Miracle-dz/UniFAR-FTUs huggingface dataset:
Model hierarchy for the shape optimization of a microchannel cooling system

We model a microchannel cooling system and consider the optimization of its shape by means of shape calculus. A three‐dimensional model covering all relevant physical effects and three reduced models are introduced. The latter are derived via a homogenization of the geometry in 3D and a transformation of the three‐dimensional models to two dimensions. A shape optimization problem based on the tracking of heat absorption by the cooler and the uniform distribution of the flow through the microchannels is formulated and adapted to all models. We present the corresponding shape derivatives and adjoint systems, which we derived with a material derivative free adjoint approach. To demonstrate the feasibility of the reduced models, the optimization problems are solved numerically with a gradient descent method. A comparison of the results shows that the reduced models perform similarly to the original one while using significantly less computational resources.
A fully stochastic second-order trust region method

ABSTRACT A stochastic second-order trust region method is proposed, which can be viewed as an extension of the trust-region-ish (TRish) algorithm proposed by Curtis et al. [A stochastic trust region algorithm based on careful step normalization. INFORMS J. Optim. 1(3) 200–220, 2019]. In each iteration, a search direction is computed by (approximately) solving a subproblem defined by stochastic gradient and Hessian estimates. The algorithm has convergence guarantees in the fully stochastic regime, i.e. when each stochastic gradient is merely an unbiased estimate of the gradient with bounded variance and the stochastic Hessian estimates are bounded. This framework covers a variety of implementations, such as when the stochastic Hessians are defined by sampled second-order derivatives or diagonal matrices, such as in RMSprop, Adagrad, Adam and other popular algorithms. The proposed algorithm has a worst-case complexity guarantee in the nearly deterministic regime, i.e. when the stochastic gradients and Hessians are close in expectation to the true gradients and Hessians. The results of numerical experiments for training CNNs for image classification and an RNN for time series forecasting are presented. These results show that the algorithm can outperform a stochastic gradient and first-order TRish algorithm.
Spatial resolution of local field potential signals in macaque V4

Objective. An important challenge for the development of cortical visual prostheses is to generate spatially localized percepts of light, using artificial stimulation. Such percepts are called phosphenes, and the goal of prosthetic applications is to generate a pattern of phosphenes that matches the structure of the retinal image. A preliminary step in this process is to understand how the spatial positions of phosphene-like visual stimuli are encoded in the distributed activity of cortical neurons. The spatial resolution with which the distributed responses discriminate positions puts a limit on the capability of visual prosthesis devices to induce phosphenes at multiple positions. While most previous prosthetic devices have targeted the primary visual cortex, the extrastriate cortex has the advantage of covering a large part of the visual field with a smaller amount of cortical tissue, providing the possibility of a more compact implant. Here, we studied how well ensembles of Local Field Potentials (LFPs) and Multiunit activity (MUA) responses from extrastriate cortical visual area V4 of a behaving macaque monkey can discriminate between two-dimensional spatial positions. Approach. We used support vector machines (SVM) to determine the capabilities of LFPs and MUA to discriminate responses to phosphene-like stimuli (probes) at different spatial separations. We proposed a selection strategy based on the combined responses of multiple electrodes and used the linear learning weights to find the minimum number of electrodes for fine and coarse discriminations. We also measured the contribution of correlated trial-to-trial variability in the responses to the discrimination performance for MUA and LFP. Main results. We found that despite the large receptive field sizes in V4, the combined responses from multiple sites, whether MUA or LFP, are capable of fine and coarse discrimination of positions. Our electrode selection procedure significantly increased discrimination performance while reducing the required number of electrodes. Analysis of noise correlations in MUA and LFP responses showed that noise correlations in LFPs carry more information about spatial positions. Significance. This study determined the coding strategy for fine discrimination, suggesting that spatial positions could be well localized with patterned stimulation in extrastriate area V4. It also provides a novel approach to build a compact prosthesis with relatively few electrodes, which has the potential advantage of reducing tissue damage in real applications.
Two New Rapidly Rotating ON Stars Found with LAMOST

