Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the Variability of the Structure Parameter in the PROSPECT Leaf Optical Properties Model

Radiative transfer model (RTM) inversion allows for the quantitative estimation of vegetation biochemical composition from satellite sensor data, but large uncertainties associated with inversion make accurate estimation difficult. The leaf structure parameter (Ns) is one of the largest sources of uncertainty in inversion of the widely used leaf-level PROSPECT model, since it is the only parameter that cannot be directly measured. In this study, we characterize Ns as a function of phenology by collecting an extensive dataset of leaf measurements from samples of three dicotyledon species (hard red wheat, soft white wheat, and upland rice) and one monocotyledon (soy), grown under controlled conditions over two full growth seasons. A total of 230 samples were collected: measured leaf reflectance and transmittance were used to estimate Ns from each sample. These experimental data were used to investigate whether Ns depends on phenological stages (early/mid/late), and/or irrigation regime (irrigation at 85%, 75%, 60% of the initial saturated tray weight, and pre-/post-irrigation). The results, supported by the extensive experimental data set, indicate a significant difference between Ns estimated on monocotyledon and dicotyledon plants, and a significant difference between Ns estimated at different phenological stages. Different irrigation regimes did not result in significant Ns differences for either monocotyledon or dicotyledon plant types. To our knowledge, this study provides the first systematic record of Ns as a function of phenology for common crop species.
Better generalization with less data using robust gradient descent

For learning tasks where the data (or losses) may be heavy-tailed, algorithms based on empirical risk minimization may require a substantial number of observations in order to perform well offsample. In pursuit of stronger performance under weaker assumptions, we propose a technique which uses a cheap and robust iterative estimate of the risk gradient, which can be easily fed into any steepest descent procedure. Finite-sample risk bounds are provided under weak moment assumptions on the loss gradient. The algorithm is simple to implement, and empirical tests using simulations and real-world data illustrate that more efficient and reliable learning is possible without prior knowledge of the loss tails.
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning

The ability to perform ofﬂine A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommender systems, search engines, ad placement, and personalized health care. Both ofﬂine A/B-testing and off-policy learning require a counterfactual estimator that evaluates how some new policy would have performed, if it had been used instead of the logging policy. In this paper, we present and analyze a family of counterfactual estimators which sub-sumes most estimators proposed to date. Most importantly, this analysis identiﬁes a new estimator – called Continuous Adaptive Blending (CAB) – which enjoys many advantageous theoretical and practical properties. In particular, it can be substantially less biased than clipped Inverse Propensity Score (IPS) weighting and the Direct Method, and it can have less variance than Doubly Robust and IPS estimators. In addition, it is subdifferentiable such that it can be used for learning, unlike the SWITCH estimator. Experimental results show that CAB provides excellent evaluation accuracy and outperforms other counterfactual estimators in terms of learning performance.
Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences

A standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. In this paper, we propose a graph-based word embedding method, named ‘word-node2vec’. By relaxing the strong constraint of locality, our method is able to capture both the local and non-local co-occurrences. Word-node2vec constructs a graph where every node represents a word and an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization.
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation

We present a deep generative model which explicitly models object occlusions for compositional scene representation. Latent representations of objects are disentangled into location, size, shape, and appearance, and the visual scene can be generated compositionally by integrating these representations and an infinite-dimensional binary vector indicating presences of objects in the scene. By training the model to learn spatial dependences of pixels in the unsupervised setting, the number of objects, pixel-level segregation of objects, and presences of objects in overlapping regions can be estimated through inference of latent variables. Extensive experiments conducted on a series of specially designed datasets demonstrate that the proposed method outperforms two state-of-the-art methods when object occlusions exist.
Validation of Preliminary Results of Thermal Tropopause Derived from FY-3C GNOS Data

