Wi-Fi primary channels can have a significant impact on LAA throughput.
The Littlewood-Offord Problem for Markov Chains

The celebrated Littlewood-Offord problem asks for an upper bound on the probability that the random variable e1v1+⋯+envn lies in the Euclidean unit ball, where e1,…,en∈{−1,1} are independent Rademacher random variables and v1,…,vn∈Rd are fixed vectors of at least unit length. We extend some known results to the case that the ei are obtained from a Markov chain, including the general bounds first shown by Erdős in the scalar case and Kleitman in the vector case, and also under the restriction that the vi are distinct integers due to Sarkozy and Szemeredi. In all extensions, the upper bound includes an extra factor depending on the spectral gap and additional dependency on the dimension. We also construct a pseudorandom generator for the Littlewood-Offord problem using similar techniques.
A CMOS Image Sensor and an AI Accelerator for Realizing Edge-Computing-Based Surveillance Camera Systems

This paper presents a CMOS image sensor and an AI accelerator to realize surveillance camera systems based on edge computing. For CMOS image sensors to be used for surveillance, it is desirable that they are highly sensitive even in low illuminance. We propose a new timing shift ADC used in CMOS image sensors for improving high sensitivity performance. Our proposed ADC improves non-linearity characteristics under low illuminance by 63%. Achieving power-efficient edge computing is a challenge for the systems to be used widely in the surveillance camera market. We demonstrate that our proposed AI accelerator performs inference processing for object recognition with 1 TOPS/W. Keywords: CMOS image sensor, surveillance camera system, low light imaging, AI accelerator, edge computing
How Do (Green) Innovators Respond to Climate Change Scenarios? Evidence from a Field Experiment

This paper aims to unpack the pro-social motivations of green innovators. In a field experiment inviting SBIR grantees to learn more about and apply to MIT Solve, we provide scientifically valid sc...
Communication-free Cohesive Flexible-Object Transport using Decentralized Robot Networks

Decentralized network theories focus on achieving consensus and in speeding up the rate of convergence to consensus. However, network cohesion (i.e., maintaining consensus) during transitions between consensus values is also important when transporting flexible structures. Deviations in the robot positions due to loss of cohesion when moving flexible structures from one position to another, such as uncured-composite aircraft wings, can cause large deformations, which in turn, can result in potential damage. The major contribution of this work is to develop a decentralized approach to transport flexible objects in a cohesive manner using local force measurements, without the need for additional communication between the robots. Additionally, stability conditions are developed for discrete-time implementation of the proposed cohesive-transition approach, and experimental results are presented, which show that the proposed cohesive transportation approach can reduce the relative deformations by 85% when compared to the case without it.
Graded persistence diagrams and persistence landscapes

We introduce a refinement of the persistence diagram, the graded persistence diagram. It is the Mobius inversion of the graded rank function, which is obtained from the rank function using the unary numeral system. Both persistence diagrams and graded persistence diagrams are integer-valued functions on the Cartesian plane. Whereas the persistence diagram takes non-negative values, the graded persistence diagram takes values of 0, 1, or $$-1$$ . The sum of the graded persistence diagrams is the persistence diagram. We show that the positive and negative points in the kth graded persistence diagram correspond to the local maxima and minima, respectively, of the kth persistence landscape. We prove a stability theorem for graded persistence diagrams: the 1-Wasserstein distance between kth graded persistence diagrams is bounded by twice the 1-Wasserstein distance between the corresponding persistence diagrams, and this bound is attained. In the other direction, the 1-Wasserstein distance is a lower bound for the sum of the 1-Wasserstein distances between the kth graded persistence diagrams. In fact, the 1-Wasserstein distance for graded persistence diagrams is more discriminative than the 1-Wasserstein distance for the corresponding persistence diagrams.
Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

Motivation To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. Results To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e., 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Supplementary information Supplementary data are available at Bioinformatics online.
Agreement between state registry, health record, and self-report of influenza vaccination.