The ON stars are a rare subtype of O stars of uncertain origin. We report two new, rapidly rotating ON stars found in data acquired with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. LS I +61 28 is an ON8.5 Vn dwarf with a projected equatorial rotational velocity of , while HDE 236672 is an ON9 IVn subgiant with . The former is the first rapidly rotating ON dwarf to be found, and the latter is only the third ON subgiant. The luminosity classes of non-supergiant ON stars appear to be influenced by the axial inclination angle i: the rapidly rotating giants are close to equator-on, while ON dwarfs with lower values are viewed more nearly pole-on. Combining parallaxes and proper motions from Gaia Data Release 2 with radial-velocity measurements, we investigate the kinematics of non-supergiant ON stars and infer that the dynamics, rapid rotation, and surface nitrogen characteristics may all be consequences of binary interaction.
Benchmarking the Performance and Power of AI Accelerators for AI Training

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a huge amount of calculations, taking a long running time and consuming a lot of energy. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) are designed to improve the AI training performance. However, different processors from different vendors perform very differently in terms of performance and power consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU and Google TPU) in training DNNs, we carry out a detailed benchmark study on the performance and power (when possible) of these processors when training a representative set of DNNs, including three classical convolutional neural networks (CNNs), a recurrent neural network (LSTM), Deep Speech 2, and Transformer. We try to understand the impact of hardware, vendor's software library, and deep learning framework on the final training performance. Our evaluation results make two valuable directions for end-users and vendors. For the end-users, the evaluation results provide a guide for selecting a proper accelerator for training DNN models. For the vendors, some advantages and disadvantages revealed in our evaluation results could be useful for future architecture design and software library optimization.
Aging Deep Face Features: Finding Missing Children

Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose an age-progression module that can age-progress deep face features output by any commodity face matcher. For time lapses larger than 10 years (the missing child is found after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of FaceNet from 40% to 49.56% and CosFace from 56.88% to 61.25% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate from 94.91% to 95.91% on a public aging dataset, FG-NET, and from 99.50% to 99.58% on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.
Working Memory Graphs

Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the question of how Transformer-based models can improve the performance of sequential decision-making agents. We present the Working Memory Graph (WMG), an agent that employs multi-head self-attention to reason over a dynamic set of vectors representing observed and recurrent state. We evaluate WMG in three environments featuring factored observation spaces: a Pathfinding environment that requires complex reasoning over past observations, BabyAI gridworld levels that involve variable goals, and Sokoban which emphasizes future planning. We find that the combination of WMG's Transformer-based architecture with factored observation spaces leads to significant gains in learning efficiency compared to baseline architectures across all tasks. WMG demonstrates how Transformer-based models can dramatically boost sample efficiency in RL environments for which observations can be factored.
Assigning Medical Codes at the Encounter Level by Paying Attention to Documents

The vast majority of research in computer assisted medical coding focuses on coding at the document level, but a substantial proportion of medical coding in the real world involves coding at the level of clinical encounters, each of which is typically represented by a potentially large set of documents. We introduce encounter-level document attention networks, which use hierarchical attention to explicitly take the hierarchical structure of encounter documentation into account. Experimental evaluation demonstrates improvements in coding accuracy as well as facilitation of human reviewers in their ability to identify which documents within an encounter play a role in determining the encounter level codes.
Re-Pair in Small Space

Re-Pair is a grammar compression scheme with favorably good compression rates. The computation of Re-Pair comes with the cost of maintaining large frequency tables, which makes it hard to compute Re-Pair on large scale data sets. As a solution for this problem we present, given a text of length n whose characters are drawn from an integer alphabet, an O(n^2) time algorithm computing Re-Pair in n lg max(n, τ) bits of working space including the text space, where τ is the number of terminals and non-terminals.
NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units

To satisfy the compute and memory demands of deep neural networks (DNNs), neural processing units (NPUs) are widely being utilized for accelerating DNNs. Similar to how GPUs have evolved from a slave device into a mainstream processor architecture, it is likely that NPUs will become first-class citizens in this fast-evolving heterogeneous architecture space. This paper makes a case for enabling address translation in NPUs to decouple the virtual and physical memory address space. Through a careful data-driven application characterization study, we root-cause several limitations of prior GPU-centric address translation schemes and propose a memory management unit (MMU) that is tailored for NPUs. Compared to an oracular MMU design point, our proposal incurs only an average 0.06% performance overhead.
Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing

