Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Noise Matters for Neural Natural Language Generation

Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97%, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.
Community Detection for Power Systems Network Aggregation Considering Renewable Variability

The increasing penetration of variable renewable energy (VRE) has brought significant challenges for power systems planning and operation. These highly variable sources are typically distributed in the grid; therefore, a detailed representation of transmission bottlenecks is fundamental to approximate the impact of the transmission network on the dispatch with VRE resources. The fine grain temporal scale of short term and day-ahead dispatch, taking into account the network constraints, also mandatory for mid-term planning studies, combined with the high variability of the VRE has brought the need to represent these uncertainties in stochastic optimization models while taking into account the transmission system. These requirements impose a computational burden to solve the planning and operation models. We propose a methodology based on community detection to aggregate the network representation, capable of preserving the locational marginal price (LMP) differences in multiple VRE scenarios, and describe a real-world operational planning study. The optimal expected cost solution considering aggregated networks is compared with the full network representation. Both representations were embedded in an operation model relying on Stochastic Dual Dynamic Programming (SDDP) to deal with the random variables in a multi-stage problem.
Ruminating Word Representations with Random Noised Masker

We introduce a training method for both better word representation and performance, which we call GROVER (Gradual Rumination On the Vector with maskERs). The method is to gradually and iteratively add random noises to word embeddings while training a model. GROVER first starts from conventional training process, and then extracts the fine-tuned representations. Next, we gradually add random noises to the word representations and repeat the training process from scratch, but initialize with the noised word representations. Through the re-training process, we can mitigate some noises to be compensated and utilize other noises to learn better representations. As a result, we can get word representations further fine-tuned and specialized on the task. When we experiment with our method on 5 text classification datasets, our method improves model performances on most of the datasets. Moreover, we show that our method can be combined with other regularization techniques, further improving the model performance.
Multiphoton blockade in the two-photon Jaynes-Cummings model

We study multiphoton blockade and photon-induced tunneling effects in the two-photon Jaynes-Cummings model, where a single-mode cavity field and a two-level atom are coupled via a two-photon interaction. We consider both the cavity-field-driving and the atom-driving cases and find that single-photon blockade and photon-induced tunneling effects can be observed when the cavity mode is driven, while the two-photon blockade effect appears when the atom is driven. For the atom-driving case (the two-photon transition process), we present a criterion of the correlation functions for the multiphoton blockade effect. Specifically, we show that quantum interference can enhance the photon blockade effect in the single-photon cavity-field-driving case. Our results are confirmed by analytically and numerically calculating the correlation function of the cavity-field mode. Our work has potential applications in quantum information processing and paves the way for the study of multiphoton quantum coherent devices.
Molecular Analysis of the Genetic Diversity of Chinese Hami Melon and Its Relationship to the Melon Germplasm from Central and South Asia

Chinese Hami melon consists of the varieties cassaba, chandalak, amen, and zard. To show their genetic diversity, 120 melon accessions, including 24 accessions of Hami melon, were analyzed using molecular markers of nuclear and cytoplasmic genomes. All Hami melon accessions were classified as the large-seed type with seed length longer than 9 mm, like US and Spanish Inodorus melon. Conomon accessions grown in east China were all the small-seed type. Both large- and small-seed types were in landraces from Iran, Afghanistan, Pakistan, and Central Asia. Analysis of an SNP in the PS-ID region (Rpl16-Rpl14) and size polymorphism of ccSSR7 showed that the melon accessions consisted of three chloroplast genome types, that is, maternal lineages. Hami melon accessions were T/338 bp type, which differed from Spanish melon and US Honey Dew (T/333 bp type), indicating a different maternal lineage within group Inodorus. The gene diversity (D), calculated from random amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR) polymorphism, was 0.476 in 120 melon accessions; the largest diversity was in Central Asian accessions (D = 0.377) but was low for Hami melon accessions (D = 0.243), even though Hami melon has diverse morphological traits, earliness, and shelf life. Reflecting such small genetic diversity, Hami melon accessions of vars. ameri and zard were grouped into cluster II, except for one accession, by the unweighted pair group method and the arithmetic mean (UPGMA) cluster analysis. Variety chandalak with distinct characters, such as early maturing and poor shelf life, was assigned to clusters IV and VI, indicating inter-varietal genetic differentiation within Hami melon. Three accessions from Turkmenistan and Afghanistan, with large seeds and T/338 bp type of chloroplast genome, were classified as cluster II with Hami melon accessions of vars. ameri and zard. We therefore concluded that Hami melon may have been transmitted from the west. The small-seed type melon of group Conomon grown in east China may have been introduced into China independently of Hami melon, because it had the A/338 bp type of the chloroplast genome and was clustered distantly from Hami melon according to nuclear genome analysis.
Multi-Path Policy Optimization

Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, particularly for on-policy methods. Previous exploration methods either rely on complex structure to estimate the novelty of states, or incur sensitive hyper-parameters causing instability. In this paper, we propose an efficient exploration method, Multi-Path Policy Optimization (MPPO), which does not incur high computation cost and ensures stability. MPPO maintains an efficient mechanism that effectively utilizes a population of diverse policies to enable better exploration, especially in sparse environments. We also give a theoretical guarantee of the stable performance. We build our scheme upon two widely-adopted on-policy methods, the Trust-Region Policy Optimization (TRPO) algorithm and Proximal Policy Optimization (PPO) algorithm. We conduct extensive experiments on several MuJoCo tasks and their sparsified variants to fairly evaluate the proposed method. Results show that MPPO significantly outperforms state-of-the-art exploration methods in terms of both sample efficiency and final performance.
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization

Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique unsupervised setting with no human-annotated document labels that uses no external knowledge resources and only corpus-derived term statistics to drive the training. Further, we show that semi-supervised training using our architecture on large amounts of unlabeled data can augment performance on the text categorization task when limited labeled data is available. Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del.icio. us and Ohsumed datasets. We will make our code and data publicly available.
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution

Negation is an important characteristic of language, and a major component of information extraction from text. This subtask is of considerable importance to the biomedical domain. Over the years, multiple approaches have been explored to address this problem: Rule-based systems, Machine Learning classifiers, Conditional Random Field models, CNNs and more recently BiLSTMs. In this paper, we look at applying Transfer Learning to this problem. First, we extensively review previous literature addressing Negation Detection and Scope Resolution across the 3 datasets that have gained popularity over the years: the BioScope Corpus, the Sherlock dataset, and the SFU Review Corpus. We then explore the decision choices involved with using BERT, a popular transfer learning model, for this task, and report state-of-the-art results for scope resolution across all 3 datasets. Our model, referred to as NegBERT, achieves a token level F1 score on scope resolution of 92.36 on the Sherlock dataset, 95.68 on the BioScope Abstracts subcorpus, 91.24 on the BioScope Full Papers subcorpus, 90.95 on the SFU Review Corpus, outperforming the previous state-of-the-art systems by a significant margin. We also analyze the model’s generalizability to datasets on which it is not trained.
YELM: End-to-End Contextualized Entity Linking

We propose yet another entity linking model (YELM) which links words to entities instead of spans. This overcomes any difficulties associated with the selection of good candidate mention spans and makes the joint training of mention detection (MD) and entity disambiguation (ED) easily possible. Our model is based on BERT and produces contextualized word embeddings which are trained against a joint MD and ED objective. We achieve state-of-the-art results on several standard entity linking (EL) datasets.
LSTM-based Online Learning: An Efficient EKF Based Algorithm with a Convergence Guarantee

We investigate online nonlinear regression with long short term memory (LSTM) based networks, which we refer to as LSTM-based online learning. For LSTM-based online learning, we introduce a highly efficient extended Kalman filter (EKF) based training algorithm with a theoretical convergence guarantee. Our algorithm is truly online such that it does not make any assumption on the underlying data generating process or the system dynamics of the learning algorithm to guarantee convergence. Through an extensive set of simulations, we illustrate significant performance improvements achieved by our algorithm with respect to the conventional LSTM training methods. We particularly show that our algorithm provides very similar error performance with the EKF learning algorithm in 10-40 times shorter training time depending on the parameter size of the network.
Multiple Pursuer Multiple Evader Differential Games

