Miracle-dz/UniFAR-FTUs huggingface dataset:
O ct 2 02 1 Distillation of a Bound Entangled State Under the Influence of Different Interactions

In this article, the dynamics of an open quantum system is investigated for one of the bound entangled states proposed by Bennett et al. under the influence of Heisenberg interaction, bi-linear bi-quadratic interaction, and Dzyaloshinskii–Moriya (DM) interaction. During the study, one qutrit of the selected two qutrit bound entangled state has interacted with an auxiliary qutrit through different interactions. It is observed that, although the auxiliary qutrit plays a significant role during the interaction, the probability amplitude of the qutrit does not affect the open quantum system. In the present work, the computable cross-norm or realignment (CCNR) criterion has been used to detect the bound entanglement of the state and the negativity has been applied to measure the free entanglement. In this three-fold study, it is found that the bi-linear bi-quadratic interaction performs better to free the bound entanglement.
Towards Understanding the Data Dependency of Mixup-style Training

In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss can provably lead to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leads to learning the same classifier as standard training.
Towards a Multi-Agent System Architecture for Supply Chain Management

Individual business processes have been changing since the Internet was created, and they are now oriented towards a more distributed and collaborative business model, in an e-commerce environment that adapts itself to the competitive and changing market conditions. This paper presents a multi-agent system architecture for supply chain management, which explores different strategies and offers solutions in a distributed e-commerce environment. The system is designed to support different types of interfaces, which allow interoperating with other business models already developed. In order to show how the entire multi-agent system is being developed, the implementation of a collaborative agent is presented and explained.
Probing spatial orientability of a Friedmann-Robertson-Walker spatially flat spacetime

One important global topological property of a spacetime manifold is orientability. It is widely believed that spatial orientability can only be tested by global journeys around the Universe to check for orientation-reversing closed paths. Since such global journeys are not feasible, theoretical arguments that combine universality of physical experiments with local arrow of time, CP violation and CPT invariance are usually oﬀered to support the choosing of time- and space-orientable spacetime manifolds. The nonexistence of globally deﬁned spinor ﬁelds on a non-orientable spacetime is another theoretical argument for orientability. However, it is conceivable that orientability can be put to test by local physical eﬀects. In this paper, we show that it is possible to locally access spatial orientability of a spatially ﬂat Friedmann–Robertson-Walker spacetime through quantum vacuum electromagnestic ﬂuctuations. We argue that a putative non-orientability of the spatial sections of spatially ﬂat FRW spacetime can be ascertained by the study of the stochastic motions of a charged particle or a point electric dipole under quantum vacuum electromagnetic ﬂuctuations. In particular, the stochastic motions of a dipole permit the recognition of a presumed non-orientability of 3 − space in itself.
Quickest Inference of Network Cascades With Noisy Information

We study the problem of estimating the source of a network cascade given a time series of noisy information about the spread. Initially, there is a single vertex affected by the cascade (the source) and the cascade spreads in discrete time steps across the network. Although the cascade evolution is hidden, one observes a noisy measurement of the evolution at each time step. Given this information, we aim to reliably estimate the cascade source as fast as possible. We investigate Bayesian and minimax formulations of the source estimation problem, and derive near-optimal estimators for simple cascade dynamics and network topologies. In the Bayesian setting, samples are taken until the error of the Bayes-optimal estimator falls below a threshold. For the minimax setting, we design a novel multi-hypothesis sequential probability ratio test. These optimal estimators require <inline-formula> <tex-math notation="LaTeX">$\log \log n / \log (k - 1)$ </tex-math></inline-formula> observations for a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-regular tree network, and <inline-formula> <tex-math notation="LaTeX">$(\log n)^{\frac {1}{\ell + 1}}$ </tex-math></inline-formula> observations for a <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>-dimensional lattice. We then discuss conjectures on source estimation in general topologies. Finally, we provide simulations which validate our theoretical results on trees and lattices, and illustrate the effectiveness of our methods for estimating the sources of cascades on Erdős-Rényi graphs.
Structural Modeling for Dialogue Disentanglement