Abstract Background Documentation of influenza vaccination, including the specific product received, is critical to estimate annual vaccine effectiveness (VE). Methods We assessed performance of the Michigan Care Improvement Registry (MCIR) in defining influenza vaccination status relative to documentation by provider records or self-report among subjects enrolled in a study of influenza VE from 2011 through 2019. Results The specificity and positive predictive value of MCIR were high; however, >10% of vaccinations were identified only by other sources each season. The proportion of records captured by MCIR increased from a low of 67% in 2013–2014 to a high of 89% in 2018–2019, largely driven by increased capture of vaccination among adults. Conclusions State vaccine registries, such as MCIR, are important tools for documenting influenza vaccination, including the specific product received. However, incomplete capture suggests that documentation from other sources and self-report should be used in combination with registries to reduce misclassification.
Leaky buddies: cross-component covert channels on integrated CPU-GPU systems

Graphics Processing Units (GPUs) are ubiquitous components used across the range of today’s computing platforms, from phones and tablets, through personal computers, to high-end server class platforms. With the increasing importance of graphics and video workloads, recent processors are shipped with GPU devices that are integrated on the same chip. Integrated GPUs share some resources with the CPU and as a result, there is a potential for microarchitectural attacks from the GPU to the CPU or vice versa. We consider the potential for covert channel attacks that arise either from shared microarchitectural components (such as caches) or through shared contention domains (e.g., shared buses). We illustrate these two types of channels by developing two reliable covert channel attacks. The first covert channel uses the shared LLC cache in Intel’s integrated GPU architectures. The second is a contention based channel targeting the ring bus connecting the CPU and GPU to the LLC. This is the first demonstrated microarchitectural attack crossing the component boundary (GPU to CPU or vice versa). Cross-component channels introduce a number of new challenges that we had to overcome since they occur across heterogeneous components that use different computation models and are interconnected using asymmetric memory hierarchies. We also exploit GPU parallelism to increase the bandwidth of the communication, even without relying on a common clock. The LLC based channel achieves a bandwidth of 120 kbps with a low error rate of 2%, while the contention based channel delivers up to 400 kbps with a 0.8% error rate. We also demonstrate a proof-of-concept prime-and-probe side channel attack that probes the full LLC from the GPU.
Closed subsets of a CAT(0) 2–complex are intrinsically CAT(0)

Let k be at most 0, and let X be a locally-finite CAT(k) polyhedral 2-complex X, each face with constant curvature k. Let E be a closed, rectifiably-connected subset of X with trivial first singular homology. We show that E, under the induced path metric, is a complete CAT(k) space.
Stable and accurate numerical methods for generalized Kirchhoff–Love plates

Kirchhoff–Love plate theory is widely used in structural engineering. In this paper, efficient and accurate numerical algorithms are developed to solve a generalized Kirchhoff–Love plate model subject to three common physical boundary conditions: (i) clamped; (ii) simply supported; and (iii) free. The generalization stems from the inclusion of additional physics to the classical Kirchhoff–Love model that accounts for bending only. We solve the model equation by discretizing the spatial derivatives using second-order finite-difference schemes, and then advancing the semi-discrete problem in time with either an explicit predictor–corrector or an implicit Newmark-Beta time-stepping algorithm. Stability analysis is conducted for the schemes, and the results are used to determine stable time steps in practice. A series of carefully chosen test problems are solved to demonstrate the properties and applications of our numerical approaches. The numerical results confirm the stability and 2nd-order accuracy of the algorithms and are also comparable with experiments for similar thin plates. As an application, we illustrate a strategy to identify the natural frequencies of a plate using our numerical methods in conjunction with a fast Fourier transformation power spectrum analysis of the computed data. Then we take advantage of one of the computed natural frequencies to simulate the interesting physical phenomena known as resonance and beat for a generalized Kirchhoff–Love plate.
Factors controlling oxophilicity and carbophilicity of transition metals and main group metals

