Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow

Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations---which can be intractable in SCFMs, as branching on relaxations requires evaluating all (exponentially many) branching paths. Tractable alternatives mainly combine REINFORCE with complex control-variate schemes to improve the variance of naive estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs. Further, in contrast to the importance weighted autoencoder, we observe that RWS learns better models and inference networks with increasing numbers of particles. Our results suggest that RWS is a competitive, often preferable, alternative for learning SCFMs.
Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits

We study the classical linear bandit problem on graphs modelling arm rewards through an underlying graph structure G ( V , E ) such that rewards of neighboring nodes are similar. Previous attempts along this line have primarily considered the arm rewards to be a smooth function over graph Laplacian, which however failed to characterize the inherent problem complexity in terms of the graph structure.We bridge this gap by showing a regret guarantee of ˜ O ( χ ( G ) √ T ) 1 that scales only with the chromatic number of the complement graph χ ( G ) , assuming the rewards to be a smooth function over a general class of graph embeddings— Orthonormal Rep-resentations . Our proposed algorithms yield a regret guarantee of ˜ O ( r √ T ) for any general embedding of rank r . Moreover, if the rewards correspond to a minimum rank embedding, the regret boils down to ˜ O ( χ ( G ) √ T ) – none of the existing works were able to bring out such inﬂuences of graph structures over arm rewards. Finally, noting that computing the above minimum rank embedding is NP-Hard, we also propose an alternative O ( | V | + | E | ) time computable embedding scheme— Greedy Embeddings —based on greedy graph coloring, with which our algorithms perform optimally on a large family of graphs, e.g. union of cliques, complement of k -colorable graphs, regular graphs, trees etc, and are also shown to outperform state-of-the-art methods on real datasets. Our ﬁndings open up new roads for exploiting graph structures on regret performance.
Short-term implicit memory for words and nonwords.

A short-term implicit memory effect is reported and interpreted as arising within the word recognition system. In Experiment 1, repetition priming in lexical decision was determined for low-frequency words and pseudowords at lags of 0, 1, 2, 3, 4, 5, 9, and 23 intervening items. For words, a large short-term priming component decayed rapidly but smoothly over the first 3 items (8 s) to a stable long-term value. For nonwords, priming dropped to the long-term value with a single intervening item. This Lag x Lexicality interaction was replicated with a naming task in Experiment 2 and with high-frequency words in Experiment 3. Word frequency affected long-term priming but not the size or decay rate of short-term priming, dissociating the two repetition effects. In Experiment 4, an old-new decision task was used to test explicit memory. Parallel word and nonword decay patterns were found, dissociating short-term priming from explicit working memory.
End-to-end defect detection in automated fiber placement based on artificially generated data

Automated fiber placement (AFP) is an advanced manufacturing technology that has led to an increased rate of production in composite materials. At the same time, the need for adaptable and fast inline control methods increases. Existing inspection systems make use of handcrafted filter chains and feature detectors tuned for a specific measurement method by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network using a recent architecture designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.
How curvilinear mask patterning will enhance the EUV process window: a study using rigorous wafer+mask dual simulation

It has been known for quite a long time that the best possible process window obtainable for 193i layers is by using ILT correction. These are typically converted (at a high runtime cost) to Manhattan masks both for reasons of mask manufacturability as well as computational efficiency; OPC M3D and rigorous simulators have, until very recently, been optimized for speed to run primarily with Manhattan shapes. We have recently shown that the insertion into production of multibeam mask writers makes writing curvilinear masks possible and that it is preferable to move toward a completely curvilinear paradigm, not only because the ILT is better, but because the mask manufactured will have reduced variability. Recent studies have shown a similar need for ILT-style corrections for EUV, mainly due to more complex thick mask effects. We extend the work using Monte-Carlo methods for mask variability to show that EUV layers more strongly require curvilinear approaches to mask writing in order to minimize the wafer PV bands due to both the tighter overall tolerances combined with the smaller wavelength (13.5 vs, 193) which transfers mask defects to wafer over smaller lengthscales.
Integrated model of application of remote sensing and field investigations for sanitary status assessment of forest stands in two reserves in West Balkan Range, Bulgaria

