Miracle-dz/UniFAR-FTUs huggingface dataset:
Rice bast resistance identification based on multi-spectral computer vision

Rice blast resistance identification based on multi-spectral computer vision technology was studied.Multi-spectral camera(MS3100) was used to capture rice seedlings infected by blast sample images,and then image gray values of near infrared(NIR)、red(R) and green(G) were measured by image processing and analysis.The support vector machine method was adopted to build rice blast resistance identification model,and image gray values of three bands (NIR、R and G) as parameters were put into the model.The results show that the classing precision can achieve 100% of healthy samples and sensing disease samples,and 96.8% of healthy samples and middling sensing disease samples.The results provide a new method for rice disease resistance identification and made a preparation for early detection on rice blast.
Measuring Consumer Satisfaction in Treatment Foster Care

Agencies that provide Treatment Foster Care (TFC) often need to meet demands from funders or accrediting bodies to collect consumer satisfaction information. Other agencies may be motivated to collect such information for a Board or management. Or, they may want to monitor and improve service quality or staff and treatment family performance. Some treatment foster care agencies may use these data to inform strategic planning or for external marketing purposes. Others may want information about consumer satisfaction to help interpret client outcomes.
The Enterprise as a System

To enable more efficient and effective enterprise transformation, the enterprise needs to be looked at “as a system,” rather than as a collection of functions connected solely by information systems and shared facilities (Rouse 2005 and 2009; Lawson 2010). What distinguishes the design of enterprise systems from product systems is the inclusion of people as a component of the system, not merely as a user/operator of the system. The term 'enterprise system' has taken on a narrow meaning of only the information system an organization uses. Research and project experience has taught us that to design a good enterprise system, we need to adopt a much broader understanding of enterprise systems. The greater view of enterprise systems is inclusive of the processes the system supports, the people who work in the system, and the information [and knowledge] content of the system. (Giachetti 2010) It is worth noting that the concept of "service" systems also includes people in the system. The thoughts above do not take this into account, primarily since their perspectives come mainly from a product system experience. The practice of service systems engineering is relatively new and is an emerging discipline. For more information on this, see the articles on Service Systems Engineering.
Analyzing Capital Structure across Industries: Evidence from Croatia

Abstract Capital structure refers to the delicate balance between equity and debt that a company uses to finance its assets. It is typically expressed as a debt-to-equity or debt-to-capital ratio, with the components usually located on the right side of the company’s balance sheet. Capital structure can exert great influence on the company’s risk profile and ability to leverage its operations. For this reason, the authors conducted an investigation of the capital structure of 16 joint stock companies listed on the Zagreb Stock Exchange comprising CROBEX, the equity index of Croatia for a three-year period starting in 2015 and ending in 2017. The study demonstrates that many CROBEX-listed companies are very risk averse and choose to remain debt-free. Some are, however, starting to discover the potential offered by financial leverage and have slowly started adjusting their capital structure. In conclusion, capital structure is slowly becoming an issue worthy of discussion on the corporate agendas in Croatia.
Safe Record Sharing in Dynamic Programming Languages

Skyrme, Alexandre Rupert Arpini; Rodriguez, Noemi de La Rocque; Ierusalimschy, Roberto. Safe Record Sharing in Dynamic Programming Languages. Rio de Janeiro, 2015. 77p. DSc Thesis — Departmento de Informática, Pontif́ıcia Universidade Católica do Rio de Janeiro. Dynamic programming languages have become increasingly popular and have been used to implement a range of applications. Meanwhile, multicore processors have become the norm, even for desktop computers and mobile devices. Therefore, programmers must turn to parallelism as a means to improve performance. However, concurrent programming remains difficult. Besides, despite improvements in static languages, we find dynamic languages are still lacking in concurrency support. In this thesis, we argue that the main problem with concurrent programming is unpredictability – unexpected program behaviors, such as returning out-of-thin-air values. We observe that unpredictability is most likely to happen when shared memory is used. Consequently, we propose a concurrency communication model to discipline shared memory in dynamic languages. The model is based on the emerging concurrency patterns of not sharing data by default, data immutability, and types and effects (which we turn into capabilities). It mandates the use of shareable objects to share data. Besides, it establishes that the only means to share a shareable object is to use message passing. Shareable objects can be shared as read-write or read-only, which allows both individual read-write access and parallel read-only access to data. We implemented a prototype in Lua, called luashare, to experiment with the model in practice, as well as to carry out a general performance evaluation. The evaluation showed us that safe data sharing makes it easier to allow for communication among threads. Besides, there are situations where copying data around is simply not an option. However, enforcing control over shareable objects has a performance cost, in particular when working with nested objects.
Regulation of the cyclic AMP response element binding protein (CREB) by group I metabotropic glutamate receptors