The strength of interaction between a metal and oxygen and/or carbon is a crucial factor for catalytic performance, materials stability, and other important applications. While these are fundamental properties in materials science, there is no general understanding of what makes a metal oxophilic or carbophilic, especially for main group metals. In this work, we elucidate the factors that control how oxophilic or carbophilic a metal is by creating a predictive model and applying it to a variety of data sets for transition metals and main group metals, including DFT-calculated adsorption energies and experimental formation energies. Our model is easily interpretable and accurately describes oxophilic and carbophilic trends across different regions of the periodic table. This model captures the ionic contribution to bonding, the adsorbate-sp contribution to bonding, and the adsorbate-d contribution to bonding by using the reduction potential, matrix coupling elements, band centers, and band filling. For transition metals, the adsorbate–surface d coupling is the major factor that determines oxophilicity relative to carbophilicity. For metals that do not contain d electrons either in their core or valence shell (Li, Be, Na, Mg, Al, K, and Ca), the reduction potential and the adsorbate–surface s coupling are the major factors. As a simple application, we demonstrate the utility of oxophilicity and carbophilicity in rapidly screening metal dopants for improved selectivity for ethylene epoxidation on silver-based catalysts. Using our model, we establish a direct relationship between the electronic properties of the metal dopants and their calculated selectivity for ethylene epoxidation. The results suggest that transition metals with high adsorbate–surface d coupling and s block metals with low adsorbate–surface s coupling are good silver-dopant candidates for this reaction. Overall, the improved linkage between a metal's electronic structure and its interaction with carbon or oxygen will be broadly useful in design of functional materials for a variety of applications.
Skein theoretic approach to Yang-Baxter homology

Abstract We introduce skein theoretic techniques to compute the Yang-Baxter (YB) homology and cohomology groups of the R-matrix corresponding to the Jones polynomial. More specifically, we show that the YB operator R for Jones, normalized for homology, admits a skein decomposition R = I + β α , where α : V ⊗ 2 → k is a “cup” pairing map and β : k → V ⊗ 2 is a “cap” copairing map, and differentials in the chain complex associated to R can be decomposed into horizontal tensor concatenations of cups and caps. We apply our skein theoretic approach to determine the second and third YB homology groups, confirming a conjecture of Przytycki and Wang. Further, we compute the cohomology groups of R, and provide computations in higher dimensions that yield some annihilations of submodules.
How to Understand Common Patterns in Big Data: The Case of Human Collective Memory

Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of nature. Those observed regularities set the challenge of understanding the mechanistic processes that generate common patterns. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data will require better methods for developing alternative, empirically testable causal models.
Analysis of nuclear induced plasmas

A kinetic model is developed for a plasma generated by fission fragments, and the results are employed to study He plasma generated in a tube coated with fissionable material. Because both the heavy particles and electrons play important roles in creating the plasma, their effects are considered simultaneously. The calculations are carried out for a range of neutron fluxes and pressures. In general, the predictions of the theory are in good agreement with available intensity measurements. Moreover, the theory predicts the experimentally measured inversions. However, the calculated gain coefficients are such that lasing is not expected to take place in a helium plasma generated by fission fragments.
Parallelization of Plane Sweep Based Voronoi Construction with Compiler Directives

Voronoi diagram construction is a common and fundamental problem in computational geometry and spatial computing. Numerous sequential and parallel algorithms for Voronoi diagram construction exists in literature. This paper presents a multi-threaded approach where we augment an existing sequential implementation of Fortune's planesweep algorithm with compiler directives. The novelty of our fine-grained parallel algorithm lies in exploiting the concurrency available at each event point encountered during the algorithm. On the Intel Xeon E5 CPU, our shared-memory parallelization with OpenMP achieves around 2x speedup compared to the sequential implementation using datasets containing 2k-128k sites.
Dep-\(L_0\): Improving \(L_0\)-Based Network Sparsification via Dependency Modeling

Training deep neural networks with an \(L_0\) regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training by encouraging weights to become exactly zero. However, recent work of Gale et al. [11] reveals that although this method yields high compression rates on smaller datasets, it performs inconsistently on large-scale learning tasks, such as ResNet50 on ImageNet. We analyze this phenomenon through the lens of variational inference and find that it is likely due to the independent modeling of binary gates, the mean-field approximation [2], which is known in Bayesian statistics for its poor performance due to the crude approximation. To mitigate this deficiency, we propose a dependency modeling of binary gates, which can be modeled effectively as a multi-layer perceptron (MLP). We term our algorithm Dep-\(L_0\) as it prunes networks via a dependency-enabled \(L_0\) regularization. Extensive experiments on CIFAR10, CIFAR100 and ImageNet with VGG16, ResNet50, ResNet56 show that our Dep-\(L_0\) outperforms the original \(L_0\)-HC algorithm of Louizos et al. [32] by a significant margin, especially on ImageNet. Compared with the state-of-the-arts network sparsification algorithms, our dependency modeling makes the \(L_0\)-based sparsification once again very competitive on large-scale learning tasks. Our source code is available at https://github.com/leo-yangli/dep-l0.
Projecting the likely importance of weak-interaction-driven bulk viscosity in neutron star mergers

