Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Nitrogen and Phosphate on the Levels of Intermediates in Bakers' Yeast Grown in Continuous Culture

SUMMARY: Bakers’ yeast (Saccharomyces cerevisiae) has been grown in continuous culture using a control medium and media which contained low levels of ammonium and phosphate. The effects of medium composition and growth rate on the levels of intermediates of the glycolytic pathways, the tricarboxylic acid cycle and the glyoxylate cycle were investigated. The energy charge varied only between 0-7 and 0-9 over the range of dilution rates studied; however, the level of ATP decreased by 50% at higher aerobic growth rates. Intermediates of the Embden-Meyerhof-Parnas pathway were higher at the low aerobic growth rates and decreased as the dilution rate was increased. However, higher levels of these intermediates were also observed at even higher dilution rates at which ethanol formation and fermentative metabolism occurred. Significant differences in levels of intermediates were observed between control experiments and fermentations using the low nitrogen and phosphate media. The greatest differences were observed in the levels of glucose 6-phosphate, 6-phosphogluconate, pyruvate, citrate and glyoxylate. Twenty-one different steady states were investigated and each was found to have a unique composition.
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets

Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward “real” understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.
Restriction of Laplace–Beltrami Eigenfunctions to Arbitrary Sets on Manifolds

Given a compact Riemannian manifold $(M, g)$ without boundary, we estimate the Lebesgue norm of Laplace-Beltrami eigenfunctions when restricted to a wide variety of subsets $\Gamma$ of $M$. The sets $\Gamma$ that we consider are Borel measurable, Lebesgue-null but otherwise arbitrary with positive Hausdorff dimension. Our estimates are based on Frostman-type ball growth conditions for measures supported on $\Gamma$. For large Lebesgue exponents $p$, these estimates provide a natural generalization of $L^p$ bounds for eigenfunctions restricted to submanifolds, previously obtained in \cite{Ho68, Ho71, Sog88, BGT07}. Under an additional measure-theoretic assumption on $\Gamma$, the estimates are shown to be sharp in this range. As evidence of the genericity of the sharp estimates, we provide a large family of random, Cantor-type sets that are not submanifolds, where the above-mentioned sharp bounds hold almost surely.
Demonstration of a Custom LTE Emulation-based Test Environment

Notwithstanding the value of Long Term Evolution (LTE) towards an improved user experience in next-generation networks, its associated high complexity is known to place computational and time burdens on testing tasks involving real-world platforms. Simulation is currently the tool most widely used to tackle this issue. LENA, for instance, is an open source simulator based on ns-3 that allows the design, evaluation, and validation of LTE networks. Despite of modeling the main LTE elements and interfaces, one limitation of LENA is that it does not support the use of external traﬃc entities in conjunction with the simulation. In this presentation, we will demonstrate an ns-3 LENA LTE framework customized for use in emulation-based test environments. To validate our emulation results, it is possible to use as benchmark a testbed that diﬀers from the aforementioned test environment in that the ns-3 server running the simulated network is replaced with a network made up of real-world platforms. This comparison and the results are described with details in [1]. Initial validation results, based on limited tests using an industry-standard VoIP test tool, demonstrate that ns-3 LTE models can deliver voice quality and latency as good as an experimental testbed using actual LTE equipment over a range of signal-to-noise ra-tios. Similar conclusions are also drawn for throughput, thus conﬁrming the suitability of our emulation approach as a viable means to predict performance in real LTE networks. The good agreement of our experimental results is possible not only because the same functionality is implemented in both experiments but due to the use of the
Constructing Driver Hamiltonians for Several Linear Constraints

Recent advances in the field of adiabatic quantum computing and the closely related field of quantum annealers has centered around using more advanced and novel Hamiltonian representations to solve optimization problems. One of these advances has centered around the development of driver Hamiltonians that commute with the constraints of an optimization problem - allowing for another avenue to satisfying those constraints instead of imposing penalty terms for each of them. In particular, the approach is able to use sparser connectivity to embed several practical problems on quantum devices than other common practices. However, designing the driver Hamiltonians that successfully commute with several constraints has largely been based on strong intuition for specific problems and with no simple general algorithm to generate them for arbitrary constraints. In this work, we develop a simple and intuitive algebraic framework for reasoning about the commutation of Hamiltonians with linear constraints - one that allows us to classify the complexity of finding a driver Hamiltonian for an arbitrary set of constraints as NP-hard.
FaceHack: Triggering backdoored facial recognition systems using facial characteristics