The state-of-art global navigation satellite system (GNSS) occultation sounder (GNOS) onboard the FengYun 3 series C satellite (FY-3C) has been in operation for more than five years. The accumulation of FY-3C GNOS atmospheric data makes it ready to be used in atmosphere and climate research fields. This work first introduces FY-3C GNOS into tropopause research and gives the error evaluation results of long-term FY-3C atmosphere profiles. We compare FY-3C results with Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and radiosonde results and also present the FY-3C global seasonal tropopause patterns. The mean temperature deviation between FY-3C GNOS temperature profiles and COSMIC temperature profiles from January 2014 to December 2017 is globally less than 0.2 K, and the bias of tropopause height (TPH) and tropopause temperature (TPT) annual cycle derived from both collocated pairs are about 80–100 m and 1–2 K, respectively. Also, the correlation coefficients between FY-3C GNOS tropopause parameters and each radiosonde counterpart are generally larger than 0.9 and the corresponding regression coefficients are close to 1. Multiple climate phenomena shown in seasonal patterns coincide with results of other relevant studies. Our results demonstrate the long-term stability of FY-3C GNOS atmosphere profiles and utility of FY-3C GNOS data in the climate research field.
Audience and Expert Perspectives on Second Screen Engagement with Political Debates

Televised debates remain a key point in elections, during which there are vast amounts of online activity, much of it conducted through personal devices or second screens. Amidst growing recognition of the influence of online political discourse, we explore the issues and opportunities arising at this specific point in election cycles, using a design-led multi-stakeholder approach to understand both the audience and expert perspectives. Workshops with debate viewers highlighted six key issues and possible solutions, which were encapsulated in four speculative design concepts. These were used to prompt further discussion with political and media experts, who were able to identify the implications and challenges of addressing the opportunities identified by the participants. Together, these perspectives allow us to unravel some of the complexities of designing for this multifaceted problem.
Market Disciplining of the Developing Countries' Sovereign Governments

This paper contributes to the current literature on market disciplining of the sovereign governments in two ways: Firstly, it distinguishes both sides of the market discipline hypothesis (MDH) by adopting 3SLS to incorporate the contemporaneous feedback effects between primary structural budget balances and country default risk premium. Secondly, by utilizing the IMF's disaggregated government finance statistics data, structural primary budget balances are estimated to test the MDH for developing countries. The results show a disciplinary effect of the financial markets on the sovereign governments and the findings are robust to two alternative measurements of structural budget balances. After controlling for the exchange rate regime, the results confirm that sovereign governments are more disciplined in floating countries, while, in countries with fixed exchange rate regime, sovereigns seem to be irresponsive to the change in the default risk premium posed by the market. As for the market's response to change in fiscal indicators, 3SLS estimation results show well-functioning financial markets in the sample countries. Controlling for political ideology does not change the conclusion but financial markets seem to function more efficient in the fixed exchange rate regime seeking countries.
Harnessing Evolution for Multi-Hunk Program Repair

Despite significant advances in automatic program repair (APR) techniques over the past decade, practical deployment remains an elusive goal. One of the important challenges in this regard is the general inability of current APR techniques to produce patches that require edits in multiple locations, i.e., multi-hunk patches. In this work, we present a novel APR technique that generalizes single-hunk repair techniques to include an important class of multi-hunk bugs, namely bugs that may require applying a substantially similar patch at a number of locations. We term such sets of repair locations as evolutionary siblings - similar looking code, instantiated in similar contexts, that are expected to undergo similar changes. At the heart of our proposed method is an analysis to accurately identify a set of evolutionary siblings, for a given bug. This analysis leverages three distinct sources of information, namely the test-suite spectrum, a novel code similarity analysis, and the revision history of the project. The discovered siblings are then simultaneously repaired in a similar fashion. We instantiate this technique in a tool called HERCULES and demonstrate that it is able to correctly fix 46 bugs in the Defects4J dataset, the highest of any individual APR technique to date. This includes 15 multi-hunk bugs and overall 11 bugs which have not been fixed by any other technique so far.
SeeDep: Deploying Reproducible Application Topologies on Cloud Platform

As part of the scientific method, any researcher should be able to reproduce the experimentation in order to not only verify the result but also evaluate and compare this experimentation with other approaches. The need of a standard tool allowing researchers to easily generate, share and reproduce experiments set-up arises. In this paper, we present SeeDep, a framework aiming at being such a standard tool. By associating a generation key to a network experiment set-up, SeeDep allows for reproducing network experiments independently from the used infrastructure.
Studies on the Software Testing Profession