Warwick, H.K. , Nahorski, S.R. and Challiss, R.A. J. (2003) Regulation of the cAMPResponse Element Binding (CREB) protein by the Group I Metabotropic Glutamate Receptors British Journal o f Pharmacology 2003 139, 12P Warwick, H.K. , Nahorski, S.R. and Challiss, R.A. J. (2003) Group I Metabotropic Glutamate Receptor-Activated Phosphorylation of the Cyclic AMP-Response Element Binding Protein (CREB). Society for Neuroscience 33rd Annual Meeting, November 2003, New Orleans. Poster 575.13 Poole, M. , Warwick, H.K. , and Challiss, R.A. J. (2003) Effects of p2-adrenoceptor and M3 muscarinic acetylcholine receptor stimulation on the phosphorylation of the cyclic AMP response-element binding protein (CREB). British Pharmacology Society Meeting 2003, pA2 online 1, 183P Warwick, H.K. , Patel, R. , Nahorski, S.R. and Challiss, R.A. J. (2004) Regulation of Nuclear Factor kB (NFkB) by Tumour Necrosis Factor (TNF)aand Histamine-Receptors. The Biochemical Society, Biosciences 2004 Meeting, Glasgow, UK
Hormones and growth factors in nipple aspirates from normal women and benign breast disease patients.

: Several reports have indicated that the breast fluid obtained by nipple aspiration provides valuable biological material for inclusion in studies of breast cancer biochemical epidemiology. Breast fluids contain a variety of hormones and growth factors, often at concentrations considerably higher than those in the corresponding serum. Aspirates from benign breast disease patients have very high levels of bioactive lactogenic hormones and, to a lesser extent, immunoassayable prolactin, compared with healthy controls. A growth hormone-like protein, recognized in a radioimmunoassay but not in a growth hormone-specific immunoradiometric assay, and lacking activity in the Nb2 cell bioassay, is usually present in fluids from Finnish (low breast cancer risk) but not American (high breast cancer risk) women. Epidermal growth factor and transforming growth factor-alpha are present in breast fluids, with preliminary indications that these functionally related polypeptides occur at relatively high concentrations in benign breast disease.
A Hybrid Deep Dependency Parsing Approach Enhanced With Rules and Morphology: A Case Study for Turkish

Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is not high-resource and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on three different Turkish treebanks and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.
Learning Cost Functions for Optimal Transport

Learning the cost function for optimal transport from observed transport plan or its samples has been cast as a bi-level optimization problem. In this paper, we derive an unconstrained convex optimization formulation for the problem which can be further augmented by any customizable regularization. This novel framework avoids repeatedly solving a forward optimal transport problem in each iteration which has been a thorny computational bottleneck for the bi-level optimization approach. To validate the effectiveness of this framework, we develop two numerical algorithms, one is a fast matrix scaling method based on the Sinkhorn-Knopp algorithm for the discrete case, and the other is a supervised learning algorithm that realizes the cost function as a deep neural network in the continuous case. Numerical results demonstrate promising efficiency and accuracy advantages of the proposed algorithms over existing state of the art methods.
Emergent Communication with World Models

We introduce Language World Models, a class of language-conditional generative model which interpret natural language messages by predicting latent codes of future observations. This provides a visual grounding of the message, similar to an enhanced observation of the world, which may include objects outside of the listening agent's field-of-view. We incorporate this "observation" into a persistent memory state, and allow the listening agent's policy to condition on it, akin to the relationship between memory and controller in a World Model. We show this improves effective communication and task success in 2D gridworld speaker-listener navigation tasks. In addition, we develop two losses framed specifically for our model-based formulation to promote positive signalling and positive listening. Finally, because messages are interpreted in a generative model, we can visualize the model beliefs to gain insight into how the communication channel is utilized.
My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits

Consider N cooperative but non-communicating players where each plays one out of M arms for T turns. Players have different utilities for each arm, representable as an NxM matrix. These utilities are unknown to the players. In each turn players select an arm and receive a noisy observation of their utility for it. However, if any other players selected the same arm that turn, all colliding players will all receive zero utility due to the conflict. No other communication or coordination between the players is possible. Our goal is to design a distributed algorithm that learns the matching between players and arms that achieves max-min fairness while minimizing the regret. We present an algorithm and prove that it is regret optimal up to a $\log\log T$ factor. This is the first max-min fairness multi-player bandit algorithm with (near) order optimal regret.
Compactly Representing Uniform Interpolants for EUF using (conditional) DAGS

The concept of a uniform interpolant for a quantifier-free formula from a given formula with a list of symbols, while well-known in the logic literature, has been unknown to the formal methods and automated reasoning community. This concept is precisely defined. Two algorithms for computing the uniform interpolant of a quantifier-free formula in EUF endowed with a list of symbols to be eliminated are proposed. The first algorithm is non-deterministic and generates a uniform interpolant expressed as a disjunction of conjunction of literals, whereas the second algorithm gives a compact representation of a uniform interpolant as a conjunction of Horn clauses. Both algorithms exploit efficient dedicated DAG representation of terms. Correctness and completeness proofs are supplied, using arguments combining rewrite techniques with model-theoretic tools.
Optimality Study of Existing Quantum Computing Layout Synthesis Tools

Layout synthesis, an important step in quantum computing, processes quantum circuits to satisfy device layout constraints. In this paper, we construct QUEKO benchmarks for this problem, which have known optimal depths and gate counts. We use QUEKO to evaluate the optimality of current layout synthesis tools, including Cirq from Google, Qiskit from IBM, <inline-formula><tex-math notation="LaTeX">$\mathsf {t}|\mathsf {ket}\rangle$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">t</mml:mi><mml:mo>|</mml:mo><mml:mi mathvariant="sans-serif">ket</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tan-ieq1-3009140.gif"/></alternatives></inline-formula> from Cambridge Quantum Computing, and a recent academic work. To our surprise, despite over a decade of research and development by academia and industry on compilation and synthesis for quantum circuits, we are still able to demonstrate large optimality gaps: 1.5-12x on average on a smaller device and 5-45x on average on a larger device. This suggests substantial room for improvement of the efficiency of quantum computer by better layout synthesis tools. Finally, we also prove the NP-completeness of the layout synthesis problem for quantum computing. We have made the QUEKO benchmarks open-source.
Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques

In todays global economy, accuracy in predicting macro-economic parameters such as the foreign the exchange rate or at least estimating the trend correctly is of key importance for any future investment. In recent times, the use of computational intelligence-based techniques for forecasting macroeconomic variables has been proven highly successful. This paper tries to come up with a multivariate time series approach to forecast the exchange rate (USD/INR) while parallelly comparing the performance of three multivariate prediction modelling techniques: Vector Auto Regression (a Traditional Econometric Technique), Support Vector Machine (a Contemporary Machine Learning Technique), and Recurrent Neural Networks (a Contemporary Deep Learning Technique). We have used monthly historical data for several macroeconomic variables from April 1994 to December 2018 for USA and India to predict USD-INR Foreign Exchange Rate. The results clearly depict that contemporary techniques of SVM and RNN (Long Short-Term Memory) outperform the widely used traditional method of Auto Regression. The RNN model with Long Short-Term Memory (LSTM) provides the maximum accuracy (97.83%) followed by SVM Model (97.17%) and VAR Model (96.31%). At last, we present a brief analysis of the correlation and interdependencies of the variables used for forecasting.
Explicit and Implicit Dynamic Coloring of Graphs with Bounded Arboricity

Graph coloring is a fundamental problem in computer science. We study the fully dynamic version of the problem in which the graph is undergoing edge insertions and deletions and we wish to maintain a vertex-coloring with small update time after each insertion and deletion. We show how to maintain an $O(\alpha \lg n)$-coloring with polylogarithmic update time, where $n$ is the number of vertices in the graph and $\alpha$ is the current arboricity of the graph. This improves upon a result by Solomon and Wein (ESA'18) who maintained an $O(\alpha_{\max}\lg^2 n)$-coloring, where $\alpha_{\max}$ is the maximum arboricity of the graph over all updates. Furthermore, motivated by a lower bound by Barba et al. (Algorithmica'19), we initiate the study of implicit dynamic colorings. Barba et al. showed that dynamic algorithms with polylogarithmic update time cannot maintain an $f(\alpha)$-coloring for any function $f$ when the vertex colors are stored explicitly, i.e., for each vertex the color is stored explicitly in the memory. Previously, all dynamic algorithms maintained explicit colorings. Therefore, we propose to study implicit colorings, i.e., the data structure only needs to offer an efficient query procedure to return the color of a vertex (instead of storing its color explicitly). We provide an algorithm which breaks the lower bound and maintains an implicit $2^{O(\alpha)}$-coloring with polylogarithmic update time. In particular, this yields the first dynamic $O(1)$-coloring for graphs with constant arboricity such as planar graphs or graphs with bounded tree-width, which is impossible using explicit colorings. We also show how to dynamically maintain a partition of the graph's edges into $O(\alpha)$ forests with polylogarithmic update time. We believe this data structure is of independent interest and might have more applications in the future.
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems

Plug-and-play (PnP) is a non-convex framework that combines ADMM or other proximal algorithms with advanced denoiser priors. Recently, PnP has achieved great empirical success, especially with the integration of deep learning-based denoisers. However, a key problem of PnP based approaches is that they require manual parameter tweaking. It is necessary to obtain high-quality results across the high discrepancy in terms of imaging conditions and varying scene content. In this work, we present a tuning-free PnP proximal algorithm, which can automatically determine the internal parameters including the penalty parameter, the denoising strength and the terminal time. A key part of our approach is to develop a policy network for automatic search of parameters, which can be effectively learned via mixed model-free and model-based deep reinforcement learning. We demonstrate, through numerical and visual experiments, that the learned policy can customize different parameters for different states, and often more efficient and effective than existing handcrafted criteria. Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results. This is prevalent on both linear and nonlinear exemplary inverse imaging problems, and in particular, we show promising results on Compressed Sensing MRI and phase retrieval.
The Two Regimes of Deep Network Training

Learning rate schedule has a major impact on the performance of deep learning models. Still, the choice of a schedule is often heuristical. We aim to develop a precise understanding of the effects of different learning rate schedules and the appropriate way to select them. To this end, we isolate two distinct phases of training, the first, which we refer to as the "large-step" regime, exhibits a rather poor performance from an optimization point of view but is the primary contributor to model generalization; the latter, "small-step" regime exhibits much more "convex-like" optimization behavior but used in isolation produces models that generalize poorly. We find that by treating these regimes separately-and em specializing our training algorithm to each one of them, we can significantly simplify learning rate schedules.
The Break-Even Point on Optimization Trajectories of Deep Neural Networks

The early phase of training of deep neural networks is critical for their final performance. In this work, we study how the hyperparameters of stochastic gradient descent (SGD) used in the early phase of training affect the rest of the optimization trajectory. We argue for the existence of the "break-even" point on this trajectory, beyond which the curvature of the loss surface and noise in the gradient are implicitly regularized by SGD. In particular, we demonstrate on multiple classification tasks that using a large learning rate in the initial phase of training reduces the variance of the gradient, and improves the conditioning of the covariance of gradients. These effects are beneficial from the optimization perspective and become visible after the break-even point. Complementing prior work, we also show that using a low learning rate results in bad conditioning of the loss surface even for a neural network with batch normalization layers. In short, our work shows that key properties of the loss surface are strongly influenced by SGD in the early phase of training. We argue that studying the impact of the identified effects on generalization is a promising future direction.
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks

Despite recent progress, deep neural networks generally continue to be vulnerable to so-called adversarial examples--input images with small perturbations that can result in changes in the output classifications, despite no such change in the semantic meaning to human viewers. This is true even for seemingly simple challenges such as the MNIST digit classification task. In part, this suggests that these networks are not relying on the same set of object features as humans use to make these classifications. In this paper we examine an additional, and largely unexplored, cause behind this phenomenon--namely, the use of the conventional training paradigm in which the entire input space is parcellated among the training classes. Owing to this paradigm, learned decision spaces for individual classes span excessively large regions of the input space and include images that have no semantic similarity to images in the training set. In this study, we train models that include a null class. That is, models may "opt-out" of classifying an input image as one of the digit classes. During training, null images are created through a variety of methods, in an attempt to create tighter and more semantically meaningful decision spaces for the digit classes. The best performing models classify nearly all adversarial examples as nulls, rather than mistaking them as a member of an incorrect digit class, while simultaneously maintaining high accuracy on the unperturbed test set. The use of a null class and the training paradigm presented herein may provide an effective defense against adversarial attacks for some applications. Code for replicating this study will be made available at this https URL .