Recent advances in Machine Learning (ML) have opened up new avenues for its extensive use in real-world applications. Facial recognition, specifically, is used from simple friend suggestions in social-media platforms to critical security applications for biometric validation in automated immigration at airports. Considering these scenarios, security vulnerabilities to such ML algorithms pose serious threats with severe outcomes. Recent work demonstrated that Deep Neural Networks (DNNs), typically used in facial recognition systems, are susceptible to backdoor attacks; in other words,the DNNs turn malicious in the presence of a unique trigger. Adhering to common characteristics for being unnoticeable, an ideal trigger is small, localized, and typically not a part of the main im-age. Therefore, detection mechanisms have focused on detecting these distinct trigger-based outliers statistically or through their reconstruction. In this work, we demonstrate that specific changes to facial characteristics may also be used to trigger malicious behavior in an ML model. The changes in the facial attributes maybe embedded artificially using social-media filters or introduced naturally using movements in facial muscles. By construction, our triggers are large, adaptive to the input, and spread over the entire image. We evaluate the success of the attack and validate that it does not interfere with the performance criteria of the model. We also substantiate the undetectability of our triggers by exhaustively testing them with state-of-the-art defenses.
Inferring Contributions from Unresolved Point Sources to Diffuse Emissions Measured in UV Sky Surveys: General Method and SOHO/SWAN Case Study

In observations of diffuse emissions like, e.g., the Lyα heliospheric glow, contributions to the observed signal from point sources (e.g., stars) are considered to be a contamination. There are relatively few bright point sources that are usually properly resolved and can be subtracted or masked. We present results of an analysis of the distribution of point sources using UV sky-survey maps from the Solar and Heliospheric Observatory (SOHO)/Solar Wind ANisotropy (SWAN) instrument and spectrophotometry data from the International Ultraviolet Explorer satellite. The estimated distribution suggests that the number of these sources increases with decreasing intensity. Below a certain threshold, these sources cannot be resolved against the diffuse signal from the backscatter glow, that results in a certain physical background from unresolved point sources. Detection, understanding, and subtraction of the point-source background has implications for proper characterization of diffuse emissions and accurate comparison with models. Stars are also often used as standard candles for in-flight calibration of satellite UV observations, thus proper understanding of signal contributions from the point sources is important for the calibration process. We present a general approach to quantify the background radiation level from unresolved point sources in UV sky-survey maps. In the proposed method, a distribution of point sources as a function of their intensity is properly integrated to compute the background signal level. These general considerations are applied to estimate the unresolved-point-source background in the SOHO/SWAN observations that on average amounts to 28.9 R. We also discuss the background radiation anisotropies and general questions related to modeling the point-source contributions to diffuse UV-emission observations.
Fruit–frugivore interactions in Amazonian seasonally flooded and unflooded forests

Abstract: Constructing community fruit–frugivore networks has proved challenging in tropical forests to date, particularly in lowland Amazonia, which hosts the most diverse spectrum of frugivorous vertebrates and morphological fruit types worldwide. We assessed data on fruit resource production, frugivore assemblages and corresponding fruit–frugivore networks in two contrasting forest types along the Rio Juruá of western Brazilian Amazonia: seasonally flooded várzea (VZ) and unflooded terra firme forest (TF). Over 2 y we conducted monthly surveys of fruit patches and medium- to large-bodied vertebrate frugivores within three 100-ha plots (two TF, one VZ), supplemented by fruit surveys along 67 5-km transects distributed across two contiguous forest reserves (41 TF, 26 VZ). Observations of trophic interactions were supplemented by semi-structured interviews with experienced hunters and fishermen from 16 local communities. The resultant binary networks contained low proportions of all potential interactions (TF: 25.7%, VZ: 19.4%) between 36 functional groups of frugivores and 152 plant genera and, while we report significant heterogeneity in fruit resource use among broad frugivore guilds within each forest type, recursive partitioning analysis failed to clearly match differences in fruit selection to fruit traits. The annual flood pulse in várzea forests had an overriding influence on the species turnover of both fruit resources and frugivores between the two forest types, with higher-order effects on network structure.
DiRS: On Creating Benchmark Datasets for Remote Sensing Image Interpretation