This paper attempts to understand motivators and de-motivators that influence the decisions of software professionals to take up and sustain software testing careers across four different countries, i.e. Canada, China, Cuba, and India. The research question can be framed as "How many software professionals across different geographies are keen to take up testing careers, and what are the reasons for their choices? " Towards that, we developed a cross-sectional but simple survey-based instrument. In this study we investigated how software testers perceived and valued what they do and their environmental settings. The study pointed out the importance of visualizing software testing activities as a set of human-dependent tasks and emphasized the need for research that examines critically individual assessments of software testers about software testing activities. This investigation can help global industry leaders to understand the impact of work-related factors on the motivation of testing professionals, as well as inform and support management and leadership in this context.
Efficient On-Device Models using Neural Projections

Many applications involving visual and language understanding can be effectively solved using deep neural networks. Even though these techniques achieve state-of-the-art results, it is very challenging to apply them on devices with limited memory and computational capacity such as mobile phones, smart watches and IoT. We propose a neural projection approach for training compact on-device neural networks. We introduce projection networks that use locality-sensitive projections to generate compact binary representations and learn small neural networks with computationally efficient operations. We design a joint optimization framework where the projection network can be trained from scratch or leverage existing larger neural networks such as feed-forward NNs, CNNs or RNNs. The trained neural projection network can be directly used for inference on device at low memory and computation cost. We demonstrate the effectiveness of this as a general-purpose approach for significantly shrinking memory requirements of different types of neural networks while preserving good accuracy on multiple visual and text classification tasks.
Mind the Portability: A Warriors Guide through Realistic Profiled Side-channel Analysis

—Proﬁled side-channel attacks represent a practical threat to digital devices, thereby having the potential to disrupt the foundation of e-commerce, the Internet of Things (IoT), and smart cities. In the proﬁled side-channel attack, the adversary gains knowledge about the target device by getting access to a cloned device. Though these two devices are different in real-world scenarios, yet, unfortunately, a large part of research works simpliﬁes the setting by using only a single device for both pro-ﬁling and attacking. There, the portability issue is conveniently ignored to ease the experimental procedure. In parallel to the above developments, machine learning techniques are used in recent literature, demonstrating excellent performance in proﬁled side-channel attacks. Again, unfortunately, the portability is neglected. Inthispaper, we consider realistic side-channel scenarios and commonly used machine learning techniques to evaluate the inﬂu-ence of portability on the efﬁcacy of an attack. Our experimental results show that portability plays an important role and should not be disregarded as it contributes to a signiﬁcant overestimate of the attack efﬁciency, which can easily be an order of magnitude size. After establishing the importance of portability, we propose a new model called the Multiple Device Model (MDM) that formally incorporates the device to device variation during a proﬁled side-channel attack. We show through experimental studies how machine learning and MDM
New Preparation Method of Visible Light Responsive Titanium Dioxide Photocatalytic Films

We report a new and simple preparation method of the visible light responsive Titanium dioxide (TiO2) photocatalytic films using sol-gel method and ultraviolet light (UV) irradiation. Proposed films were prepared on fused silica plates using titanium tetra-isopropoxide, urea, 2-methoxyethanol, water and UV irradiation. The 650°C-annealed films were carbon-containing anatase type TiO2, not carbon-doped ones. The prepared films absorbed visible light with wavelengths longer than 400 nm. Also, organic dyes were effectively photodegradated by visible light irradiation in the presence of these films.
Evaluating Composition Models for Verb Phrase Elliptical Sentence Embeddings

Ellipsis is a natural language phenomenon where part of a sentence is missing and its information must be recovered from its surrounding context, as in “Cats chase dogs and so do foxes. ”. Formal semantics has different methods for resolving ellipsis and recovering the missing information, but the problem has not been considered for distributional semantics, where words have vector embeddings and combinations thereof provide embeddings for sentences. In elliptical sentences these combinations go beyond linear as copying of elided information is necessary. In this paper, we develop different models for embedding VP-elliptical sentences. We extend existing verb disambiguation and sentence similarity datasets to ones containing elliptical phrases and evaluate our models on these datasets for a variety of non-linear combinations and their linear counterparts. We compare results of these compositional models to state of the art holistic sentence encoders. Our results show that non-linear addition and a non-linear tensor-based composition outperform the naive non-compositional baselines and the linear models, and that sentence encoders perform well on sentence similarity, but not on verb disambiguation.
Ca 2 + TRANSPORT PROCESSES OF LOBSTER HEPATOPANCREATIC BRUSH-BORDER MEMBRANE VESICLES ZHENPENG ZHUANG