Deep learning-based image processing is capable of creating highly appealing results. However, it is still widely considered as a “blackbox” transformation. In medical imaging, this lack of comprehensibility of the results is a sensitive issue. The integration of known operators into the deep learning environment has proven to be advantageous for the comprehensibility and reliability of the computations. Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing. The output images are only processed by the guided filter while the guidance map can be trained to be task-optimal in an end-to-end fashion. We investigate the performance based on two popular tasks: image super resolution and denoising. The evaluation is conducted based on pairs of multi-modal magnetic resonance imaging and cross-modal computed tomography and magnetic resonance imaging datasets. For both tasks, the proposed approach is on par with state-of-the-art approaches. Additionally, we can show that the input image’s content is almost unchanged after the processing which is not the case for conventional deep learning approaches. On top, the proposed pipeline offers increased robustness against degraded input as well as adversarial attacks.
THE RESEARCH INSTITUTE OF INDUSTRIAL ECONOMICS

This study examines the relation between the internationalization of firms and CEO compensation. Starting from a sample of Norwegian and Swedish listed firms we analyze the effects of internationalization as manifest in the capital market (international cross-listing), the market for corporate control (foreign board membership), and the product and service market (export and foreign sales). We conclude that all three markets contribute positively to the compensation level of CEOs. We argue that part of the higher CEO compensation in internationally oriented firms as compared to less internationalized firms within the same country reflects a risk premium for reduction in job security.
ASSOCIATION FOR CONSUMER RESEARCH

This exploratory study, based on a research with consumers of constant technologically updated products of personal use, aims to explore the relationships between innovativeness and perceived characteristics of the innovation, during the consumer’s decision process. The contribution of such analysis resides in conciliating the broad approach of the decision process, brought by the innovation theory, increasing the knowledge about the adopter’s profile and the way they make their decisions related to innovative products. The study attempts to comprehend the elements involved in a decision making process of innovations and how different adoption behavior leads consumers to use different strategies to achieve their goals during the choice process.
Journal of Solid Mechanics Vol

In this article, classical plate theory (CPT) is reformulated using the nonlocal differential constitutive relations of Eringen to develop an equivalent continuum model for orthotropic triangular nanoplates. The equations of motion are derived and the Galerkin’s approach in conjunction with the area coordinates is used as a basis for the solution. Nonlocal theories are employed to bring out the effect of the small scale on natural frequencies of nano scaled plates. Effect of nonlocal parameter, lengths of the nanoplate, aspect ratio, mode number, material properties, boundary condition and in-plane loads on the natural frequencies are investigated. It is shown that the natural frequencies depend highly on the non-locality of the nanoplate, especially at the very small dimensions, higher mode numbers and stiffer edge condition. © 2016 IAU, Arak Branch.All rights reserved.
Interactive comment on “Factors determining the effect of aerosols on cloud mass and the dependence of these factors on liquid-water path” by S. S. Lee and J. E. Penner

The submitted manuscript examines aerosol-cloud interactions in marine stratocumulus. Specifically, the work attempts to explain an apparent dichotomy between clouds of low and high liquid water path (LWP). Using the Goddard Cumulus Ensemble (GCE), the authors perform eight simulations generating mean LWPs ranging from 73.3 g m−2 down to 36.2 g m−2 by simply multiplying or dividing the latent heat flux from the surface by an arbitrary factor. It is noted that the clouds are considered to be “thin” when the LWP is less than about 50 g m−2. A budget analysis of the production and loss terms (i.e., condensation, evaporation, autoconversion, accretion, and sedimentation) is performed to analyze the microphysical nature of each cloud. This analysis shows that condensation/evaporation is at least 1-2 orders of magnitude greater than the re-
World Bank Document

Although trade liberalization is being actively promoted as a key component in development strategies, theoretically, the impact of trade openness on poverty reduction is ambiguous. On the one hand, a more liberalized trade regime is argued to change relative factor prices in favor of the more abundant factor. If poverty and relative low income stem from abundance of labor, greater trade openness should lead to higher labor prices and a decrease in poverty. However, should the re-allocation of factors be hampered, the expected benefits from freer trade may not materialize. The theoretical ambiguity on the effects of openness is reflected in the available empirical evidence. This paper examines how the effect of trade openness on poverty may depend on complementary reforms that help a country take advantage of international competition. Using a non-linear regression specification that interacts a proxy of trade openness with proxies of various country structural specificities and a panel of 30 African countries over the period 1981-2010, we find that trade openness tends to reduce poverty in countries where financial sectors are deep, education levels high and institutions strong. JEL Classification Numbers: F02, F15, O11, O55
Shared Response Inhibition Deficits but Distinct Error Processing Capacities Between Schizophrenia and Obsessive-Compulsive Disorder Patients Revealed by Event-Related Potentials and Oscillations During a Stop Signal Task

