Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Hierarchical Retrieval for Open-Domain Question Answering

Retrieval accuracy is crucial to the performance of open-domain question answering (ODQA) systems. Recent work has demonstrated that dense hierarchical retrieval (DHR), which re-trieves document candidates first and then relevant passages from the refined document set, can significantly outperform the single stage dense passage retriever (DPR). While effective, this approach requires document structure information to learn document representation and is hard to adopt to other domains without this information. Additionally, the dense retrievers tend to generalize poorly on out-of-domain data comparing with sparse retrievers such as BM25. In this paper, we propose Hybrid Hierarchical Retrieval (HHR) to address the existing limitations. Instead of relying solely on dense retrievers, we can apply sparse retriever, dense retriever, and a combination of them in both stages of document and passage retrieval. We perform extensive experiments on ODQA benchmarks and observe that our framework not only brings in-domain gains, but also generalizes better to zero-shot TriviaQA and Web Questions datasets with an average of 4.69% improvement on recall@100 over DHR. We also offer practical insights to trade off between retrieval accuracy, latency, and storage cost. The code is available on github ∗ .
Typology Guided Multilingual Position Representations: Case on Dependency Parsing

Recent multilingual models benefit from strong unified semantic representation models. However, due to conflict linguistic regularities, ignoring language-specific features during multi-lingual learning may suffer from negative transfer. In this work, we analyze the relation be-tween a language’s position space and its ty-pological characterization, and suggest deploying different position spaces for different languages. We develop a position generation network which combines prior knowledge from typology features and existing position vectors. Experiments on the multilingual dependency parsing task show that the learned position vectors exhibit meaningful hidden structures, and they can help achieving the best multilingual parsing results.
Measurements of the CKM angle $\gamma$ at LHCb

The CKM angle $\gamma$ is the least well-known angle of the Unitarity Triangle, and the only one easily accessible at tree level. Important constraints on \g are obtained from the analysis of $B^{\pm} \to D^{0} K^{\pm}$ decays, where the $D^{0}$ meson is reconstructed in the $K^+ K^-$ or $\pi^+ \pi^-$ final states; the latest world-best results using the Run 1 (2011 and 2012) and Run 2 (2015 and 2016) LHCb datasets are presented here. The measurement of $B^{\pm} \to D^{*0} K^{\pm}$ decays using a partial reconstruction method is also performed at LHCb for the first time, where both $D^{*0} \to D^{0} \pi^{0}$ and $D^{*0} \to D^{0} \gamma$ decays are considered. Both sets of results contribute to the ultimate goal of degree level precision on $\gamma$, via the exploitation of all possible channels and techniques.
Constructing Procedural Graphs with Multiple Dependency Relations: A New Dataset and Baseline

Current structured and semi-structured knowledge bases mainly focus on representing descriptive knowledge but ignore another commonsense knowledge (Procedural Knowledge). To structure the procedural knowledge, existing methods are proposed to automatically generate ﬂow graphs from procedural documents. They focus on extracting sequential dependency between sentences but neglect another two important dependencies (i.e., inclusion dependency and constraint dependency) in procedural documents. In our paper, we explore a problem of automatically generating procedural graph with multiple dependency relations to extend the ﬂow graph constructed by existing methods and propose a procedural graph construction method with syntactic information and discourse structures. A new dataset (WHPG) is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model.
Distributed Transactions at Scale in Amazon DynamoDB

NoSQL cloud database services are popular for their simple key-value operations, high availability, high scalability, and predictable performance. These characteristics are generally considered to be at odds with support for transactions that permit atomic and serializable updates to partitioned data. This paper explains how transactions were added to Amazon DynamoDB using a timestamp ordering protocol while exploiting the semantics of a key-value store to achieve low latency for both transactional and non-transactional operations. The results of experiments against a production implementation demonstrate that distributed transactions with full ACID properties can be supported without compromising on performance, availability, or scale
Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations

We extend a non-parametric Bayesian model of (Titov and Klementiev, 2011) to deal with homonymy and polysemy by leveraging distributed contextual word and phrase representations pre-trained on a large collection of unla-belled texts. Then, unsupervised semantic parsing is performed by decomposing sentences into fragments, clustering the fragments to abstract away syntactic variations of the same meaning, and predicting predicate-argument relations between the fragments. To better model the statistical dependencies between predicates and their arguments, we further conduct a hierarchical Pitman-Yor process. An improved Metropolis-Hastings merge-split sampler is proposed to speed up the mixing and convergence of Markov chains by leveraging pre-trained distributed representations. The experimental re-sults show that the models achieve better accuracy on both question-answering and relation extraction tasks.
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific natural language reasoning problems. In this study, we observe that a large language model can serve as a highly effective few-shot semantic parser. It can convert natural language sentences into a logical form that serves as input for answer set programs, a logic-based declarative knowledge representation formalism. The combination results in a robust and general system that can handle multiple question-answering tasks without requiring retraining for each new task. It only needs a few examples to guide the LLM's adaptation to a specific task, along with reusable ASP knowledge modules that can be applied to multiple tasks. We demonstrate that this method achieves state-of-the-art performance on several NLP benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN. Additionally, it successfully tackles robot planning tasks that an LLM alone fails to solve.
This paper is included in the Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation.

Datacenter applications are often structured as many inter-connected microservices, and the service mesh has become a popular approach to route RPC traﬃc among services. This paper presents ServiceRouter (SR), Meta’s global service mesh, which has been in production since 2012. SR diﬀers from publicly known service meshes in several important ways. First, SR is designed for hyperscale and currently uses millions of L7 routers to route tens of billions of requests per second across tens of thousands of services. Second, while SR adopts the common approach of using sidecar or remote proxies to route 1% of RPC requests in our ﬂeet, it employs a routing library that is directly linked into service executables to route the remaining 99% directly from clients to servers, without the extra hop of going through a proxy. This approach signiﬁcantly reduces the hardware costs of our hyperscale service mesh, saving hundreds of thousands of machines. Third, SR provides built-in support for sharded services, which account for 68% of RPC requests in our ﬂeet, whereas existing general-purpose service meshes do not support sharded services. Finally, SR introduces the concept of locality rings to simultaneously minimize RPC latency and balance load across geo-distributed datacenter regions, which, to our knowledge, has not been attempted before.
Decentralized and Stateful Serverless Computing on the Internet Computer Blockchain

The Internet Computer (IC) is a fast and efficient decentralized blockchain-based platform for the execution of general-purpose applications in the form of smart contracts. In other words, the IC service is the antithesis of current serverless computing. Instead of ephemeral, stateless functions operated by a single entity, the IC offers decentralized stateful server-less computation over untrusted, independent datacenters. De-velopers deploy stateful canisters that serve calls either to end-users or other canisters. The IC programming model is similar to serverless clouds, with applications written in modern languages such as Rust or Python, yet simpler: state is maintained automatically, without developer intervention. In this paper, we identify and address significant systems challenges to enable efficient decentralized stateful serverless computation: scalability, stateful execution through orthogonal persistence, and deterministic scheduling. We describe the design of the IC and characterize its operational data gathered over the past 1.5 years, and its performance.
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing

Task-oriented semantic parsing has drawn a lot of interest from the NLP community, and especially the voice assistant use-cases as it enables representing the meaning of user requests with arbitrarily nested semantics, including multiple intents and compound entities. SOTA models are large seq2seq transformers and require hundreds of thousands of annotated examples to be trained. However annotating such data to boot-strap new domains or languages is expensive and error-prone, especially for requests made of nested semantics. In addition large models easily break the tight latency constraints imposed in a user-facing production environment. As part of this work we explore leveraging exter-nal knowledge as a replacement for additional annotated data in order to improve model accuracy in low-resource and low-compute settings. We demonstrate that using knowledge-enhanced encoders inside seq2seq models does not result in performance gains by itself, but multitask learning to uncover entities in addition to the parse generation is a simple yet effective way of improving performance across the domains and data regimes. We show this is especially true in the low-compute low-data setting and for entity-rich domains, with relative gains up to 74.48% in some cases on the TOPv2 dataset.
Userspace Bypass: Accelerating Syscall-intensive Applications