The past decade has witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images, where benchmark datasets are essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image analysis. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present some principles, i.e., diversity, richness, and scalability (called DiRS), on constructing benchmark datasets in efficient manners. Following the DiRS principles, we also provide an example on building datasets for RS image classification, i.e., Million-AID, a new large-scale benchmark dataset containing million instances for RS scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this paper will provide RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.
Cluster thinning as a tool to hasten ripening of wine grapes in the Okanagan Valley, British Columbia

Hannam, K. D. , Neilsen, G. H. , Neilsen, D. and Bowen, P. 2015. Cluster thinning as a tool to hasten ripening of wine grapes in the Okanagan Valley, British Columbia. Can. J. Plant Sci. 95: 103-113. Achieving fruit maturity can be a challenge on some Okanagan vineyards in some years. Cluster thinning is widely used to hasten ripening, but may not be effective on sites with balanced crop loads. In a Merlot vineyard in Summerland, BC, the effects of cluster thinning on juice soluble solids (an indicator of fruit maturity), yield and vine growth were examined between 2008 and 2011 across a range of treatments that manipulated the frequency and quantity of applied irrigation water. Cluster thinning increased juice soluble solids in 2 out of 3 study years and consistently increased cluster weights, but had few effects on juice pH, titratable acidity or yield. In 2 of 3 yr, correlation analyses showed that cluster thinning was most effective at improving the maturity of fruit with low soluble solids. Irrigation treatments did not have a consistent effect on juice composition but year-to-year variability was significant. Response ratios calculated from values reported in the literature show that cluster thinning in this region causes small but inconsistent improvements in juice soluble solids across a range of crop loads.
The Nearest Discovered Black Hole Is Likely Not in a Triple Configuration

HR 6819 was recently claimed to be a hierarchical triple system of a Be star in a wide orbit around an inner binary system of a black hole (BH) and a B III type star. We argue that this system is unlikely to be a hierarchical triple for three reasons. (i) Given that this system is discovered in a magnitude-limited Bright Star Catalog, the expected number of such systems in the Milky Way (MW) amounts to about 104, while the estimate for the MW budget for such systems is between 102 and 103 systems under generous assumptions. Such a large gap cannot be reconciled as it would otherwise likely overflow the MW budget for BHs. (ii) The dynamical stability of this system sets lower bounds on the orbital separation of the outer Be star, while it not being resolved by Gaia places an upper limit on its projected sky separation. We show that these two constraints would imply a narrow range for the outer orbit without resorting to geometrical fine-tuning. (iii) The triple system should have survived the stellar evolution prior to the formation of the BH in the inner binary. We perform numerical simulations starting with conservative initial conditions of this system and show that a small parameter space for BH progenitor star’s mass loss, BH natal kicks, and initial orbital separation can reproduce HR 6819. Therefore, we propose this system is a chance superposition of a Be star with a binary.
Real-time PCR assay to distinguish Phytophthora ramorum lineages using the cellulose binding elicitor lectin (CBEL) locus

Abstract Phytophthora ramorum is a pathogenic oomycete that causes sudden oak death in the Western USA and sudden larch death in the UK. Until recently, three genetically divergent clonal lineages of P. ramorum were known (EU1, NA1 and NA2), named according to the continent on which they were first detected. In 2009, a fourth lineage named EU2 was discovered in the UK. Sequencing and microsatellite genotyping revealed that the EU2 lineage is genetically distinct from all other lineages. Allele-specific oligonucleotide-PCR (ASO-PCR) assays using real-time PCR were developed in this study, allowing for the identification of the EU2 lineage. Also, a combination of ASO-PCR assays targeting the cellulose binding elicitor lectin (CBEL) locus was validated to rapidly identify all four lineages. Sequencing of the CBEL locus revealed eight single nucleotide polymorphisms (SNPs) that distinguished EU2 from the other three lineages. Two ASO-PCR assays were developed from these SNPs, providing the ability to rapidly identify EU2 individuals relative to EU1, NA1 and NA2 individuals. These new assays were combined with two existing assays targeting the same locus to allow rapid and simple identification of all four lineages. Blind tests performed on a panel of representative samples revealed diagnostic profiles unique to each lineage. These markers can be used with diseased field samples, making them well suited for routine procedures in diagnostic laboratories to identify P. ramorum.
Effects of chemical composition of wild bush tea (Athrixia phylicoides DC.) growing at locations differing in altitude, climate and edaphic factors