In this work, we estimate how much bulk viscosity driven by Urca processes is likely to affect the gravitational wave signal of a neutron star coalescence. In the late inspiral, we show that bulk viscosity affects the binding energy at fourth post-Newtonian (PN) order. Even though this effect is enhanced by the square of the gravitational compactness, the coefficient of bulk viscosity is likely too small to lead to observable effects in the waveform during the late inspiral, when only considering the orbital motion itself. In the post-merger, however, the characteristic time-scales and spatial scales are different, potentially leading to the opposite conclusion. We post-process data from a state-of-the-art equal-mass binary neutron star merger simulation to estimate the effects of bulk viscosity (which was not included in the simulation itself). In that scenario, we find that bulk viscosity can reach high values in regions of the merger. We compute several estimates of how much it might directly affect the global dynamics of the considered merger scenario, and find that it could become significant. Even larger effects could arise in different merger scenarios or in simulations that include non-linear effects. This assessment is reinforced by a quantitative comparison with relativistic heavy-ion collisions where such effects have been explored extensively.
End-to-end Ultrasound Frame to Volume Registration

Fusing intra-operative 2D transrectal ultrasound (TRUS) image with pre-operative 3D magnetic resonance (MR) volume to guide prostate biopsy can significantly increase the yield. However, such a multimodal 2D/3D registration problem is very challenging due to several significant obstacles such as dimensional mismatch, large modal appearance difference, and heavy computational load. In this paper, we propose an end-to-end frame-to-volume registration network (FVR-Net), which can efficiently bridge the previous research gaps by aligning a 2D TRUS frame with a 3D TRUS volume without requiring hardware tracking. The proposed FVR-Net utilizes a dual-branch feature extraction module to extract the information from TRUS frame and volume to estimate transformation parameters. To achieve efficient training and inference, we introduce a differentiable 2D slice sampling module which allows gradients backpropagating from an unsupervised image similarity loss for content correspondence learning. Our experiments demonstrate the proposed method’s superior efficiency for real-time interventional guidance with highly competitive registration accuracy. Source code of this work is publicly available at https://github.com/DIAL-RPI/FVR-Net.
Quantum-accelerated constraint programming

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the $\texttt{alldifferent}$ global constraint and discuss its applicability to a broader family of global constraints with similar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault-tolerant quantum computers and beyond.
Updating the theory of buffer sizing

Abstract Routers have packet buffers to reduce packet drops during times of congestion. It is important to correctly size the buffer: make it too small, and packets are dropped unnecessarily and the link may be underutilized; make it too big, and packets may wait for a long time, and the router itself may be more expensive to build. Despite its importance, there are few guidelines for picking the buffer size. The two most well-known rules only apply to long-lived TCP Reno flows; either for a network carrying a single TCP Reno flow (the buffer size should equal the bandwidth-delay product, or BDP ) or for a network carrying n TCP Reno flows (the buffer size should equal BDP / n ). Since these rules were introduced, TCP Reno has been replaced by newer algorithms as the default congestion control algorithm in all major operating systems, yet little has been written about how the rules need to change. This paper revisits both rules. For the single flow case, we generalize the BDP rule to account for changes to TCP, such as Proportional Rate Reduction (PRR), and the introduction of new algorithms including Cubic and BBR. We find that buffers can be made 60%–75% smaller for newer algorithms. For the multiple flow case, we show that the square root of n rule holds under a broader set of assumptions than previously known, including for these new congestion control algorithms. We also demonstrate situations where the square root of n rule does not hold, including for unfair flows and certain settings with ECN. We validate our results by precisely measuring the time series of buffer occupancy in a real network, and comparing it to the per-packet window size.
SMART: A Heterogeneous Scratchpad Memory Architecture for Superconductor SFQ-based Systolic CNN Accelerators