Over the last decades, massive forest decline has occurred in many countries because of prolonged periods of drought and anomalous climatic phenomena. Studies show that in most cases this is the result of a combination of unfavourable climatic conditions and impact of harmful biotic factors, mostly insect pests and fungal pathogens. The massiveness of these unfavourable phenomena, as well as the specificities of their occurrence and spatial distribution, including mountainous and difficult to access areas, require the application of flexible, high-tech methods of collecting and processing data and information, and in recent years, modern unmanned aerial platforms and systems. This article presents the used approach, the methodology for complex assessment and the results obtained in integrated application of the potential of modern unmanned aerial platforms and traditional entomological and phytopathological methods for field investigation of sanitary status of two protected areas in West Balkan Range in Bulgaria – Gornata koria Reserve and Chuprene Biosphere Reserve.
Estimation of irrigation water requirements at irrigation district level using MODIS evapotranspiration product

An accurate Soil Water Balance (SWB) assessment is a crucial tool to guarantee efficient and sustainable management of water resources in agriculture, especially in the context of limited water availability. However, developing an SWB over large areas (i.e. irrigation district, river basin) requires a large number of input data obtained from expensive and timeconsuming field measurements. Remote sensing (RS) thus represents a valid source to retrieve the necessary biophysical vegetation’s parameters useful for estimating the SWB’s components. The main scope of this study is to set an operational large-scale SWB model, derived from an adaptation of FAO-56 model, and test its capacity to estimate the seasonal Irrigation Water Requirements (IWR) using as input a combination of in-situ agrometeorological measurements and MODIS derived evapotranspiration and vegetation’s parameters products. The SWB was assessed over the “Sinistra Ofanto” irrigation scheme (Italy), characterized by a semi-arid climate and an extremely fragmented and heterogeneous landscape with the presence of the most significant Mediterranean crops. The comparison of the estimated IWR with the water volumes provides by the Irrigation Consortium for the considerate two crop season (2015 and 2016) allows a first assessment about the SWB’s performance. Though the moderate spatial resolution of MODIS products (500 meters), the comparison demonstrates as the prosed SWB procedure has a good capability to estimate the IWR volumes at irrigation district scale in a simple and cost-effective way. Moreover, the SWB proposed exploits only free of charge products and thus can be easily applied in other contexts characterized by similar agroclimatic conditions and limited field data.
Non invasive control and scoring of psoriasis severity

In dermatology, image processing allows non-contact and non-invasive metrological measurements. Psoriasis is an incurable skin disease with an unknown origin. One of the most important tasks in the treatment of psoriasis is to evaluate the degree of the illness following a severity score. Dermatologists use visual and tactile senses to assess the lesions severity. In this article, we propose an automated methodology for assessing objectively the severity of psoriasis by measuring the physical parameters of the skin. Thus, from the colorimetry and geometry obtained by photometric-stereo, we determine the level of erythema and skin thickness. Our results show that for a low acquisition time, the scores obtained are highly correlated with those of dermatologists.
Guess who? On the importance of using appropriate name: case study of Marphysasanguinea (Montagu, 1813)

Abstract The common bait worm Marphysasanguinea (Montagu, 1813), originally described from the south coast of England, is the type species of the genus. This species has been widely reported from all around the world and has been considered as cosmopolitan until recently. This is partly because the original description was very brief and poorly illustrated, and also because all species superficially look similar. In order to clarify the situation, M.sanguinea was redescribed and a neotype was designated by Hutchings and Karageorgpoulos in 2003. Recently, specimens from Cornwall, close to the type locality, were sampled, examined morphologically, and used to obtain COI gene sequences for this species. Molecular results permitted us to confirm the identity and presence of M.sanguinea along the French coasts and to highlight the presence of inaccurate sequences of this species on GenBank. Use of this “false” cosmopolitan species at a worldwide scale by many biologists is also discussed in this paper.
Towards a fully automatic processing chain for operationally mapping burned areas countrywide exploiting Sentinel-2 imagery

Burned area mapping is essential for quantifying the environmental impact of wildfires, for compiling statistics, and for designing effective short- to mid-term impact mitigation measures. The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. However, the high detail and volume of the information provided actually encumbers the automation of the mapping process, at least for the level of automation required to map systematically wildfires on a national level. This paper presents a preliminary methodology for mapping burned areas using Sentinel-2 data, which aims to eliminate user interaction and achieve mapping accuracy that is acceptable for operational use. It follows an objectbased image analysis (OBIA) approach, whereby the initial image is segmented into a set of adjacent and non-overlapping small regions (objects). The most unambiguous of them are labeled automatically through a set of empirical rules that combine information extracted from both a pre-fire Sentinel-2 image and a post-fire one. The burned area is finally delineated following a supervised learning approach, whereby a Support Vector Machine (SVM) is trained using the labeled objects and subsequently applied to the whole image considering a set of optimally selected object-level features. Preliminary results on a set of recent large wildfires in Greece indicate that the proposed methodology constitutes a solid basis for fully automating the burned area mapping process.
Measuring code similarity using word mover's distance for programming course