This study was conducted to determine the chemical composition of wild bush tea growing at locations differing in altitude, climate and edaphic factors. Samples were collected from 8 locations selected for their abundance of wild bush tea and replicated 6 times in the laboratory using a completely randomized design (CRD). The areas where bush tea samples were collected comprised of Khalavha, Louis Trichardt, Mudzidzidzi, Muhuyu, Haenertsburg, Hazyview, Barberton and Levubu, respectively. Bush tea samples were collected from the bush, and the total polyphenol content, tannin and total antioxidant contents were determined. Polyphenol content reached a maximum of 7.7 mg/100 g in bush tea samples from Haenertsburg, whereas the lowest content of 3.6 mg/100 g was recorded in Levubu. The highest polyphenol content was reached when altitude was at 1 410 m in Haenertsburg. Tannin content was the lowest (0.05 mg/100 g) in Khalavha. The highest tannin content was observed in Levubu with a maximum of 9.8 mg/100 g. The amount of total antioxidant content remained the same in all the locations at 35 µmol/ g. A positive correlation (R2 = 0.55) was observed between total polyphenol content and altitude. However, rainfall, temperature, soil macro elements and soil pH did not have any influence on total polyphenol, total tannin and total antioxidant contents. Key words: Polyphenols, tannins, antioxidants, edaphic factors, climatic factors.
Securing Smart Home Edge Devices against Compromised Cloud Servers

Smart home IoT systems often rely on cloud-based servers for communication between components. Although there exists a body of work on IoT security, most of it focuses on securing clients (i.e., IoT devices). However, cloud servers can also be compromised. Existing approaches do not typically protect smart home systems against compromised cloud servers. This paper presents FIDELIUS: a runtime system for secure cloud-based storage and communication even in the presence of compromised servers. FIDELIUS's design is tailored for smart home systems that have intermittent Internet access. In particular, it supports local control of smart home devices in the event that communication with the cloud is lost, and provides a consistency model using transactions to mitigate inconsistencies that can arise due to network partitions. We have implemented FIDELIUS, developed a smart home benchmark that uses FIDELIUS, and measured FIDELIUS's performance and power consumption. Our experiments show that compared to the commercial this http URL framework, FIDELIUS reduces more than 50% of the data communication time and increases battery life by 2X. Compared to PyORAM, an alternative (ORAM-based) oblivious storage implementation, FIDELIUS has 4-7X faster access times with 25-43X less data transferred.
Feature Alignment and Restoration for Domain Generalization and Adaptation

For domain generalization (DG) and unsupervised domain adaptation (UDA), cross domain feature alignment has been widely explored to pull the feature distributions of different domains in order to learn domain-invariant representations. However, the feature alignment is in general task-ignorant and could result in degradation of the discrimination power of the feature representation and thus hinders the high performance. In this paper, we propose a unified framework termed Feature Alignment and Restoration (FAR) to simultaneously ensure high generalization and discrimination power of the networks for effective DG and UDA. Specifically, we perform feature alignment (FA) across domains by aligning the moments of the distributions of attentively selected features to reduce their discrepancy. To ensure high discrimination, we propose a Feature Restoration (FR) operation to distill task-relevant features from the residual information and use them to compensate for the aligned features. For better disentanglement, we enforce a dual ranking entropy loss constraint in the FR step to encourage the separation of task-relevant and task-irrelevant features. Extensive experiments on multiple classification benchmarks demonstrate the high performance and strong generalization of our FAR framework for both domain generalization and unsupervised domain adaptation.
Verifying Accountability for Unbounded Sets of Participants

Little can be achieved in the design of security protocols without trusting at least some participants. This trust should be justified or, at the very least, subject to examination. One way to strengthen trustworthiness is to hold parties accountable for their actions, as this provides a strong incentive to refrain from malicious behavior. This has led to an increased interest in accountability in the design of security protocols.In this work, we combine the accountability definition of Künnemann, Esiyok, and Backes [21] with the notion of case tests to extend its applicability to protocols with unbounded sets of participants. We propose a general construction of verdict functions and a set of verification conditions that achieve soundness and completeness.Expressing the verification conditions in terms of trace properties allows us to extend TAMARIN—a protocol verification tool— with the ability to analyze and verify accountability properties in a highly automated way. In contrast to prior work, our approach is significantly more flexible and applicable to a wider range of protocols.