Miracle-dz/UniFAR-FTUs huggingface dataset:
BaG: Behavior-Aware Group Detection in Crowded Urban Spaces Using WiFi Probes

Group detection is gaining popularity as it enables variousXzX applications ranging from marketing to urban planning. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be identified as groups. Moreover, RSSI is vulnerable to many factors like the human body attenuation and thus is unreliable in crowded scenarios. In this work, we propose a behavior-aware group detection system (BaG). BaG fuses people’s mobility information and smartphone usage behaviors. We observe that people in a group tend to have similar phone usage patterns. Those patterns could be effectively captured by the proposed feature: number of bursts (NoB). Unlike RSSI, NoB is more resilient to environmental changes as it only cares about receiving packets or not. Besides, both mobility and usage patterns correspond to the same underlying grouping information. We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously. Experimental results indicate BaG outperforms baseline approaches by <inline-formula><tex-math notation="LaTeX">$3.97\% \sim 15.79\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>. </mml:mo><mml:mn>97</mml:mn><mml:mo>%</mml:mo><mml:mo>∼</mml:mo><mml:mn>15</mml:mn><mml:mo>. </mml:mo><mml:mn>79</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="shen-ieq1-2999491.gif"/></alternatives></inline-formula> in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness.
Flexible Framework for Elasticity in Cloud Computing

Recently, cloud computing has been gaining more popularity and has received a great deal of attention from both industrial and academic worlds. Industries and application providers have moved or plan to move to clouds in order to focus on their core business. This frees them from the burden and cost of managing their physical servers in local data center infrastructures. However, the main factor motivating the use of cloud computing is its ability to provide resources according to the customer’s needs or what is referred to as elastic provisioning and de-provisioning. Therefore, elasticity is one of the key features in cloud computing that dynamically adjusts the amount of allocated resources to meet changes in workload demands. The workload of cloud applications usually varies drastically over time and hence maintaining sufficient resources to meet peak requirements can be costly, and will increase the application provider’s functional cost. Conversely, if providers cut the costs by maintaining only minimum computing resources, there will not be sufficient resources to meet peak requirements and cause bad performance, violating Service Level Agreement (SLA). Therefore, adapting cloud applications during their execution according to demand variation is a challenging task. In addition, cloud elasticity is diverse and heterogeneous because it encompasses different approaches, policies, purposes, and applications. Furthermore, elasticity can be applied at the infrastructure level or application level. The infrastructure is powered by a certain virtualization technology such as VMware, Xen, containers or a provider-specific virtualization platform. We are interested in investigating: How to overcome the problem of over-provisioning and under-provisioning? How to guaranty resource availability? How to overcome the problems of heterogeneity and resource granularity? How to standardize, unify elasticity solutions and model its diversity at a high level of abstraction to manage its different aspects? In this thesis, we solved such challenges and we investigated all the aspects of elasticity to manage efficiently the resources provisioning and de-provisioning in cloud computing. It extended the state-of-the-art by making the following three contributions. Firstly, an up-to-date state-of-the-art of the cloud elasticity which reviews different works related to elasticity for both Virtual Machines (VMs) and containers. Secondly, ElasticDocker, an approach to manage container elasticity including vertical elasticity, live migration, and elasticity combination between different virtualization techniques. Thirdly, Model-Driven Elasticity Management with OCCI (MoDEMO), a new unified, standard-based, model-driven, highly extensible, highly reconfigurable elasticity management framework that supports multiple elasticity policies, both vertical and horizontal elasticities, different virtualization techniques and multiple cloud providers.
Engineering and biocatalytic applications of methylaspartate ammonia lyase

