Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiparty Quantum Key Agreement That is Secure Against Collusive Attacks

—Quantum key agreement enables remote users to fairly establish a secure shared key based on their private inputs. In the circular-type multiparty quantum key agreement protocol, two or more malicious participants can collude together to steal the private inputs of honest participants or to generate the final key alone. In this paper, we focus on a powerful collusive attack strategy in which two malicious participants in particular positions, can learn sensitive information or generate the final key alone without revealing their malicious behavior. Most of the proposed circular-type multiparty quantum key agreement protocols are not secure against this particular collusive attack strategy. As an example, we analyze the security of a recently proposed multiparty key agreement protocol to show the vulnerability of existing circular-type multiparty quantum key agreement protocols against this collusive attack. We then design a general secure multiparty key agreement model that would remove this vulnerability from such circular-type key agreement protocols and describe the necessary steps to implement this model. Our model is general and does not depend on the specific physical implementation of quantum key agreement.
A Hybrid Approach Combining Control Theory and AI for Engineering Self-Adaptive Systems

Control theoretical techniques have been successfully adopted as methods for self-adaptive systems design to provide formal guarantees about the effectiveness and robustness of adaptation mechanisms. However, the computational effort to obtain guarantees poses severe constraints when it comes to dynamic adaptation. In order to solve these limitations, in this paper, we propose a hybrid approach combining software engineering, control theory, and AI to design for software self-adaptation. Our solution proposes a hierarchical and dynamic system manager with performance tuning. Due to the gap between high-level requirements specification and the internal knob behavior of the managed system, a hierarchically composed components architecture seek the separation of concerns towards a dynamic solution. Therefore, a two-layered adaptive manager was designed to satisfy the software requirements with parameters optimization through regression analysis and evolutionary meta-heuristic. The optimization relies on the collection and processing of performance, effectiveness, and robustness metrics w.r. t control theoretical metrics at the offline and online stages. We evaluate our work with a prototype of the Body Sensor Network (BSN) in the healthcare domain, which is largely used as a demonstrator by the community. The BSN was implemented under the Robot Operating System (ROS) architecture, and concerns about the system dependability are taken as adaptation goals. Our results reinforce the necessity of performing well on such a safety-critical domain and contribute with substantial evidence on how hybrid approaches that combine control and AI-based techniques for engineering self-adaptive systems can provide effective adaptation.
Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling

As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has naturally arisen to cope with this data scarcity problem. In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling. Our model first learns the general pattern of slot entities by detecting whether the tokens are slot entities or not. It then predicts the specific types for the slot entities. In addition, we propose a template regularization approach to improve the adaptation robustness by regularizing the representation of utterances based on utterance templates. Experimental results show that our model significantly outperforms state-of-the-art approaches in slot filling. Furthermore, our model can also be applied to the cross-domain named entity recognition task, and it achieves better adaptation performance than other existing baselines. The code is available at https://github.com/zliucr/coach.
Hybrid Combining of Directional Antennas for Periodic Broadcast V2V Communication

A hybrid analog-digital combiner for broadcast vehicular communication is proposed. It has an analog part that does not require any channel state information or feedback from the receiver, and a digital part that uses maximal ratio combining (MRC). We focus on designing the analog part of the combiner to optimize the received signal strength along all azimuth angles for robust periodic vehicle-to-vehicle (V2V) communication, in a scenario of one dominant component between the communicating vehicles (e.g., highway scenario). We show that the parameters of a previously suggested fully analog combiner solves the optimization problem of the analog part of the proposed hybrid combiner. Assuming <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> directional antennas with uniform angular separation together with the special case of a two-port receiver, we show that it is optimal to combine groups of <inline-formula> <tex-math notation="LaTeX">$\lceil {L/2}\rceil $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\lfloor {L/2}\rfloor $ </tex-math></inline-formula> antennas in analog domain and feed the output of each group to one digital port. This is shown to be optimal under a sufficient condition on the sidelobes level of the directional antennas. Moreover, we derive a performance bound for the hybrid combiner to guide the choice of antennas needed to meet the reliability requirements of the V2V communication links.
Elastohydrodynamics of a soft coating under fluid-mediated loading by a spherical probe