Ultra-fast & low-power superconductor single-flux-quantum (SFQ)-based CNN systolic accelerators are built to enhance the CNN inference throughput. However, shift-register (SHIFT)-based scratchpad memory (SPM) arrays prevent a SFQ CNN accelerator from exceeding 40% of its peak throughput, due to the lack of random access capability. This paper first documents our study of a variety of cryogenic memory technologies, including Vortex Transition Memory (VTM), Josephson-CMOS SRAM, MRAM, and Superconducting Nanowire Memory, during which we found that none of the aforementioned technologies made a SFQ CNN accelerator achieve high throughput, small area, and low power simultaneously. Second, we present a heterogeneous SPM architecture, SMART, composed of SHIFT arrays and a random access array to improve the inference throughput of a SFQ CNN systolic accelerator. Third, we propose a fast, low-power and dense pipelined random access CMOS-SFQ array by building SFQ passive-transmission-line-based H-Trees that connect CMOS sub-banks. Finally, we create an ILP-based compiler to deploy CNN models on SMART. Experimental results show that, with the same chip area overhead, compared to the latest SHIFT-based SFQ CNN accelerator, SMART improves the inference throughput by 3.9 × (2.2 ×), and reduces the inference energy by 86% (71%) when inferring a single image (a batch of images).
A Formal Characterization of Black-Box System Safety Performance With Scenario Sampling

A typical scenario-based evaluation framework seeks to characterize a black-box system's safety performance (e.g., failure rate) through repeatedly sampling initialization configurations (scenario sampling) and executing a certain test policy for scenario propagation (scenario testing) with the black-box system involved as the test subject. In this letter, we first present a novel safety evaluation criterion that seeks to characterize the actual operational domain within which the test subject would remain safe indefinitely with high probability. By formulating the black-box testing scenario as a dynamic system, we show that the presented problem is equivalent to finding a certain almost robustly forward invariant set for the given system. Second, for an arbitrary scenario testing strategy, we propose a scenario sampling algorithm that is provably asymptotically optimal in obtaining the safe invariant set with arbitrarily high accuracy. Moreover, as one considers different testing strategies (e.g., biased sampling of safety-critical cases), we show that the proposed algorithm still converges to the unbiased approximation of the safety characterization outcome if the scenario testing satisfies a certain condition. Finally, the effectiveness of the presented scenario sampling algorithms and various theoretical properties are demonstrated in a case study of the safety evaluation of a control barrier function-based mobile robot collision avoidance system.
Dynamic Modeling of Bucket-Soil Interactions Using Koopman-DFL Lifting Linearization for Model Predictive Contouring Control of Autonomous Excavators

A lifting-linearization method based on the Koopman operator and Dual Faceted Linearization is applied to the control of a robotic excavator. In excavation, a bucket interacts with the surrounding soil in a highly nonlinear and complex manner. Here, we propose to represent the nonlinear bucket-soil dynamics with a set of linear state equations in a higher-dimensional space. The space of independent state variables is augmented by adding variables associated with nonlinear elements involved in the bucket-soil dynamics. These include nonlinear resistive forces and moment acting on the bucket from the soil, and the effective inertia of the bucket that varies as the soil is captured into the bucket. Variables associated with these nonlinear resistive and inertia elements are treated as additional state variables, and their time evolution is represented as another set of linear differential equations. The lifted linear dynamic model is then applied to Model Predictive Contouring Control, where a cost functional is minimized as a convex optimization problem thanks to the linear dynamics in the lifted space. The lifted linear model is tuned based on a data-driven method by using a soil dynamics simulator. Simulation experiments on homogeneous soil verify the effectiveness of the proposed lifting linearization compared to its counterpart.
Commentary on the Radius of the Sun: Optical Illusion or Manifestation of a Real Surface?

But however diffi cult it may be for present theories to account for the tenuity of the solar atmosphere immediately above the photosphere, and however readily the same fact may be accounted for by the theory of Schmidt, it is certain that the observer who has studied the structure of the Sun’s surface, and particularly the aspect of the spots and other markings as they approach the limb, must feel convinced that these forms actually occur at practically the same level, that is, that the photosphere is an actual and not an optical surface. Hence it is, no doubt, that the theory is apt to be more favorably regarded by mathematicians than by observers.
GPU-Enabled Searches for Periodic Signals of Unknown Shape