Teachers tend to ask students submit their assignments online not only in online courses but also face to face courses. The phenomena of plagiarism is becoming more and more serious due to the ease with which resources can be found on the Internet also, especially in a computer programming course. This paper aims to develop a robust automated detection technology of code plagiarism towards programming course. After analyzing and summarized state of art of code plagiarism technology, a more robust detection technology is developed by combining word2vec with Word mover's distance (WMD) similarity metric in the paper. We consider the different plagiarism methods when students commit their program source code. Then we collect more than 20 thousands code submissions in our introductory C++ programming course for non-major students and check whether it is a plagiarized code manually. In the process, we examine how our proposed method compare with two other main algorithms and their suitability for different plagiarism characteristics. The results obtained on the dataset indicate that our approach is well suited for detect different types of code plagiarism. We conclude that incorporating WMD similarity metric is crucial for improved effective and adaptability.
Fake It Till You Make It: Learning-Compatible Performance Support

A longstanding goal of artiﬁcial intelligence is to develop technologies that augment or assist humans. Current approaches to developing agents that can assist humans focus on adapting behavior of the assistant, and do not consider the potential for assistants to support human learning. We argue that in many cases, it is worthwhile to provide assistance in a manner that also promotes task learning or skill maintenance. We term such assistance Learning-Compatible Performance Support, and present the Stochastic Q Bumpers algorithm for greatly improving learning outcomes while still providing high levels of performance support. We demonstrate the effectiveness of our approach in multiple domains with simulated learners, including a complex ﬂight control task.
Evaluation of local CD and placement distribution on EUV mask and its impact on wafer

The mask is a known contributor to intra-field fingerprints at the wafer level. Traditionally, a 3σ distribution of critical dimensions (CDs) on mask was considered sufficient to characterize the contribution to the CD distribution at wafer level. Recent studies report wafer local CD distributions characterized for statistics beyond 3σ1. Mask has been shown to contribute to wafer local CD distribution also which is typically quantified as Local CD Uniformity (LCDU), a 3σ metric2. Additionally, the local placement distribution on wafer could be a contributor to Edge Placement Error (EPE)3. Consequently, it is imperative to understand, characterize and ultimately control the mask contributions to local CD and placement distribution at wafer level. This work is an investigation of local CD and placement distribution on an EUV mask and its impact on distributions at wafer level.
The Speed of Neural Visual Motion Perception and Processing Determines the Visuomotor Reaction Time of Young Elite Table Tennis Athletes

Purpose: Recent research in adult badminton athletes has shown the visuomotor reaction time (VMRT) is strongly dependent on the speed of visual signal perception and processing in the brain’s visual motion system. However, it remains unclear if this relation can be confirmed for other visuomotor demanding disciplines as well as different age groups. This study aimed to validate previous findings in international elite youth table tennis players to shed light on the generalizability of neural performance determinants across different visuomotor demanding sports and age groups. Methods: Thirty-seven young elite international table tennis players (18 male, 19 female, mean age: 13.5 years) from 23 nations participated in this study. Participants performed a visuomotor reaction task in response to visual motion stimuli presented at two different motion velocity conditions. Visuomotor performance was evaluated by measuring the electromyographic (EMG) onset as well as the VMRT. In addition, a 64-channel electroencephalography (EEG) system was used to investigate the stimulus and response-locked event-related potentials (ERPs) in the brain’s visual motion sensitive area MT as well as the pre- and supplementary motor cortex indicating the speed of cortical visual and motor information processing, respectively. Correlation and multiple regression analyses identified the neural processes determining visuomotor performance. Results: The VMRT (232 vs. 258 ms, P < 0.001, d = −2.33) and EMG onset (181 vs. 206 ms, P < 0.001, d = −2.14) were accelerated in the fast motion velocity condition which was accompanied by an earlier stimulus-locked N2 (187 vs. 193 ms, P < 0.001, d = −0.80) and later response-locked N2-r (17 vs. −0.1 ms, P < 0.001, d = 1.04). The N2 and N2-r latencies were correlated with EMG onset and VMRT in both velocity conditions and explained between 80% and 90% of the variance in visuomotor reaction speed. Neural processes in BA6 did not differ between stimulus velocity conditions and did not contribute to the regression model. Conclusion: The results validate our previous findings and support the importance of neural visual processes for the visuomotor reaction speed across different visuomotor demanding sports and age groups. This suggests the visual system might be a promising target for specific visual diagnostics and training interventions.
Sensor scheduling method for space-based early warning system