Context switching between kernel mode and user mode often causes prominent overhead, which slows down applications with frequent system calls (or syscalls), e.g., those with high I/O demand. The overhead is further ampliﬁed by security mechanisms like Linux kernel page-table isolation (KPTI). To accelerate such applications, many efforts have been put in removing syscalls from the I/O paths, mainly by combining drivers and applications in the same space or batching syscalls. Nonetheless, such solutions require developers to refactor their applications or even update hardware, which impedes their broad adoption. In this paper, we propose another approach, userspace bypass (UB), to accelerate syscall-intensive applications, by transparently moving userspace instructions into kernel. Userspace bypass requires no modiﬁcation to userspace binaries or code and achieves full binary compatibility. Speciﬁ-cally, to avoid overhead caused by frequent syscalls, kernel identiﬁes the short userspace execution path between consecutive system calls, and converts the instructions in the path into code blocks with Software-Based Fault Isolation (SFI) guarantee. According to our evaluation, I/O micro-benchmark can be accelerated by 30.3 – 88.3%, Redis GET Requests Per Second (RPS) can be improved by 4.4 –10.8% for 1B – 4KiB data sizes, when the application is executed in a virtualized setting with KPTI turned on. The performance boost will be reduced when KPTI is turned off.
A Collaborative Composite Event Detection Approach in Wireless Sensor Network Using Fuzzy Assisted Decision System

: Recently, in Wireless Sensor Networks (WSNs), several energy efﬁcient event detection algorithms have been proposed that aim to minimize battery storage, energy consumption and to maximize the network lifetime by framing reduced fuzzy rules related to spatio-temporal properties for determining the event. Since sensors are prone to intermittent fault it would result to fault susceptible reading that generates false alarm event, which would lead to lack of conforming the event at a higher conﬁdence rate. Hence, during decision making, the conﬁdence level parameter of the sensor node is integrated by considering the spatio-temporal properties. In this paper, we demonstrate through collaborative techniques by setting hypothesis to conﬁrm the composite event collected from the neighborhood nodes later the intelligent fuzzy decision system evaluates the rules with the composition of novel parameter conﬁdent factor, which results in higher event detection accuracy. This work is implemented in MATLAB and simulations are carried out under different network scenarios. The algorithm is evaluated with various metrics such as event detection accuracy, false positive rate, error rate of the event and energy consumption. Based on the results of the simulations, we conclude that our intelligent hypothesis based on the fuzzy decision system outperform than the well-established J48 decision tree classiﬁcation algorithm
Machine translations: statistical approach with additional linguistic knowledge

In this thesis, three possible aspects of using linguistic ( i.e. morpho-syntactic) knowledge for statistical machine translation are described: the tre atm nt of syntactic differences between source and target language using source P OS tags, statistical machine translation with a small amount of bilingual training data, and automatic error anal ysis of translation output. Reorderings in the source language based on the P OS tags are systematically investigated: local reorderings of nouns and adjectives for the Spanish–E nglish language pair and long-range reorderings of verbs for the German–English language pair. Both types of reorderings result in better performance of the translation system, local reor d ring being more important for the scarce training corpora. For such corpora, strategies for achieving an acceptable tr anslation quality by applying appropriate morpho-syntactic transformations are exploite d for three language pairs: Spanish– English, German–English and Serbian–English. Very scarce task-specific corpora as well as conventional dictionaries are used as bilingual training m aterial. In addition to conventional dictionaries, the use of phrasal lexica is proposed and inve stigated. A framework for automatic analysis and classification of act u l errors in translation output based on combining existing automatic evaluation measures with linguistic information is presented. Experiments on different types of corpora and vario us language pairs show that the results of automatic error analysis correlate very well wit h the results of human evaluation. The new metrics based on analysed error categories are used for c omparison of different translation systems trained on various sizes of texts with and without mo rph -syntactic transformations. For improving the quality of a statistical machine translat ion system by the use of morphosyntactic information, the choice of the method and the sign ificance of improvements strongly depend on the language pair, the translation direction and t he nature of the corpus. Error analysis of the translation output is important in order to define weak points of the system and apply methods for improvement in the optimal way.
Coil-current effect in Kibble balances: analysis, measurement, and optimization

The Kibble balance is expected to become an important instrument in the near future for realizing the unit of mass, the kilogram, in the revised international system of units (SI). The Kibble balance assumes an equality of two magnetic profiles measured in the weighing and velocity phases. A recent study conducted in the Kibble balance group at the Bureau International des Poids et Mesures (BIPM) showed that the coil current could significantly affect the magnetic profile, which should be carefully taken into account in the Kibble balance experiment. This paper gives a deeper understanding and investigation of the effect, and discusses the magnetic profile change due to the coil current, for both the classical two-mode and the one-mode Kibble balances. The coil current effect has been theoretically and experimentally investigated based on a typical magnet design with an air gap. One important conclusion found in the one-mode Kibble balance is that the magnetic profile change measured in the velocity phase is twice the change in the weighing phase. A compensation suggestion, to minimize the profile change due to the coil current in a BIPM-type magnet, is presented.
Application of complementary split ring resonators for size reduction in patch antenna arrays