Motion of an object near a soft wall with intervening fluid is a canonical problem in elastohydrodynamics, finding presence in subjects spanning biology to tribology. Particularly, motion of a sphere towards a soft substrate with intervening fluid is often encountered in the context of scanning probe microscopy. While there have been fundamental theoretical studies on this setup, they have focussed on specific applications and thus enforced suitable simplifications. Here we present a versatile semi-analytical framework for studying the elastohydrodynamics of axisymmetric loading of a rigid sphere near a soft elastic substrate coated on a rigid platform mediated by an aqueous electrolytic solution. Three loading modes are considered - approach, recession and oscillatory. The framework incorporates - large oscillation frequency and amplitude, two-way coupling between pressure and substrate deformation, and presence of DLVO forces. From computations using the framework, we gain insights on the effects of DLVO forces, substrate thickness, substrate material compressibility (quantified by Poisson's ratio) and high oscillation frequency for different physical setups encountered in SPM and the likes. We list some key observations. A substrate that is thicker and more compressible allows for larger deformation, i.e. is effectively softer. Presence of DLVO forces lead to magnification in force of upto two orders of magnitude and in substrate deformation of upto an order of magnitude for oscillatory loading at low frequencies and approach/recession loading at low speed. For oscillatory loading at high frequencies, DLVO forces do not appreciably affect the force and deflection behaviour of the system. Having demonstrated the versatility and utility of our framework, we expect it to evolve into a diverse and useful tool for solving problems of soft-lubrication.
Temporal concatenation for Markov decision processes

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.
Using B cell receptor lineage structures to predict affinity

We are frequently faced with a large collection of antibodies, and want to select those with highest affinity for their cognate antigen. When developing a first-line therapeutic for a novel pathogen, for instance, we might look for such antibodies in patients that have recovered. There exist effective experimental methods of accomplishing this, such as cell sorting and baiting; how-ever they are time consuming and expensive. Next generation sequencing of B cell receptor (BCR) repertoires offers an additional source of sequences that could be tapped if we had a reliable method of selecting those coding for the best antibodies. In this paper we introduce a method that uses evolutionary information from the family of related sequences that share a naive ancestor to predict the affinity of each resulting antibody for its antigen. When combined with information on the identity of the antigen, this method should provide a source of effective new antibodies. We also introduce a method for a related task: given an antibody of interest and its inferred ancestral lineage, which branches in the tree are likely to harbor key affinity-increasing mutations? These methods are implemented as part of continuing development of the partis BCR inference package, available at https://github.com/psathyrella/partis. Comments. Please post comments or questions on this paper as new issues at https://git.io/Jvxkn.
Deep learning for smart fish farming: applications, opportunities and challenges

With the rapid emergence of deep learning (DL) technology, it has been successfully used in various fields including aquaculture. This change can create new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on the applications of DL in aquaculture, including live fish identification, species classification, behavioral analysis, feeding decision-making, size or biomass estimation, water quality prediction. In addition, the technical details of DL methods applied to smart fish farming are also analyzed, including data, algorithms, computing power, and performance. The results of this review show that the most significant contribution of DL is the ability to automatically extract features. However, challenges still exist; DL is still in an era of weak artificial intelligence. A large number of labeled data are needed for training, which has become a bottleneck restricting further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs in the handling of complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for the implementation of smart fish farming.
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.
Finding Black Cat in a Coal Cellar - Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments

Diversity in content and open-ended questions are inherent in complex assignments across online graduate programs. The natural scale of these programs poses a variety of challenges across both peer and expert feedback including rogue reviews. While the identification of relevant content and associating it to predefined rubrics would simplify and improve the grading process, the research to date is still in a nascent stage. As such in this paper we aim to quantify the effectiveness of supervised and unsupervised approaches for the task for keyphrase extraction and generic/specific keyphrase-rubric relationship extraction. Through this study, we find that (i) unsupervised MultiPartiteRank produces the best result for keyphrase extraction (ii) supervised SVM classifier with BERT features that offer the best performance for both generic and specific keyphrase-rubric relationship classification. We finally present a comprehensive analysis and derive useful observations for those interested in these tasks for the future. The source code is released in \url{this https URL}.
Analyse physiologique et moleculaire du mutant procuste1 d'arabidopsis thaliana affecte dans l'elongation cellulaire