Nieuwe milieuvriendelijke syntheseroute voor niet-natuurlijke aminozuren Het onderzoek van Hans Raj heeft een nieuwe, schone en milieuvriendelijke syntheseroute opgeleverd voor verschillende niet-natuurlijke aminozuren die met de traditionele chemische methoden moeilijk zijn te synthetiseren. Raj heeft hiertoe het enzym methylaspartaat ammonia-lyases (MAL), dat de synthese van aminozuren kan faciliteren, zo aangepast dat het bij meer reacties als katalysator kan worden ingezet. Deze nieuwe biokatalytische methodologie biedt interessante mogelijkheden voor de ‘groene’, milieuvriendelijke synthese van een groot scala aan gesubstitueerde asparaginezuren, wat waardevolle stoffen zijn voor neurobiologisch onderzoek en chirale uitgangsstoffen voor farma- en neutriceutica. Aminozuren zijn belangrijke stoffen voor biologisch onderzoek en interessante uitgangsstoffen voor de productie van lage-calorie zoetstoffen en medicijnen. Tot op heden is de synthese van optisch zuivere aminozuren lastig en tijdrovend met behulp van traditionele, organisch chemische methoden. Een belangrijk, competitief alternatief voor de synthese van optisch zuivere aminozuren wordt geboden door de toepassing van enzymen, zoals MAL. In zijn natuurlijke vorm katalyseert MAL de additie van ammonia aan 2-methylfumaarzuur, wat 3-methylasparaginezuur als product oplevert. Helaas vertoont MAL geen enkele katalytische activiteit met niet-natuurlijke substraten zoals grote alkylamines, complexe aryloxyfumaraten en monocarbonzuren zodat het ongeschikt is voor biotechnologische toepassingen. In zijn proefschrift beschrijft Raj het onderzoek naar het verhogen van de diastereoselectiviteit van MAL - en de hoeveelheid substraten die dit enzym accepteert - door middel van het aanbrengen van gerichte mutaties (structure-based engineering). Hij beschrijft voorbeelden van verhoging van de diastereoselectiviteit van wild-type MAL en ook toepassingen van de gemuteerde MALs in de stereoselectieve synthese van optisch zuivere asparaginezuurderivaten. Bovendien wist hij op deze manier de hoeveelheid substraten die door MAL wordt geaccepteerd succesvol te verhogen. De bruikbaarheid van deze gemuteerde MALs toonde hij vervolgens aan door ze toe te passen bij de synthese van diverse nieuwe asparaginezuurderivaten.
Overline : Emerging infections One Sentence Summary : Neutralizing antibodies prevent Zika infection

Therapies to prevent maternal Zika virus (ZIKV) infection and its subsequent fetal developmental complications are urgently required. We isolated three potent ZIKV-neutralizing monoclonal antibodies (nmAbs) from the plasmablasts of a ZIKV-infected patient—SMZAb1, SMZAb2, and SMZAb5—directed against two different domains of the virus. We engineered these nmAbs with Correspondence to: D.R. B. (burton@scripps.edu) or D.I. W. (dwatkins@med.miami.edu). Author contributions: D.M. M. , T. R. , G.S. , E.G.K. , R.C. D. , S.S.W. , D.R. B. and D.I. W. , planned the studies. D.M. M. , T. R. , N.B., A.S. , M.J.R. , B.B. , D.S. , K.L. , N.D. G. , V.K. B. , L.G. , N.P., M.J.G. , C.G. T.S. , H.S. M. , A.D., M.A.M., D.E. L, E.E. O. , S.J., E.A.S, A.C. , T.H. V. , and S.S.W. conducted the experiments. M.C. B. provided reagents. D.M. M. , T.R. , G.S. , E.G.K. , R.C. D. , S.W. , D.R. B. and D.I. W. , interpreted the studies. D.M. M. , T.R. , D.R. B. and D.I. W. wrote the first draft. T.R. , G.S. , S.S.W. , D.R. B. and D.I. W obtained funding. All authors reviewed, edited and approved the paper. Competing interests: Dennis Burton and Thomas Rogers are inventors on a patent application submitted by The Scripps Research Institute that covers anti-Zika monoclonal antibodies. Data and materials availability: The antibody sequences are provided in the supplementary materials. HHS Public Access Author manuscript Sci Transl Med. Author manuscript; available in PMC 2018 September 25. Published in final edited form as: Sci Transl Med. 2017 October 04; 9(410): . doi:10.1126/scitranslmed.aan8184. A uhor M anscript
KGGen: A Generative Approach for Incipient Knowledge Graph Population

Knowledge graph is becoming an indispensable resource that offers structured information for numerous AI applications. However, the knowledge graph often suffers from its incompleteness. Building a complete, high-quality knowledge graph is time-consuming and requires significant human annotation efforts. In this paper, we study the Knowledge Graph Population task, which aims at extending the scale of structured knowledge, with a special focus on reducing data preparation and annotation efforts. Previous works mainly based on discriminative methods build classifiers and verify candidate triplets that are extracted from texts, which heavily rely on the quality of data collection and co-occurrance of entities in the text. However, such methods fail to generalize on entity pairs that are not highly co-occurred, and fail to discover entity pairs that are not co-occurred at all in the given text corpus. We introduce a generative perspective to approach this task and define each relationship by learning the data distribution that embodies the core common properties for relational reasoning. A generative model KGGen is proposed, which samples from the learned data distribution for each relation and can generate triplets regardless of entity pair co-occurrence in the text corpus. To further improve the generation quality while alleviate human annotation efforts, adversarial learning is adopted to not only encourage generating high quality triplets, but also give model the ability to automatically assess the generation quality. Quantitative and qualitative experimental results conducted on two real-world generic knowledge graphs show that the proposed model KGGen generates novel and meaningful triplets with improved efficiency and less human annotation comparing with the state-of-the-art approaches.
Modeling Multiphase Contaminant Transport in Porous Media Using First-Order Mass Transfer Kinetics