The sensor scheduling problem for the space-based early warning system is studied.The process and characteristics of early warning tasks are analyzed,and a task decomposing method based on key-points is presented,which can not only simplify relationships between the multiple tasks and resources,but also reduce resource occupancy of one task.An algorithm based on multiplex priority is designed to solve the scheduling problem of all sub-tasks.Experimental results show that satisfying scheduling schemes can be attained in a receivable timespan.
Non-invasive and label-free follow-up of accelerated-crosslinking using multiphoton tomography

In clinical practice, ectatic disorders, such as keratoconus, are treated by accelerated corneal collagen crosslinking (ACXL). The treatment is based on the photodynamic reaction of riboflavin with ultraviolet A (UVA) light and increases the cornea’s mechanical stability. The clinical outcome of ACXL is usually evaluated several weeks post-treatment. An earlier evaluation could lead to a faster re-intervention in case of failure which could avoid additional discomfort and pain for the patient. We propose multiphoton tomography (MPT) to evaluate the outcome of ACXL soon after treatment. In this study, we investigate ACXL-induced changes to the cornea autofluorescence (AF) using MPT. ACXL was performed in de-epithelialized corneal donor buttons and keratoconus corneas by infusing the samples with 0.1% riboflavin solution followed by UVA irradiation using either an in-house adapted system or a commercial ACXL system. AF lifetime images of the tissue were acquired prior and after treatment using MPT. As a control, corneas without treatment were monitored at the same time points. Higher AF lifetimes were observed in the stroma of treated corneas than in control samples. The stroma AF lifetime was higher anteriorly, corresponding to the area where ACXL was most effective. First changes were observed as soon as 2 ℎ after treatment. We demonstrate that MPT can be used to follow-up the outcome of ACXL and that ACXL-induced changes can be detected sooner than with conventional methods and non-invasively.
Automatic camera pose estimation based on a flat surface map

This paper proposes a novel approach that performs extrinsic parameter estimation of a camera installed in a man-made environment using a single image. The problem of extrinsic parameter calibration is identical to 6DoF (six-degrees of freedom) localization problem of the camera. We take advantage of line information that is usually present in the man-made environment such as inside of the building. Our approach only requires a flat surface map for a 3D environment model which can be easily obtained from the blueprint of the artificial environment (e.g., CAD data). In order to manage the complicated 6DoF search problem, we propose a novel image descriptor defined in quantized Hough space to perform 3D-2D matching process between line features from the 3D flat surface model and the 2D single image. The proposed method can robustly estimate the complete extrinsic parameters of the camera, as we demonstrate experimentally.
A testbed for intelligent software defined security framework

In this paper, we propose an IntelligentSDS testbed, which is based on a novel Software Defined Security (SDS) framework powered with Artificial Intelligence. In particular, IntelligentSDS aims to flexibly deploy multiple security agents to white-box devices, so as to automatically learn ongoing threats and proactively protect the network. In particular, we discuss the design and realization of OpenStack-based intelligentSDS cloud, software-defined firewall, intelligent autodetection and defense system, and honeynet.
Computation offloading for mobile edge computing with accuracy guarantee

In this paper, we investigate the problem of energy expenditure minimization under latency and accuracy constraints in mobile edge computing (MEC)-based computation offloading. Given the non-convexity of the formulated problem, we first propose an energy-efficient computation resource allocation scheme inspired by the recent successive convex approximation (SCA) advances. After carefully exploring the problem structure, we fortunately derive the optimal solution, whose optimality is theoretically proved. Numerical results show that, compared with the SCA-based scheme and two other benchmark schemes, the optimal computation resource allocation scheme achieves the lowest energy consumption while satisfying the latency and accuracy requirements.
Variational Training for Large-Scale Noisy-OR Bayesian Networks

We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships. One application is to the learning of hierarchical topic models for text data. While previous work has focused on two-layer networks popular in applications like medical diagnosis, we develop scalable algorithms for deep networks that capture a multi-level hierarchy of interactions. Our key innovation is a family of constrained variational bounds that only explicitly optimize posterior probabilities for the sub-graph of topics most related to the sparse observations in a given document. These constrained bounds have comparable accuracy but dramatically reduced computational cost. Using stochastic gradient updates based on our variational bounds, we learn noisy-OR Bayesian networks orders of magnitude faster than was possible with prior Monte Carlo learning algorithms, and provide a new tool for understanding large-scale binary data.