Miracle-dz/UniFAR-FTUs huggingface dataset:
MAVEN: Multi-Agent Variational Exploration

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in complex environments [43]. We specifically focus on QMIX [40], the current state-of-the-art in this domain. We show that the representational constraints on the joint action-values introduced by QMIX and similar methods lead to provably poor exploration and suboptimality. Furthermore, we propose a novel approach called MAVEN that hybridises value and policy-based methods by introducing a latent space for hierarchical control. The value-based agents condition their behaviour on the shared latent variable controlled by a hierarchical policy. This allows MAVEN to achieve committed, temporally extended exploration, which is key to solving complex multi-agent tasks. Our experimental results show that MAVEN achieves significant performance improvements on the challenging SMAC domain [43].
Evaluation of leaf-level optical properties employed in land surface models

Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.
Toward Designing Cost-Optimal Policies to Utilize IaaS Clouds with Online Learning

Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51 percent when spot and on-demand instances are considered and of up to 43.74 percent when self-owned instances are considered, compared to previously proposed or intuitive policies.
CHEMICAL COMPOSITION OF CAMEL'S COLOSTRUM AND MILK IN UNITED ARAB EMIRATES

The chemical composition of 350 samples from camel's colostrum and milk in United Arab Emirates were studied. Results of colostrum showed 6.19 for pH, 1.041g/ml for density, 3.1% for fat, 4.95% for protein, 4.4% for lactose, 12.88% for total solids, 10.55% for non fat total solids, 1.11% for ash, 87% for moisture . For mature milk, data were 6.6 for pH, 0.133% for acidity, 1.028g/ml for density, 3.2% for fat, 2.4% for protein, 4.6% for lactose, 11% for total solids, 7.88% for non-fat total solids, 0.88% for ash, and 88.9% for moisture. The minerals contents in camel colostrum were (mg/100mg) as follows: 103 for calcium, 15.4 for magnesium, 14.0 for sodium, 128 for potassium, 0.25 for iron, 0.17 for copper and 43.4 for inorganic phosphate. Elements in mature camel milk were (mg/100mg) as follows: 74.67 for calcium, 3.4 for magnesium, 42.36 for sodium, 136.64 for potassium, 0.18 for iron, 0.04 for copper, 0.02 for manganese, 0.3 for zinc and 38.4 for phosphorus.
Reconstructing Capsule Networks for Zero-shot Intent Classification

Intent classification is an important building block of dialogue systems. With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification. Nevertheless, research on this problem is still in the incipient stage and few methods are available. A recently proposed zero-shot intent classification method, IntentCapsNet, has been shown to achieve state-of-the-art performance. However, it has two unaddressed limitations: (1) it cannot deal with polysemy when extracting semantic capsules; (2) it hardly recognizes the utterances of unseen intents in the generalized zero-shot intent classification setting. To overcome these limitations, we propose to reconstruct capsule networks for zero-shot intent classification. First, we introduce a dimensional attention mechanism to fight against polysemy. Second, we reconstruct the transformation matrices for unseen intents by utilizing abundant latent information of the labeled utterances, which significantly improves the model generalization ability. Experimental results on two task-oriented dialogue datasets in different languages show that our proposed method outperforms IntentCapsNet and other strong baselines.
Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection

We address the problem of Duplicate Question Detection (DQD) in low-resource domain-specific Community Question Answering forums. Our multi-view framework MV-DASE combines an ensemble of sentence encoders via Generalized Canonical Correlation Analysis, using unlabeled data only. In our experiments, the ensemble includes generic and domain-specific averaged word embeddings, domain-finetuned BERT and the Universal Sentence Encoder. We evaluate MV-DASE on the CQADupStack corpus and on additional low-resource Stack Exchange forums. Combining the strengths of different encoders, we significantly outperform BM25, all single-view systems as well as a recent supervised domain-adversarial DQD method.
Sentiment Analysis of News Articles in Spanish using Predicate Features