Tangled multi-party dialogue context leads to challenges for dialogue reading comprehension, where multiple dialogue threads ﬂow simultaneously within the same dialogue history, thus increasing difﬁculties in understanding a dialogue history for both human and machine. Dialogue disentanglement aims to clarify conversation threads in a multi-party dialogue history, thus reducing the difﬁculty of comprehending the long disordered dialogue passage. Existing studies commonly focus on utterance encoding with carefully designed feature engineering-based methods but pay in-adequate attention to dialogue structure. This work designs a novel model to disentangle multi-party history into threads, by taking dialogue structure features into account. Specifically, based on the fact that dialogues are constructed through successive participation of speakers and interactions between users of interest, we extract clues of speaker property and reference of users to model the structure of a long dialogue record. The novel method is evaluated on the Ubuntu IRC dataset and shows state-of-the-art experimental results in dialogue disentanglement.
Anti-Jamming Beam Alignment in Millimeter-Wave MIMO Systems

In millimeter-wave (MMW) multiple-input multiple-output (MIMO) communications, users and their corresponding base station (BS) have to align their beam during both initial access and data transmissions to compensate for the high propagation loss. The beam alignment (BA) procedure specified for 5th Generation (5G) New Radio (NR) has been designed to be fast and precise in the presence of non-malicious interference and noise. A smart jammer might exploit this weakness and may launch an attack during the BA phase in order to degrade the accuracy of beam selection and, thus, adversely impacting the end-to-end performance and quality-of-service experienced by the users. In this paper, we study the effects of a jamming attack at MMW frequencies during the BA procedure used to perform initial access for idle users and adaptation/recovery for connected users. We show that the BA procedure adopted in 5G NR is extremely vulnerable to a smart jamming attack and, consequently, we propose a countermeasure based on the idea of randomized probing, which consists of randomly corrupting the probing sequence transmitted by the BS in order to reject the jamming signal at the UE via a subspace-based technique based on orthogonal projections and jamming cancellation. Numerical results corroborate our theoretical findings and show the very satisfactory accuracy of the proposed anti-jamming approach.
GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts

To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modeling techniques are needed. Existing work tends to apply topic models on written text datasets. In this paper, we propose a topic extractor on video transcripts. Exploiting neural word embeddings through graph-based clustering, we aim to improve usability and semantic coherence. Unlike most topic models, this approach works without knowing the true number of topics, which is important when no such assumption can or should be made. Experimental results on the real-life multimodal dataset MuSe-CaR demonstrates that our approach GraphTMT extracts coherent and meaningful topics and outperforms baseline methods. Furthermore, we successfully demonstrate the applicability of our approach on the popular Citysearch corpus.
Leveraging Order-Free Tag Relations for Context-Aware Recommendation

Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.
Plastidic pyruvate dehydrogenase complex E1 component subunit Alpha1 is involved in galactolipid biosynthesis required for amyloplast development in rice

Summary Starch accounts for over 80% of the total dry weight in cereal endosperm and determines the kernel texture and nutritional quality. Amyloplasts, terminally differentiated plastids, are responsible for starch biosynthesis and storage. We screened a series of rice mutants with floury endosperm to clarify the mechanism underlying amyloplast development and starch synthesis. We identified the floury endosperm19 (flo19) mutant which shows opaque of the interior endosperm. Abnormal compound starch grains (SGs) were present in the endosperm cells of the mutant. Molecular cloning revealed that the FLO19 allele encodes a plastid‐localized pyruvate dehydrogenase complex E1 component subunit α1 (ptPDC‐E1‐α1) that is expressed in all rice tissues. In vivo enzyme assays demonstrated that the flo19 mutant showed decreased activity of the plastidic pyruvate dehydrogenase complex. In addition, the amounts of monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) were much lower in the developing flo19 mutant endosperm, suggesting that FLO19 participates in fatty acid supply for galactolipid biosynthesis in amyloplasts. FLO19 overexpression significantly increased seed size and weight, but did not affect other important agronomic traits, such as panicle length, tiller number and seed setting rate. An analysis of single nucleotide polymorphism data from a panel of rice accessions identified that the pFLO19L haplotype was positively associated with grain length, implying a potential application in rice breeding. In summary, our study demonstrates that FLO19 is involved in galactolipid biosynthesis which is essential for amyloplast development and starch biosynthesis in rice.
Blood DNA methylation score predicts breast cancer risk: applying OPERA in molecular, environmental, genetic and analytic epidemiology