In this article an <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-pursuer versus <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-evader team conflict is studied. This article extends classical differential game theory to simultaneously address weapon assignments and multiplayer pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players’ optimal strategies require the codesign of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is employed and the Value function of the attendant game is obtained. It is shown that the Value function is continuously differentiable and that it satisfies the Hamilton–Jacobi–Isaacs equation—the curse of dimensionality is overcome and the optimal strategies are obtained. The cases of <inline-formula><tex-math notation="LaTeX">$N=M$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$N>M$</tex-math></inline-formula> are considered. In the latter case, cooperative guidance strategies are also developed in order for the pursuers to exploit their numerical advantage. This article provides a foundation to formally analyze complex and high-dimensional conflicts between teams of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> pursuers and <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> evaders by means of differential game theory.
The accretion environment of Supergiant Fast X-ray Transients probed with XMM-Newton

Supergiant fast X-ray transients (SFXTs) are characterized by a remarkable variability in the X-ray domain, widely ascribed to the accretion from a clumpy stellar wind. In this paper we performed a systematic and homogeneous analysis of sufficiently bright X-ray flares from the SFXTs observed with XMM-Newton to probe spectral variations on timescales as short as a few hundred of seconds. Our ultimate goal is to investigate if SFXT flares and outbursts are triggered by the presence of clumps and eventually reveal whether strongly or mildly dense clumps are required. For all sources, we employ a technique developed by our group, making use of an adaptive rebinned hardness ratio to optimally select the time intervals for the spectral extraction. A total of twelve observations performed in the direction of five SFXTs are reported. We show that both strongly and mildly dense clumps can trigger these events. In the former case, the local absorption column density may increase by a factor of >>3, while in the latter case, the increase is only by a factor of 2-3 (or lower). Overall, there seems to be no obvious correlation between the dynamic ranges in the X-ray fluxes and absorption column densities in SFXTs, with an indication that lower densities are recorded at the highest fluxes. This can be explained by the presence of accretion inhibition mechanism(s). We propose a classification of the flares/outbursts from these sources to drive future observational investigations. We suggest that the difference between the classes of flares/outbursts is related to the fact that the mechanism(s) inhibiting accretion can be overcome more easily in some sources compared to others. We also investigate the possibility that different stellar wind structures, rather than clumps, could provide the means to temporarily overcome the inhibition of accretion in SFXTs.
Can Neural Image Captioning be Controlled via Forced Attention?

Learned dynamic weighting of the conditioning signal (attention) has been shown to improve neural language generation in a variety of settings. The weights applied when generating a particular output sequence have also been viewed as providing a potentially explanatory insight in the internal workings of the generator. In this paper, we reverse the direction of this connection and ask whether through the control of the attention of the model we can control its output. Specifically, we take a standard neural image captioning model that uses attention, and fix the attention to predetermined areas in the image. We evaluate whether the resulting output is more likely to mention the class of the object in that area than the normally generated caption. We introduce three effective methods to control the attention and find that these are producing expected results in up to 27.43% of the cases.
Beyond Similarity: Relation Embedding with Dual Attentions for Item-based Recommendation