Le mecanisme de l'elongation cellulaire joue un role fondamental dans la croissance des plantes. L'hypocotyle d'arabidopsis thaliana est un organe d'origine embryonnaire dont la croissance, apres la germination est due essentiellement a un phenomene d'elongation cellulaire. A l'obscurite, cet organe peut subir une croissance importante et constitue donc un modele d'etude ideal pour comprendre les mecanismes qui regissent le processus de l'elongation cellulaire. Ce travail de these s'inscrit dans une approche physiologique et moleculaire et a pour objet l'etude des mutants procuste1 (prc1), caracterises a l'obscurite par un hypocotyle environ quatre fois plus court que celui du type sauvage. Une analyse biochimique et par ftir (spectroscopie avec transformee de fourier) de la composition des parois a permis de mettre en evidence que les parois des mutants prc1 sont deficientes en cellulose et que cette deficience semble etre compensee par un taux plus eleve de pectines et d'hemicelluloses. D'autre part, l'utilisation de plusieurs techniques de microscopie a revele que dans des coupes d'hypocotyles des mutants prc1 etioles se trouvent de nombreuses parois incompletes, ce qui n'est jamais observe dans les coupes du type sauvage. L'observation d'embryons mutants prc1 a permis d'observer que les parois de l'hypocotyle sont intactes a ce stade du developpement, suggerant que les mutants prc1 ne sont pas affectes dans le mecanisme de la cytokinese. Afin de mieux comprendre le role du gene prc1 dans l'elongation cellulaire, celui-ci a ete isole par clonage positionnel. La cartographie genetique de la mutation a permis de positionner la mutation prc1 dans une region d'environ 150 kb en bas du chromosome 5. Cette region a ete sous-clonee dans des vecteurs binaires, et un fragment de 8,9 kb capable de restaurer le phenotype sauvage dans les mutants prc1 a ete identifie. Cette region porte deux genes candidats, dont l'un code une proteine de fonction inconnue et l'autre une proteine similaire a des transporteurs de type abc. Des experiences ulterieures doivent permettre de determiner lequel des deux genes candidats correspond au gene prc1.
Bacteriophages to Control Campylobacter in Commercially Farmed Broiler Chickens, in Australia

This study describes the development and use of bacteriophage cocktails to control Campylobacter in broiler chickens, in a commercial setting, in Queensland Australia, following the birds from farm to the processing plant. The components of the bacteriophage cocktails were selected to be effective against the maximum number of Campylobacter jejuni and Campylobacter coli isolates encountered on SE Queensland farms. Farms were identified that had suitable Campylobacter target populations and phage were undetectable 1 week prior to the intended treatment. Cocktails of phages were administered at 47 days of age. Groups of study birds were slaughtered the following day, on-farm, at the end of flock transport to the plant, and at processing (approximately 28 h post-treatment). On Farm A, the phage treatment significantly reduced Campylobacter levels in the ceca at the farm in the range of 1–3 log10 CFU/g (p = 0.007), compared to mock treated controls. However, individual birds sampled on farm (1/10) or following transport (2/10) exhibited high cecal Campylobacter counts with low phage titers, suggesting that treatment periods > 24 h may be required to ensure phage replication for effective biocontrol in vivo. At the time of the trial the control birds in Farm B were phage positive despite having been negative one week earlier. There was no significant difference in the cecal Campylobacter counts between the treatment and control groups following treatment but a fall of 1.7 log10 CFU/g was observed from that determined from birds collected the previous week (p = 0.0004). Campylobacter isolates from both farms retained sensitivity to the treatment phages. These trials demonstrated bacteriophages sourced from Queensland farms have the potential to reduce intestinal Campylobacter levels in market ready broiler chickens.
Deep Neural Networks can Predict Incident Atrial Fibrillation from the 12-lead Electrocardiogram and may help Prevent Associated Strokes

Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new onset AF could be predicted, targeted population screening could be used to find it early. We hypothesized that a deep neural network could predict new onset AF from the resting 12-lead electrocardiogram (ECG) and that this prediction may help prevent AF-related stroke. Methods: We used 1.6M resting 12-lead ECG voltage-time traces from 430k patients collected from 1984-2019 in this study. Deep neural networks were trained to predict new onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC). We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs prior to 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We used standard metrics to explore different prediction thresholds for the model and also calculated how many AF-related strokes might be potentially prevented. Results: The AUROC and AUPRC were 0.83 and 0.21, respectively, for predicting new onset AF within 1 year of an ECG. Adding age and sex improved the AUROC to 0.85 and the AUPRC to 0.23. The hazard ratio for the predicted high- vs. low-risk groups over a 30-year span was 7.2 [95% confidence interval: 6.9 - 7.6]. In a simulated deployment scenario, using the F2 score to select the risk prediction threshold, the model predicted new onset AF at 1 year with a sensitivity of 69%, specificity of 81%, and positive predictive value (PPV) of 12%. This model correctly predicted new onset AF in 62% of all patients that experienced an AF-related stroke within 3 years of the ECG. Conclusions: Deep learning can predict new onset AF from the 12-lead ECG in patients with no prior history of AF. This prediction may prove useful in preventing AF-related strokes.
EmptyHeaded

There are two types of high-performance graph processing engines: low- and high-level engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures and computation models but require users to write low-level imperative code, hence ensuring that efficiency is the burden of the user. In high-level engines, users write in query languages like datalog (SociaLite) or SQL (Grail). High-level engines are easier to use but are orders of magnitude slower than the low-level graph engines. We present EmptyHeaded, a high-level engine that supports a rich datalog-like query language and achieves performance comparable to that of low-level engines. At the core of EmptyHeaded’s design is a new class of join algorithms that satisfy strong theoretical guarantees, but have thus far not achieved performance comparable to that of specialized graph processing engines. To achieve high performance, EmptyHeaded introduces a new join engine architecture, including a novel query optimizer and execution engine that leverage single-instruction multiple data (SIMD) parallelism. With this architecture, EmptyHeaded outperforms high-level approaches by up to three orders of magnitude on graph pattern queries, PageRank, and Single-Source Shortest Paths (SSSP) and is an order of magnitude faster than many low-level baselines. We validate that EmptyHeaded competes with the best-of-breed low-level engine (Galois), achieving comparable performance on PageRank and at most 3× worse performance on SSSP. Finally, we show that the EmptyHeaded design can easily be extended to accommodate a standard resource description framework (RDF) workload, the LUBM benchmark. On the LUBM benchmark, we show that EmptyHeaded can compete with and sometimes outperform two high-level, but specialized RDF baselines (TripleBit and RDF-3X), while outperforming MonetDB by up to three orders of magnitude and LogicBlox by up to two orders of magnitude.
AIM

In this article, we propose Aggregation-in-Memory (AIM), a new processing-in-memory system designed for energy efficiency and near-term adoption. In order to efficiently perform aggregation, we implement simple aggregation operations in main memory and develop a locality-adaptive host architecture for in-memory aggregation, called cache-conscious aggregation. Through this, AIM executes aggregation at the most energy-efficient location among all levels of the memory hierarchy. Moreover, AIM minimally changes existing sequential programming models and provides fully automated compiler toolchain, thereby allowing unmodified legacy software to use AIM. Evaluations show that AIM greatly improves the energy efficiency of main memory and the system performance.
ESTIMA

This article presents estima, an easy-to-use tool for extrapolating the scalability of in-memory applications. estima is designed to perform a simple yet important task: Given the performance of an application on a small machine with a handful of cores, estima extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying estima is the use of stalled cycles (e.g., cycles that the processor spends waiting for missed cache line fetches or busy locks). estima measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. estima can be effectively used to predict the scalability of in-memory applications for bigger execution machines. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation shows the effectiveness of estima on a large number of in-memory benchmarks.