Abstract In this paper, a novel technique for reducing the dimensions of patch antennas and their arrays is presented. The procedure uses complementary split ring resonators (CSRRs) for increasing the slow-wave effect on the radiator, and therefore augmenting its electrical length, which implicates the modified antenna gets a lower physical dimension than a conventional one for a given frequency. Led by the use of CSRRs, the patches may be closer to each other, without affecting the side lobes magnitude either the directivity of the array compared to a conventional one. As an example, a 4 × 1 patch array was designed and its total area was 40% smaller compared to a conventional one, considering the same design conditions with and without CSRRs. The gain, directivity, and coupling of the prototype were not affected.
QUALITY CORPORATE FINANCIAL REPORTING AND STAKEHOLDERS INTERERST

Information, Communication and Globalization has made the preparation of reports very easy because large volume of transactions needs to be processed. Quality corporate reporting is very essential for the growth of an organization and thus development of the economy of any country. Over the years the quality of reporting has received global and heightened concern as a result of the role it plays in allowing users of financial statements make economic decisions which to a very large extent talks about the soundness and healthiness of a business firm or organization. The objective of the study was to critically examine the relationship between Quality of Corporate Reporting and stakeholder’s interest. The study adopted an ex-post facto research design. The population was 35 manufacturing companies while 10 were selected purposively for a period of 10 years making 100 firm year observations. The findings reveal that Quality Corporate reporting had a significant impact on Creditors interest (Adj R2= 0.653, F(2, 97) = 48.60, P < 0.05), it also had a significant relationship on employees interest(Adj R2= 0.0.733, F(2, 97) = 40.81, P < 0.05). The study concluded that there exists a significant relationship between Quality of Corporate Reporting and all the variables of Stakeholders interest. It was recommended that Owners and Management of businesses must ensure that a mechanism and a structure is put in place to ensure that reports prepared are of high quality that will meet the needs of the stakeholders.
A New Scheme for Considering Soil Water‐Heat Transport Coupling Based on Community Land Model: Model Description and Preliminary Validation

Land surface models (LSMs) have developed significantly over the past few decades, with the result that most LSMs can generally reproduce the characteristics of the land surface. However, LSMs fail to reproduce some details of soil water and heat transport during seasonal transition periods because they neglect the effects of interactions between water movement and heat transfer in the soil. Such effects are critical for a complete understanding of water‐heat transport within a soil thermohydraulic regime. In this study, a fully coupled water‐heat transport scheme (FCS) is incorporated into the Community Land Model (version 4.5) to replaces its original isothermal scheme, which is more complete in theory. Observational data from five sites are used to validate the performance of the FCS. The simulation results at both single‐point and global scale show that the FCS improved the simulation of soil moisture and temperature. FCS better reproduced the characteristics of drier and colder surface layers in arid regions by considering the diffusion of soil water vapor, which is a nonnegligible process in soil, especially for soil surface layers, while its effects in cold regions are generally inverse. It also accounted for the sensible heat fluxes caused by liquid water flow, which can contribute to heat transfer in both surface and deep layers. The FCS affects the estimation of surface sensible heat (SH) and latent heat (LH) and provides the details of soil heat and water transportation, which benefits to understand the inner physical process of soil water‐heat migration.
Model-Based Reinforcement Learning for Trajectory Tracking of Musculoskeletal Robots

This paper aims to solve the trajectory tracking task of the pneumatic musculoskeletal robot within a model-based reinforcement learning framework. Considering the limited sensors and short lifespan of self-made pneumatic artificial muscles, multi-task Gaussian process regression is employed for micro-data model learning and the learned model is combined with cross entropy method (CEM)-based model predictive control to plan for the optimal action online. To further compensate for the model imperfection and improve the control performance, a proportional derivative controller-like strategy is proposed to guide the search space of the original CEM solver. The effectiveness of our approach is verified on a real musculoskeletal system with one degree of freedom and the results show that only 50 s of interacting with the environment is enough for the robot to learn trajectory tracking skills from scratch.