In this issue, Kresovich and colleagues have published a hallmark paper in Molecular, Environmental, Genetic and Analytic Epidemiology. By applying artificial intelligence to the Sister Study they created a new methylation‐based breast cancer risk score (mBCRS) based on blood DNA methylation. Using a prospective design and after accounting for age and questionnaire‐based breast cancer risk factors, the Odds PER Adjusted standard deviation (OPERA) for mBCRS and polygenic risk score (PRS) was 1.58 (95% CI: 1.38, 1.81) and 1.58 (95% CI: 1.36, 1.83), respectively, and the corresponding area under the receiver operating curve was 0.63 for both. Therefore, mBCRS could be as powerful as the current best PRS in differentiating women of the same age in terms of their breast cancer risk. These risk scores are among the strongest known breast cancer risk‐stratifiers, shaded only by new mammogram risk scores based on measures other than conventional mammographic density, such as Cirrocumulus and Cirrus, which when combined have an OPERA as high as 2.3. The combination of PRS and mBCRS with the other measured risk factors gave an OPERA of 2.2. OPERA has many advantages over changes in areas under the receiver operator curve because the latter depend on the order in which risk factors are considered. Although more replication is needed using prospective data to protect against reverse causation, there are many novel molecular and analytic aspects to this paper which uncovers a potential mechanism for how genetic and environmental factors combine to cause breast cancer.
By wind or wing: pollination syndromes and alternate bearing in horticultural systems

Cyclical fluctuations in reproductive output are widespread among perennial plants, from multi-year masting cycles in forest trees to alternate bearing in horticultural crops. In natural systems, ecological drivers such as climate and pollen limitation can result in synchrony among plants. Agricultural practices are generally assumed to outweigh ecological drivers that might synchronize alternate-bearing individuals, but this assumption has not been rigorously assessed and little is known about the role of pollen limitation as a driver of synchrony in alternate-bearing crops. We tested whether alternate-bearing perennial crops show signs of alternate bearing at a national scale and whether the magnitude of national-scale alternate bearing differs across pollination syndromes. We analysed the Food and Agriculture Organization of the United Nations time series (1961–2018) of national crop yields across the top-producing countries of 27 alternate-bearing taxa, 6 wind-pollinated and 21 insect-pollinated. Alternate bearing was common in these national data and more pronounced in wind-pollinated taxa, which exhibited a more negative lag-1 autocorrelation and a higher coefficient of variation (CV). We highlight the mutual benefits of integrating ecological theory and agricultural data for (i) advancing our understanding of perennial plant reproduction across time, space and taxa, and (ii) promoting stable farmer livelihoods and global food supply. This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.
Fire history and weather interact to determine extent and synchrony of mast-seeding in rhizomatous scrub oaks of Florida

In disturbance-prone ecosystems, fitness consequences of plant reproductive strategies are often determined by the relative timing of seed production and disturbance events, but the role of disturbances as proximate drivers of seed production has been overlooked. We use long-term data on seed production in Quercus chapmanii, Q. geminata and Q. inopina, rhizomatous oaks found in south central Florida's oak scrub, to investigate the role of fire history and its interaction with weather in shaping acorn production and its synchrony. Acorn production increased with the time since last fire, combined with additive or interactive effects of spring precipitation (+) or drought (–). Furthermore, multiple matrix regression models revealed that ramet pairs with shared fire history were more synchronous in seed production than ones that burned in different years. Long-term trends suggest that increasingly drier spring weather, in interaction with fire frequency, may drive a decline of seed production. Such declines could affect the community of acorn-reliant vertebrates in the Florida scrub, including endangered Florida scrub-jays (Aphelocoma coerulescens). These results illustrate that fire can function as a proximate driver of seed production in mast-seeding species, highlighting the increasingly recognized importance of interactions among reproductive strategies and disturbance regimes in structuring plant populations and communities. This article is part of the theme issue ‘The ecology and evolution of synchronized seed production in plants’.
Automated mineralogical anomaly detection using a categorization of optical maturity trend at lunar surface