Background: Schizophrenia (SCH) patients are at high risk for obsessive-compulsive syndrome, which can lead to difficulty in differential diagnosis between SCH and obsessive-compulsive disorder (OCD). It would be of great clinical value to identify objective markers for these diseases based on behavioral or neurological manifestations. Deficient response inhibition has been reported in both SCH and OCD; however, it is unclear if common or distinct neural abnormalities underlie this impairment. Methods: To address this question, we compared Stop signal task performance and associated event-related potentials (ERPs) and event-related oscillation (ERO) among 24 SCH patients, 25 OCD patients, and 27 healthy controls (HCs). Results: In successful Stop trials, both SCH and OCD patients showed prolonged Stop signal response time, reduced ERP-P3 component amplitude, and weaker theta-band synchronization compared to HCs, while there were no significant differences between patient groups. In unsuccessful Stop trials, however, SCH patients demonstrated significantly lower P3 amplitudes and weaker theta-band activity than OCD patients. In addition, Stop accuracy rate in SCH patients was negatively correlated with Positive subscale score of the Positive and Negative Syndrome Scale. Conclusions: These results provide evidence that impaired response inhibition in SCH and OCD arises from common underlying neural processing abnormalities. However, the lower P3 amplitude and weaker theta-band activity in SCH patients in unsuccessful Stop trials suggest distinct neural activity patterns related to error processing. These differences in ERPs and ERO may provide clues to unique neurological abnormalities in SCH and provide objective measures for differential diagnosis.
Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction

Existing technologies employ different machine learning approachesto predict disasters from historical environmental data. However,for short-term disasters (e.g., earthquakes), historical data alonehas a limited prediction capability. In this work, we consider so-cial media as a supplementary source of knowledge in additionto historical environmental data. Further, we build a joint modelthat learns from disaster-related tweets and environmental data toimprove prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with theirsemantics representations computed with the traditionalword2vec.Our experiments show that our proposed approach outperformsthe accuracy of state-of-the-art models in disaster prediction
A Calibration Metric for Risk Scores with Survival Data

We study methods for assessing the degree of systematic overor underestimation, known as calibration, of a learned risk model in an independent validation cohort. Here, we advance methods for evaluating clinical risk prediction models by deriving a population parameter measuring the average calibration error of the predicted risk from the true risk, and providing a method for estimation and inference. Our approach improves upon commonly-used goodness of fit tests that depends on subjective bin thresholding and may yield misleading results by reporting confidence intervals for the calibration error instead of a simple P -value that conflate calibration error and sample size. This approach enables comparison among multiple risk prediction models, and can guide model revision. We illustrate how our new method helps to understand the calibration of risk models that have been profoundly influential in clinical practice, but controversial due to their potential miscalibration.
Multidimensional Traffic State Discrimination Based on Floating Car Data

Floating Car Data (FCD) is a kind of emerging data in the field of traffic engineering. There are three problems in its application of the traffic state discrimination: First, the existing traffic state discrimination model is only for road segment detection; Second, the road-segment based discrimination model is not conducive to the spatial-temporal evolution analysis of traffic state. Third, the existing road segment traffic state discrimination model directly adopts the prototypical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and the detection result is limited in accuracy. We proposed a multi-dimensional traffic state discrimination method. Firstly, the traffic state of the road segment is determined based on the improved DBSCAN algorithm. The dynamic segmentation technology is used to realize the visualization of the road traffic state. Then, the traffic incident point discrimination model is constructed according to the spatial-temporal evolution pattern of the road conditions under traffic incidents. The visualization results show that the proposed method can achieve relatively fine multidimensional traffic state discrimination.
ASP and Ontologies for Reasoning on Business Processes

In this paper we show that Answer Set Programming (ASP) can accommodate for domain ontologies in modeling and reasoning about Business Processes, especially for process verification. In this work, knowledge on the process domain is expressed in a low-complexity description logic (DL), and terms from the ontology can be used in embedding business rules in the model as well as in expressing constraints that should be verified to achieve compliance by design. Causal rules for reasoning on side-effects of activities in the process domain can be derived, based on knowledge expressed in the DL. We show how ASP can accommodate them, relying on reasoning about actions and change, for process analysis, and, in particular, for verifying formulas in temporal logic.