45Ca2+ uptake by hepatopancreatic brush-border membrane vesicles of Atlantic lobster (Homarus americanus) occurred by a combination of three independent processes: (1) an amiloride-sensitive carriermediated transport system; (2) an amiloride-insensitive carrier-mediated transport system; and (3) a verapamilinhibited channel process responsive to transmembrane potential. Both carrier-mediated processes were antiporters and capable of exchanging external Ca2+ with intravesicular Na+ or H+. The kinetic parameters of both carrier-mediated processes have been reported previously. External amiloride and Zn2+ were both competitive inhibitors of 45Ca2+ influx, reducing entry of the divalent cation at a single binding site with Ki values of 370 mmol l21 for amiloride and 940 mmol l21 for Zn2+. It is concluded that the mechanisms controlling Ca2+ entry into hepatopancreatic epithelial cells include a previously reported electrogenic 2Na+/1H+ antiporter, an electroneutral 2Na+/1Ca2+ antiporter and a verapamilsensitive Ca2+ channel, which might also be used for the entry of Zn2+ and possibly other heavy metals. Evidence from an equilibrium-shift experiment, based on the thermodynamics of a coupled transport process, suggested that both monovalent (Na+) and divalent (Ca2+ and Zn2+) cations may enter hepatopancreatic epithelial cells through a common carrier-mediated transport protein. This suite of hepatopancreatic brush-border Ca2+ transport processes qualitatively resembles that previously reported for the luminal membrane of lobster antennal glands and suggests that crustacean epithelial cells from different organs may handle this divalent cation by similar means.
Variational Laplace Autoencoders

Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. However, such amortized variational inference faces two challenges: (1) the limited posterior expressiveness of fully-factorized Gaussian assumption and (2) the amortization error of the inference model. We present a novel approach that addresses both challenges. First, we focus on ReLU networks with Gaussian output and illustrate their connection to probabilistic PCA. Building on this observation, we derive an iterative algorithm that finds the mode of the posterior and apply full-covariance Gaussian posterior approximation centered on the mode. Subsequently, we present a general framework named Variational Laplace Autoencoders (VLAEs) for training deep generative models. Based on the Laplace approximation of the latent variable posterior, VLAEs enhance the expressiveness of the posterior while reducing the amortization error. Empirical results on MNIST, Omniglot, Fashion-MNIST, SVHN and CIFAR10 show that the proposed approach significantly outperforms other recent amortized or iterative methods on the ReLU networks.
Collaborative Channel Pruning for Deep Networks

Deep networks have achieved impressive performance in various domains, but their applications are largely limited by the prohibitive computational overhead. In this paper, we propose a novel algorithm, namely collaborative channel pruning (CCP), to reduce the computational overhead with negligible performance degradation. The joint impact of pruned/preserved channels on the loss function is quantitatively analyzed, and such interchannel dependency is exploited to determine which channels to be pruned. The channel selection problem is then reformulated as a constrained 0-1 quadratic optimization problem, and the Hessian matrix, which is essential in constructing the above optimization, can be efficiently approximated. Empirical evaluation on two benchmark data sets indicates that our proposed CCP algorithm achieves higher classification accuracy with similar computational complexity than other stateof-the-art channel pruning algorithms.
Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model

Modern advanced driver assistance systems (ADAS) increasingly depend on the information gathered by the vehicle’s on-board sensors about its environment. It is thus of great interest to analyse the performance of these sensor systems and its dependence on macroscopic traffic parameters. The work at hand aims at building up an analytical model to estimate the number of objects contained in a vehicle’s environmental model. It further considers the exchange of vehicle dynamics and sensor data by vehicle-to-vehicle (V2X) communication to enhance the environmental awareness of the single vehicles. Finally, the proposed model is used to quantify the improvement in the environmental model when complementing sensor measurements with V2X communication.
Neural Finite-State Transducers: Beyond Rational Relations

We introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings. The probability of a string pair is obtained by marginalizing over all its accepting paths in a finite state transducer. In contrast to ordinary weighted FSTs, however, each path is scored using an arbitrary function such as a recurrent neural network, which breaks the usual conditional independence assumption (Markov property). NFSTs are more powerful than previous finite-state models with neural features (Rastogi et al., 2016.) We present training and inference algorithms for locally and globally normalized variants of NFSTs. In experiments on different transduction tasks, they compete favorably against seq2seq models while offering interpretable paths that correspond to hard monotonic alignments.