This article presents the development of a multiphase contaminant transport model that incorporates non-equilibrium, first-order kinetics to the solution of mass transfer processes. The model is developed to analyze the mass transfer processes and simulate the contaminant removal by air sparging (AS) and soil vapor extraction (SVE). The model considers distinguished single-phase and multiphase domains. This capability is especially important in the case of remediation techniques that involve an advective air flux such as AS and SVE. In such systems, two domains may be considered: the advective domain, i.e., the air, and the non-advective domain, which may be either the domain outside the advective air domain but in the vicinity of the air plume, or any space or pocket inside the advective air domain that is not in direct contact with the advective air domain. The model considers the contaminant mass transfer among all phases involved: aqueous, non-aqueous, gaseous, and solid. The theoretical basis of the model is built on the concept that soil particles are surrounded by water films that isolate the gaseous and non-aqueous phase system. Therefore, the contaminant mass transfer can take place across the aqueous-solid (sorption/de-sorption), aqueous-gaseous (stripping), aqueous-NAPL (dissolution), and gaseous-NAPL (volatilization) interfaces. The governing equations for the mass transfer are solved numerically using Galerkin's finite element formulation. The numerical solution was verified against an analytical solution. The model was successfully used to analyze air sparging experimental data of toluene removal from two 350 mL reactors using airflows of 0.0432 and 0.0216 cm/s. The simulation revealed that an accurate determination of the first-order mass transfer coefficients is still needed to simulate the mass transfer processes.
Quantum Hall edge states under periodic driving: A Floquet induced chirality switch

We report on the fate of the quantum Hall effect in graphene under strong laser illumination. By using Floquet theory combined with both a low energy description and full tight-binding models, we clarify the selection rules, the quasienergy band structure, as well as their connection with the two-terminal and multi-terminal conductance in a device setup as relevant for experiments. We show that the well-known dynamical gaps that appear in the Floquet spectrum at $\pm\,\hbar\Omega/2$ lead to a switch-off of the quantum Hall edge transport for different edge terminations except for the armchair one, where two terms cancel out exactly. More interestingly, we show that near the Dirac point changing the laser polarization (circular right or circular left) controls the Hall conductance, by allowing to switch it on or off, or even by flipping its sign, thereby reversing the chirality of the edge states. This might lead to new avenues to fully control topologically protected transport.
The Solubility Product Constant of ZnO

Many attempts have been made to determine the solubility product value for zinc hydroxide, and a variety of experimental methods have been enlisted in these efforts. The results, however, are far from consistent. At 25/sup 0/C, or about room temperature, they range from 10/sup -14/ (1) to 10/sup -21/ (2). There are several reasons for this discrepancy. It has been known for some time that freshly precipitated zinc hydroxide undergoes an aging process. During this process, the precipitate changes physically and its solubility decreases. The process of aging is considered to be due to a loss of water from the precipitate. Many of the attempts to determine the solubility product of zinc hydroxide involved making concentration measurements in a solution from which zinc hydroxide had just been precipitated, and, thus, these values were metastable rather than equilibrium values. Furthermore, the preparation of pure Zn(OH)/sub 2/ is difficult. A precipitate formed by adding OH/sup -/ ions to aqueous solutions of zinc salts gives a precipitate that contains the anion of the zinc salt. Most of the attempts to measure the solubility product of Zn(OH)/sub 2/ used a method that involved the addition of alkali to aqueous solutions of a zinc salt.more » In these cases, there is legitimate doubt about the composition of the solid phase in equilibrium with the saturated solution. It is more likely to be a basic salt than Zn(OH)/sub 2/. « less
Chronus+: Minimizing Switch Buffer Size during Network Updates in Timed SDNs

Although the logically-centralized perspective is offered in Software-Defined Networks (SDNs), the data plane is still distributed in nature. Update commands sent by the centralized controller are executed asynchronously and independently in each switch. The timed SDNs enable synchronous and coordinate update operations as each update command can be triggered by a pre-defined time point. Prior work on timed update mainly focuses on how to produce a congestion-free update sequence, whereas finding a congestion-free timed update sequence may be too long to be applied in practice, even worse, such an update order may not exist. In this paper, we propose Chronus+, a timed update system that utilizes switch buffer to shorten the update time while minimizing the switch buffer size during updates. We formulate the Minimum Switch Buffer Size Problem (MSBSP) as an optimization program and show its hardness. A set of efficient algorithms is proposed to determine a timed update sequence in polynomial time. Extensive evaluations in Mininet and large-scale simulations show that Chronus+ can reduce the update time by at least 17% and the switch buffer by at least 27% compared with state-of-the-art approaches.
Investigation of Oligonucleotide Usage Variance Between SARS -related Coronaviruses and Common Cold Coronaviruses