The automatic prediction of the course of action of agents involved in social or economic trends is an imperative challenge nowadays. However, it is a difficult task because stance or opinion is often spread throughout long, complex texts, such as news articles. The current study tests sentence predicates as features to automatically determine the writer’s stance in news articles. We capture the semantics and stance of the text by encoding features such as the attribute of copulative sentences, the predicate of transitive sentences, adjectival phrases, and the section of the article. Under the assumption that these features are informative enough to model the semantics of the text, each word sequence is disambiguated and assigned a sentiment value using weighting rules. Different experiments were run using either SentiWordNet and ML-Senticon to determine words’ sentiment. Feature vectors are automatically built to populate a database that is tested using two machine learning algorithms. An efficiency of 69% was achieved using a SVM with Gaussian kernel along with a feature selection strategy. This score outperformed the bag-of-words baseline in 12%. These results are promising considering that the sentiment analysis is performed on very complex texts written in Spanish.
Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks

Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.
CL-CPPA: Certificate-Less Conditional Privacy-Preserving Authentication Protocol for the Internet of Vehicles

The conditional privacy-preserving authentication (CPPA) protocol has applications in the construction of secure Internet of Vehicles (IoV) due to its capability to achieve both privacy preservation and authentication simultaneously. We demonstrate the challenge in designing the secure CPPA protocols, e.g., the insecurity of master key, invalidity of pseudo-identity (PID), linkability, etc. Then, we present a certificate-less CPPA (CL-CPPA) protocol to be used in vehicular environments that supports both privacy and security requirements in the IoV system, where the vehicles and trusted authority (TA) does not need to store any certificates for verification and tracking, respectively. We also demonstrate that our proposed CL-CPPA protocol is secure against modification attacks, impersonation attacks, and other existing attacks. A comparative summary shows that our proposed CL-CPPA protocol has lower computation and communication costs in comparison to the state-of-the-art protocols.
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation

We propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier. We advance existing methods in two aspects: feature extraction and type prediction. To capture richer contextual information, we adopt contextualized word representations instead of fixed word embeddings used in previous work. In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. We also present a hybrid classification method beyond binary relevance to exploit type inter-dependency with latent type representation. Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation. Experiment results on multiple data sets show that our model significantly advances the state-of-the-art on fine-grained entity typing, obtaining up to 6.1% and 5.5% absolute gains in macro averaged F-score and micro averaged F-score respectively.
Multiobjective and Robust Optimization in Pharmacy Delivery and Emergency Department Nurse Staffing

Over the past 20 years, hospitals have seen a drastic improvement in patient service with improved patient recovery times, faster delivery of care, and an increased focus on patient safety. In addition, healthcare has also began to focus on its staffing, making efforts to improve staff satisfaction. Despite these advancements, healthcare spending has continued increasing. The objectives of cost, staff satisfaction, and patient safety do not always align. In addition, in the healthcare sector demand is frequently unknown, compounding the difficulty in solving these problems. This dissertation provides deterministic and robust optimization models to solve the pharmacy distribution problem and the emergency department nurse staffing problem. Pharmacy distribution is one area of the healthcare system which has seen an increase in technology to improve patient safety and help facilitate distributing medication from the central pharmacy to the patient. Pharmacy robots are used to pick medication cost effectively in the central pharmacy while automated dispensing cabinets (ADCs) are point-of-use storage maintained on the inpatient units. These technologies have provided the ability to deliver medication more cost effectively and improve patient safety. However, the two technologies are often implemented independently of each other and therefore may not work cohesively within the distribution process. This dissertation presents a model which focuses on the trade-off between cost and patient safety. The model is solved to minimize the total distribution system cost, including purchasing and maintenance of technology, as well as minimizing the total distribution workload cost. A robust model is formulated and solved to account for the variation in medication demand and determine the effect variation has on planning decisions. Emergency departments treat patients with various illnesses which results in a wide range of complexities in determining staffing. Patient demand varies by the day of the week and the hour of the day. This results in difficulty determining the daily staffing levels necessary to treat patients efficiently while also considering available staffing resources. This dissertation provides a basic model which determines the daily staffing levels for an emergency department based on predetermined staffing levels. This model is expanded by determining staffing levels in the presence of understaffing by assessing nurse to patient ratios. Several additional models are introduced which consider the variation in patient demand that occurs daily. The performance of these models is considered across a calendar year by comparing the results with current patient safety standards and in comparison with each other to determine the best method to determine nurse staffing levels in the presence of variable demand and understaffing.
Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes

Аннотация - Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions. В качестве основного способа повышения эффективности обнаружения состояний узлов и агрегатов RWT мы видим направление предоставления свойства адаптивности для автоматизированной системы обработки данных из различных систем удаления бесконтактной диагностической информации. Глобальная цель может быть достигнута, в частности, за счет использования методов машинного обучения и распознавания ошибок (объектов распознавания). Для повышения эксплуатационной надежности и срока службы основных узлов и агрегатов RWT предложена соответствующая модель и алгоритм машинного обучения системы управления оператором узлов и агрегатов. Предлагается использовать нормированную меру Шеннона для энтропии и критерий информации о расстоянии Кульбака-Лейблера в качестве критерия эффективности обучения автоматизированной системы обнаружения и контроля состояния узла оператора RWT. В статье описано применение предлагаемого способа на примере системы автоматического обнаружения (АДС) состояния тягового двигателя электровоза. Приведены тестовые данные модели и алгоритма в среде MATLAB.
Privacy-Preserving Collaborative Deep Learning With Unreliable Participants

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $\epsilon $ -differential privacy. In particular, for the first time, we consider the existence of unreliable participants, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.
Introduction to Clinical Reproductive Endocrinology

The hypothalamus and the pituitary gland steroid hormones the ovaries and the menstrual cycle - hormonal changes in pregnancy sexual differentiation puberty amenorrhoea oligomenorrhoea and hirsutism - the polycystic ovary syndrom premenstrual (postovulatory) syndrome endometriosis and adenomyosis female subfertility male subfertility miscarriage, ectopic pregnancy and hydatidiform mole menorrhagia and dysmenorrhoea the menopause drug treatment.
Subquadratic High-Dimensional Hierarchical Clustering

We consider the widely-used average-linkage, single-linkage, and Ward's methods for computing hierarchical clusterings of high-dimensional Euclidean inputs. It is easy to show that there is no efficient implementation of these algorithms in high dimensional Euclidean space since it implicitly requires to solve the closest pair problem, a notoriously difficult problem. However, how fast can these algorithms be implemented if we allow approximation? More precisely: these algorithms successively merge the clusters that are at closest average (for average-linkage), minimum distance (for single-linkage), or inducing the least sum-of-square error (for Ward's). We ask whether one could obtain a significant running-time improvement if the algorithm can merge $\gamma$-approximate closest clusters (namely, clusters that are at distance (average, minimum, or sum-of-square error) at most $\gamma$ times the distance of the closest clusters). We show that one can indeed take advantage of the relaxation and compute the approximate hierarchical clustering tree using $\widetilde{O}(n)$ $\gamma$-approximate nearest neighbor queries. This leads to an algorithm running in time $\widetilde{O}(nd) + n^{1+O(1/\gamma)}$ for $d$-dimensional Euclidean space. We then provide experiments showing that these algorithms perform as well as the non-approximate version for classic classification tasks while achieving a significant speed-up.
Factors Associated with Depression and Low Life Satisfaction in the Low-Income, Frail Elderly

Abstract The aging population in the United States is growing to increasingly larger proportions, and the need for progressive mental health policy and services for the elderly is a function of the rapidly growing elderly population. Lack of attention to mental health needs of the elderly, particularly the frail elderly, is reflected in the dearth of education, literature, and services in this area. This study examined the influence of psychosocial constructs on well-being in a sample of 79 low-income, frail elderly. Statistical models indicated that several psychosocial constructs were significantly associated with well-being among these elders. Results have relevance for social work mental health service delivery and policy development for frail elders.