Given the effectiveness and ease of use, Item-based Collaborative Filtering (ICF) methods have been broadly used in industry in recent years. The key of ICF lies in the similarity measurement between items, which however is a coarse-grained numerical value that can hardly capture users' fine-grained preferences toward different latent aspects of items from a representation learning perspective. In this paper, we propose a model called REDA (latent Relation Embedding with Dual Attentions) to address this challenge. REDA is essentially a deep learning based recommendation method that employs an item relation embedding scheme through a neural network structure for inter-item relations representation. A relational user embedding is then proposed by aggregating the relation embeddings between all purchased items of a user, which not only better characterizes user preferences but also alleviates the data sparsity problem. Moreover, to capture valid meta-knowledge that reflects users' desired latent aspects and meanwhile suppress their explosive growth towards overfitting, we further propose a dual attentions mechanism, including a memory attention and a weight attention. A relation-wise optimization method is finally developed for model inference by constructing a personalized ranking loss for item relations. Extensive experiments are implemented on real-world datasets and the proposed model is shown to greatly outperform state-of-the-art methods, especially when the data is sparse.
GRuB: Gas-Efficient Blockchain Storage via Workload-Adaptive Data Replication

Modern Blockchains support the execution of user programs, called smart contracts. As a trusted computing platform, smart contracts bring decentralization, computation integrity, open access and information transparency to average users on the Internet. However, running smart-contract programs leads to high costs, known as Gas. Such costs prevent the use of smart contracts in data-intensive application scenarios, such as high-frequency trading and transparency logging. This paper addresses the Gas-based cost effectiveness in the most consuming layer of a smart contract, namely data storage. We present GRuB, a dynamic data-replication framework that monitors the smart-contract workload and makes online replication decisions. A new online algorithm is proposed that provides constant-bounded 'competitiveness' in Gas. To further save Gas, the workload monitor and decision maker are run off the Blockchain and with security against the forging of workload trace being monitored. A GRuB prototype is built, including a smart-contract component on Ethereum and an off-chain middleware on top of Google LevelDB. The cost evaluation under the YCSB workloads shows that GRuB can converge quickly to changing workloads and save Gas significantly compared with static replication schemes. Two case studies are conducted for data-intensive applications, including high-frequency trading and transparency logging, in which running GRuB leads to affordable Gas.
Variation in Senescence Pattern of Different Classes of Rice Tillers and Its Effect on Panicle Biomass Growth and Grain Yield

In rice, the initiation of tillers is staggered and temporally spaced, but maturity is synchronous. Duration of growth in a later-initiated tiller on a higher culm node is shorter and it contributes less biomass and grain yield. The present investigation attempts to discover the manner in which ordered pattern of senescence in basipetal succession impacts source capacity of tillers in two contrasting rice cultivars, namely Lalat (high tillering) and MGD-106 (medium tillering) during the dry season of 2009 and ascertain how tiller production capacity influences dry matter partitioning and tiller dynamics of the plant. In both the rice cultivars, the progress of senescence among different types of tillers was indicated by gradual decline of photosynthetic pigments, total nitrogen and protein concentrations and increase of lipid peroxidation and peroxidase activity of the flag leaf, which affected photosynthetic efficiency. The effects were more pernicious on the newer tillers compared to older tillers. It was observed that metabolic dominance of the older tillers over newer tillers could be accrued due to higher photosynthetic source capacity of the former than that of the latter. It was concluded that flag leaf of a later-initiated tiller is less tolerant to senescence induced photo-oxidative stress, which decreases both source and sink activities. Increase of tiller number and order in rice increases vulnerability of the later-initiated tillers for oxidative stress and grain filling.
Deep Learning for Cardiac Image Segmentation: A Review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
Paradox of modelling curved faults revisited with general non-hypersingular stress Green’s functions

In a dislocation problem, a paradoxical discordance is known to occur between an original smooth curve and an infinitesimally discretized curve. To solve this paradox, we have investigated a non-hypersingular expression for the integral kernel (called the stress Green’s function) which describes the stress field caused by the displacement discontinuity. We first develop a compact alternative expression of the non-hypersingular stress Green’s function for general 2-D and 3-D infinite homogeneous elastic media. We next compute the stress Green’s functions on a curved fault and revisit the paradox. We find that previously obtained non-hypersingular stress Green’s functions are incorrect for curved faults, and that smooth and infinitesimally segmented faults are equivalent. Their compatibility bridges the gap between analytical methods featuring curved faults and numerical methods using subdivided flat patches.