Abstract Recent and future generation observatories will enable the study of variable astronomical phenomena through their time-domain capabilities. High temporal fidelity will allow for unprecedented investigations into the nature of variable objects — those objects that vary in brightness over time. A major bottleneck in data processing pipelines is constructing light curve solutions for catalogs of variable objects, as it is well-known that period finding algorithms are computationally expensive. Furthermore, there are many period finding algorithms that are often suited for specific science cases. In this paper, we present the first GPU-accelerated Super Smoother algorithm. Super Smoother is general purpose and uses cross-validation to fit line segments to a time series, and as such, is more computationally expensive than other algorithms, such as Lomb–Scargle. Because the algorithm requires making several scans over the input time series for a tested frequency, we also propose a novel generalized-validation variant of Super Smoother that only requires a single scan over the data. We compare the performance of our algorithms to analogous parallel multi-core CPU implementations on three catalogs of data, and show that it is generally advantageous to use the GPU algorithm over the CPU counterparts. Furthermore, we demonstrate that our single-pass variant of Super Smoother is roughly equally as accurate at finding correct period solutions as the original algorithm. Our software supports several features, such as batching the computation to eliminate the possibility of exceeding global memory on the GPU, processing a single object or batches of objects, and we allow for scaling the algorithm across multiple GPUs.
Physics-based Shadow Image Decomposition for Shadow Removal.

We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of mean absolute error (MAE) for the shadow area by 20\%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow- free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods.
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding

Humans are remarkably flexible when understanding new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language abilities when presented with novel sentences, systematic variation uncovers the limitations in the language-understanding abilities of networks. We demonstrate that these limitations can be overcome by addressing the generalization challenges in the gSCAN dataset, which explicitly measures how well an agent is able to interpret novel linguistic commands grounded in vision, e.g., novel pairings of adjectives and nouns. The key principle we employ is compositionality: that the compositional structure of networks should reflect the compositional structure of the problem domain they address, while allowing other parameters to be learned end-to-end. We build a general-purpose mechanism that enables agents to generalize their language understanding to compositional domains. Crucially, our network has the same state-of-the-art performance as prior work while generalizing its knowledge when prior work does not. Our network also provides a level of interpretability that enables users to inspect what each part of networks learns. Robust grounded language understanding without dramatic failures and without corner cases is critical to building safe and fair robots; we demonstrate the significant role that compositionality can play in achieving that goal.
Clock transitions guard against spin decoherence in singlet fission

Short coherence times present a primary obstacle in quantum computing and sensing applications. In atomic systems, clock transitions (CTs), formed from avoided crossings in an applied Zeeman field, can substantially increase coherence times. We show how CTs can dampen intrinsic and extrinsic sources of quantum noise in molecules. Conical intersections between two periodic potentials form CTs in electron paramagnetic resonance experiments of the spin-polarized singlet fission photoproduct. We report on a pair of CTs for a two-chromophore molecule in terms of the Zeeman field strength, molecular orientation relative to the field, and molecular geometry.
On Dispersability of Some Products of Cycles

We show that the matching book thickness of the Cartesian product of two odd-length cycle-graphs is five if at least one of the cycles has length 3 or 5.
The Cholinergic Interneurons of the Striatum: Intrinsic Properties Underlie Multiple Discharge Patterns

Publisher Summary This chapter reviews that cholinergic interneurons exhibit multiple discharge patterns including two forms of rhythmic discharge – single spiking and bursting – as well as irregular firing. The mechanism that prevents the existence of a stable resting potential in these cells, and thus guarantees that they will always discharge, relies on the presence of robust persistent sodium current that repeatedly brings the cells to action potential threshold. Thus, even in the absence of synaptic input the cholinergic interneurons fire autonomously. A host of high-voltage activated calcium currents, expressed by the cells, which are selectively coupled to specific calcium-activated potassium currents, participates in selecting among the various firing patterns. While the basic mechanisms of discharge are intrinsic to the cholinergic interneurons, synaptic input can exert an important influence on the discharge of these neurons. Weak ionotropic inputs, resulting from glutamatergic inputs from the intralaminar nuclei or from the cortex, or from gamma-aminobutyric-acid (GABAergic) inputs from striatal neurons, can nudge the timing of individual action potentials. The extended and dense axonal arborization of the network of cholinergic interneurons, combined with their shared pause responses in primates, led to the proposal that these neurons act in concert to modulate activity of the whole striatal network. Tonically active neuronsTANs have been shown to discharge and pause synchronously. Future work needs to determine to what extent this synchrony is controlled by the firing pattern of the cholinergic interneurons.
Cost-effectiveness of prostate cancer chemoprevention: a quality of life-years analysis.