Background: The widespread outbreak of SARS-CoV-2 has become a deal threat for human health. This new emerged virus coupled with severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS) viruses belong to coronoviridae family, which develop SARS in human being. However, prior to the emergence of virulent viruses, the coronaviruses were known as the leading causes of mild common cold. Getting more knowledge about the genome organization of different strains can conduct us how these viruses evolve and become a virulent strain. Here, we reported the difference of oligonucleotide distribution contributing in genome of two groups of coronaviruses, SARS related viruses versa common cold coronaviruses, by employing weighting algorithms approaches. Results: In this study, we found a few oligonucleotides, which significantly distinguish two viral groups. Among dinucleotide’s features, the discrepancy of TC and CC between SARS related viruses and common cold coronaviruses was quite considerable. Furthermore, CC dinucleotide was sequentially repeated in a few multinucleotide patterns including CCA, CCAC, ACCAC, and CACCAC motifs with the highest values, which also discriminated two viral groups. Conclusions: Theses remarkable oligonucleotides might point towards the existence of some particular RNA elements that might be involved in viral infectivity.
Improving Constrained Search Results By Data Melioration

The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many search applications. Its classical definition assumes that all the information needed to verify the constraints is explicitly given. However, in real-world databases, the data available on items is often partial. Hence, adequately answering constrained search queries requires the completion of this missing information. A common approach to complete missing data is to employ Machine Learning (ML)-based inference. However, such methods are naturally error-prone. More accurate data can be obtained by asking humans to complete missing information. But, as the number of items in the repository is vast, limiting human effort is crucial. To this end, we introduce the Probabilistic Constrained Search (PCS) problem, which identifies a bounded-size item-set whose data completion is likely to be highly beneficial, as these items are expected to belong to the result set of the constrained search queries in question. We prove PCS to be hard to approximate, and consequently propose a best-effort PTIME heuristic to solve it. We demonstrate the effectiveness and efficiency of our algorithm over real-world datasets and scenarios, showing that our algorithm significantly improves the result sets of constrained search queries, in terms of both utility and constraints satisfaction probability.
27 Years and 81 Million Opportunities Later: Investigating the Use of Email Encryption for an Entire University

Email is one of the main communication tools and has seen significant adoption in the past decades. However, emails are sent in plain text by default and allow attackers easy access. Users can protect their emails by end-to-end encrypting them using tools such as S/MIME or PGP.Although PGP had already been introduced in 1991, it is a commonly held belief that email encryption is a niche tool that has not seen widespread adoption to date. Previous user studies identified ample usability issues with email encryption such as key management and user interface challenges, which likely contribute to the limited success of email encryption.However, so far ground truth based on longitudinal field data is missing in the literature. Towards filling this gap, we measure the use of email encryption based on 27 years of data for 37,089 users at a large university. While attending to ethical and data privacy concerns, we were able to analyze the use of S/MIME and PGP in 81,612,595 emails.We found that only 5.46% of all users ever used S/MIME or PGP. This led to 0.06% encrypted and 2.8% signed emails. Users were more likely to use S/MIME than PGP by a factor of six. We saw that using multiple email clients had a negative impact on signing as well as encrypting emails and that only 3.36% of all emails between S/MIME users who had previously exchanged certificates were encrypted on average.Our results imply that the adoption of email encryption is indeed very low and that key management challenges negatively impact even users who have set up S/MIME or PGP previously.
Mechanisms of Preconditioning Exercise-Induced Neurovascular Protection in Stroke

Ischemic stroke is a leading cause of death and disability. Tissue plasminogen activator is the only U.S. Food and Drug Administration approved thrombolytic therapy for ischemic stroke patients till date. However, its use is limited due to increased risk of bleeding and narrow therapeutic window. Most of the preclinically tested pharmacological agents failed to be translated to the clinic. This drives the need for alternative therapeutic approaches that not only provide enhanced neuroprotection, but also reduce the risk of stroke. Physical exercise is a sort of preconditioning that provides the body with brief ischemic episodes that can protect the body from subsequent severe ischemic attacks like stroke. Physical exercise is known to improve cardiovascular health. However, its role in providing neuroprotection in stroke is not clear. Clinical observational studies showed a correlation between regular physical exercise and reduced risk and severity of ischemic stroke and better outcomes after stroke. However, the underlying mechanisms through which prestroke exercise can reduce the stroke injury and improve the outcomes are not completely understood. The purpose of this review is to: demonstrate the impact of exercise on stroke outcomes and show the potential role of exercise in stroke prevention and recovery; uncover the underlying mechanisms through which exercise reduces the neurovascular injury and improves stroke outcomes aiming to develop novel therapeutic approaches.