ABSTRACT The mineralogical anomaly is the mineralogically diagnostic character that differs from its surrounding spectra in terms of absorption features of a spectrum. On the airless planetary body like our Moon, the characteristics of mineral exposures are mostly affected by the physical process over time because of space weathering. This inevitable fact produces mature exposures that act as a background for mineralogical anomaly detection. In this regard, the present study categorizes the optical maturation trend of a site, and the background characterization is integrated with anomaly detection, which makes it more practical, robust, and efficient. These fundamental ideas have been improvised into our automated mineralogical anomaly detection method that uses a modified density-based spatial clustering of applications with noise (Modified DBSCAN) algorithm to categorize the maturity trend from various scatter plots of spectral property. Subsequently, the Reed-Xiaoli detector analyzes the data points belonging to below 33% of maximum point density on the corresponding scatter plot. Experimentally, the proposed method has been validated with previously reported results of literatures over the varietal lunar sites (crater Theophilus, crater Humboldt, Hansteen Alpha silicic dome, western inner ring of Mare Moscoviense, and crater Von Kármán), which have diverse mineralogy in the region. The reflectance spectra of anomalous points explore the mineralogical diversity of the studied region using Chandrayaaan-1 Moon Mineralogy Mapper ( ) data. However, it can also operate with any hyperspectral datasets within the spectral range of 400–3000 nm. This flexibility makes our proposition more attractive for an end-user in the field of planetary science.
Towards Realistic Visual Dubbing with Heterogeneous Sources

The task of few-shot visual dubbing focuses on synchronizing the lip movements with arbitrary speech input for any talking head video. Albeit moderate improvements in current approaches, they commonly require high-quality homologous data sources of videos and audios, thus causing the failure to leverage heterogeneous data sufficiently. In practice, it may be intractable to collect the perfect homologous data in some cases, for example, audio-corrupted or picture-blurry videos. To explore this kind of data and support high-fidelity few-shot visual dubbing, in this paper, we novelly propose a simple yet efficient two-stage framework with a higher flexibility of mining heterogeneous data. Specifically, our two-stage paradigm employs facial landmarks as intermediate prior of latent representations and disentangles the lip movements prediction from the core task of realistic talking head generation. By this means, our method makes it possible to independently utilize the training corpus for two-stage sub-networks using more available heterogeneous data easily acquired. Besides, thanks to the disentanglement, our framework allows a further fine-tuning for a given talking head, thereby leading to better speaker-identity preserving in the final synthesized results. Moreover, the proposed method can also transfer appearance features from others to the target speaker. Extensive experimental results demonstrate the superiority of our proposed method in generating highly realistic videos synchronized with the speech over the state-of-the-art.
SparseAdapt: Runtime Control for Sparse Linear Algebra on a Reconfigurable Accelerator

Dynamic adaptation is a post-silicon optimization technique that adapts the hardware to workload phases. However, current adaptive approaches are oblivious to implicit phases that arise from operating on irregular data, such as sparse linear algebra operations. Implicit phases are short-lived and do not exhibit consistent behavior throughout execution. This calls for a high-accuracy, low overhead runtime mechanism for adaptation at a fine granularity. Moreover, adopting such techniques for reconfigurable manycore hardware, such as coarse-grained reconfigurable architectures (CGRAs), adds complexity due to synchronization and resource contention. We propose a lightweight machine learning-based adaptive framework called SparseAdapt. It enables low-overhead control of configuration parameters to tailor the hardware to both implicit (data-driven) and explicit (code-driven) phase changes. SparseAdapt is implemented within the runtime of a recently-proposed CGRA called Transmuter, which has been shown to deliver high performance for irregular sparse operations. SparseAdapt can adapt configuration parameters such as resource sharing, cache capacities, prefetcher aggressiveness, and dynamic voltage-frequency scaling (DVFS). Moreover, it can operate under the constraints of either (i) high energy-efficiency (maximal GFLOPS/W), or (ii) high power-performance (maximal GFLOPS3/W). We evaluate SparseAdapt with sparse matrix-matrix and matrix-vector multiplication (SpMSpM and SpMSpV) routines across a suite of uniform random, power-law and real-world matrices, in addition to end-to-end evaluation on two graph algorithms. SparseAdapt achieves similar performance on SpMSpM as the largest static configuration, with 5.3× better energy-efficiency. Furthermore, on both performance and efficiency, SparseAdapt is at most within 13% of an Oracle that adapts the configuration of each phase with global knowledge of the entire program execution. Finally, SparseAdapt is able to outperform the state-of-the-art approach for runtime reconfiguration by up to 2.9× in terms of energy-efficiency.