BACKGROUND The Prostate Cancer Prevention Trial (PCPT) demonstrated that finasteride reduces the prevalence of prostate cancer by 24.8% (risk reduction) but questions remain regarding the cost-effectiveness of widespread utilization. The purpose of the current analysis was to evaluate the cost-effectiveness of chemoprevention utilizing a quality-of-life adjustment. METHODS A Markov decision analysis model with probabilistic sensitivity analysis was designed to determine the lifetime prostate health-related costs, beginning at age 50 years, for men treated with finasteride compared with placebo. Model assumptions were based on data from the PCPT; Surveillance, Epidemiology, and End-Results program; literature review of costs, utilities, and transition rates among various prostate cancer health states; and local institutional cost data. RESULTS The quality-adjusted cost-effectiveness ratio for finasteride compared with men not receiving chemoprevention was $122,747 (in U.S.$) per quality-adjusted life-years saved (QALYs). If finasteride is assumed to not increase the incidence of high-grade tumors, the quality-adjusted cost-effectiveness ratio was $112,062 per QALYs. Sensitivity analysis found that chemoprevention of prostate cancer with an agent that has no effect on the prevalence of benign prostatic hyperplasia can render a cost-effectiveness ratio of <$50,000 per QALYs saved when applied to a high-risk population associated with a 25% risk reduction, and a cost of $30 per month. CONCLUSIONS Finasteride is unlikely to be cost-effective when considering the impact on survival differences among treated versus untreated groups. However, chemoprevention may be cost-effective in high-risk populations when taking into consideration adjustments for the impact on quality of life. Cancer 2008. © 2008 American Cancer Society.
Activation of the hypothalamic arcuate nucleus predicts the anorectic actions of ciliary neurotrophic factor and leptin in intact and gold thioglucose-lesioned mice.

Similar to leptin, ciliary neurotrophic factor (CNTF) suppresses appetite and selectively reduces body fat in leptin-deficient ob/ob mice. To assess the relative importance of specific regions of the hypothalamus in mediating these effects, we administered a CNTF analogue (CNTF Ax15 ) or leptin to mice made obese by administration of gold thioglucose (GTG), which destroys a well-defined portion of the medial basal hypothalamus. CNTF Ax15 treatment reduced appetite and body weight in obese GTG-lesioned C57BU6 mice, whereas leptin failed to effect similar changes regardless of whether treatment was initiated before or after the lesioned mice had become obese. Because leptin does not reduce food intake or body weight in most forms of obesity (a condition termed 'leptin resistance'), we also investigated the actions of leptin in GTG-lesioned leptin-deficient (ob/ob) mice. By contrast to C57BU6 mice, leptin treatment reduced food intake and body weight in GTG-lesioned ob/ob mice, although the effect was attenuated. To further compare the neural substrates mediating the anorectic actions of leptin and CNTF, we determined the patterns of neurone activation induced by these proteins in the hypothalamus of intact and GTG-lesioned mice by staining for phosphorylated signal transducer and activator of transcription 3 (pSTAT3). CNTF Ax15 stimulated robust pSTAT3 signalling in neurones of the medial arcuate nucleus in both intact and lesioned C57BU6 and ob/ob mice. Leptin administration stimulated pSTAT3 signalling in only a few neurones of the medial arcuate nucleus in intact or lesioned C57BU6 mice, but elicited a robust response in intact or lesioned ob/ob mice. By contrast to CNTF Ax15 , leptin treatment also resulted in prominent activation of STAT3 in several areas of the hypothalamus outside the medial arcuate nucleus. This leptin-induced pSTAT3 signal was at least as prominent in intact and GTG-lesioned C57BU6 mice as it was in ob/ob mice, and thus was not correlated with appetite suppression or weight loss. These results indicate that the medial arcuate nucleus is a key mediator of appetite suppression and weight loss produced by CNTF and leptin, whereas GTG-vulnerable regions play a role only in leptin-induced weight loss. Other regions of hypothalamus in which pSTAT3 signal is induced by leptin may regulate energy metabolism through mechanisms other than appetite reduction.
Diffusions with positive unbounded controls

We give a preliminary sketch for a theory of impulsive stochastic controls based on a time substitution. >