pwc-archive/papers-with-abstracts huggingface dataset:
Computing Execution Times with eXecution Decision Diagrams in the Presence of Out-Of-Order Resources
Authors: Zhenyu Bai, Hugues Cassé, Thomas Carle, Christine Rochange
Date: 2022-07-15 00:00:00

Worst-Case Execution Time (WCET) is a key component for the verification of critical real-time applications. Yet, even the simplest microprocessors implement pipelines with concurrently-accessed resources, such as the memory bus shared by fetch and memory stages. Although their in-order pipelines are, by nature, very deterministic, the bus can cause out-of-order accesses to the memory and, therefore, timing anomalies: local timing effects that can have global effects but that cannot be easily composed to estimate the global WCET. To cope with this situation, WCET analyses have to generate important over-estimations in order to preserve safety of the computed times or have to explicitly track all possible executions. In the latter case, the presence of out-of-order behavior leads to a combinatorial blowup of the number of pipeline states for which efficient state abstractions are difficult to design. This paper proposes instead a compact and exact representation of the timings in the pipeline, using eXecution Decision Diagram (XDD) [1]. We show how XDD can be used to model pipeline states all along the execution paths by leveraging the algebraic properties of XDD. This computational model allows to compute the exact temporal behavior at control flow graph level and is amenable to efficiently and precisely support WCET calculation in presence of out-of-order bus accesses. This model is finally experimented on the TACLe benchmark suite and we observe good performance making this approach appropriate for industrial applications.
STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability
Authors: Sandra Díaz-Seoane, Xabier Rey-Barreiro, Alejandro F. Villaverde
Date: 2022-07-15 00:00:00
Tasks: Computational Efficiency

Structural identifiability and observability are desirable properties of systems biology models. Many software toolboxes have been developed for their analysis in the last decades. STRIKE-GOLDD is a generally applicable tool that can analyse non-linear, non-rational ODE models with unknown inputs. However, this generality comes at the expense of a lower computational efficiency than other tools. Here we present STRIKE-GOLDD 4.0, which includes a new algorithm, ProbObsTest, specifically designed for the analysis of rational models. ProbObsTest is significantly faster than the FISPO algorithm - which was already available in older versions of the toolbox - when applied to computationally expensive models. An important feature of both algorithms is their ability to analyse models with unknown inputs. Thus, their coexistence in the same toolbox provides a combination of general applicability and computational efficiency. STRIKE-GOLDD 4.0 is implemented as a free and open-source Matlab toolbox with a user-friendly graphical interface. It is available under a GPLv3 license and it can be downloaded from GitHub at https://github.com/afvillaverde/strike-goldd.
The overview of the NLM-Chem BioCreative VII track: full-text chemical identification and indexing in PubMed articles
Authors: Robert Leaman, Rezarta Islamaj, Zhiyong Lu
Date: 2021-11-08 00:00:00
Proceeding: BioCreative VII Challenge Evaluation Workshop 2021 11
Tasks: Articles, Chemical Entity Recognition, Chemical Indexing, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We organized the BioCreative NLM-Chem track to call for a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: 1) Chemical Identification task, and 2) Chemical Indexing prediction task. For the Chemical Identification task, participants were expected to predict with high accuracy all chemicals mentioned in recently published full-text articles, both span (i.e., named entity recognition) and normalization (i.e., entity linking) using MeSH. For the Chemical Indexing task, participants identified which chemicals should be indexed as topics for the article's topic terms in the NLM article and indexing, i.e., appear in the listing of MeSH terms for the document.
Queueing games with an endogenous number of machines
Authors: Ata Atay, Christian Trudeau
Date: 2022-07-14 00:00:00

This paper studies queueing problems with an endogenous number of machines with and without an initial queue, the novelty being that coalitions not only choose how to queue, but also on how many machines. For a given problem, agents can (de)activate as many machines as they want, at a cost. After minimizing the total cost (processing costs and machine costs), we use a game theoretical approach to share to proceeds of this cooperation, and study the existence of stable allocations. First, we study queueing problems with an endogenous number of machines, and examine how to share the total cost. We provide an upper bound and a lower bound on the cost of a machine to guarantee the non-emptiness of the core (the set of stable allocations). Next, we study requeueing problems with an endogenous number of machines, where there is an existing queue. We examine how to share the cost savings compared to the initial situation, when optimally requeueing/changing the number of machines. Although, in general, stable allocation may not exist, we guarantee the existence of stable allocations when all machines are considered public goods, and we start with an initial schedule that might not have the optimal number of machines, but in which agents with large waiting costs are processed first.
COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning
Authors: Duowei Li, Jianping Wu, Feng Zhu, Tianyi Chen, Yiik Diew Wong
Date: 2022-07-01 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Fairness

Platooning and coordination are two implementation strategies that are frequently proposed for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections instead of using conventional traffic signals. However, few studies have attempted to integrate both strategies to better facilitate the CAV control at signal-free intersections. To this end, this study proposes a hierarchical control model, named COOR-PLT, to coordinate adaptive CAV platoons at a signal-free intersection based on deep reinforcement learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a centralized control strategy to form adaptive platoons. The optimal size of each platoon is determined by considering multiple objectives (i.e., efficiency, fairness and energy saving). The second layer employs a decentralized control strategy to coordinate multiple platoons passing through the intersection. Each platoon is labeled with coordinated status or independent status, upon which its passing priority is determined. As an efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon sizes and passing priorities respectively in the two layers. The model is validated and examined on the simulator Simulation of Urban Mobility (SUMO). The simulation results demonstrate that the model is able to: (1) achieve satisfactory convergence performances; (2) adaptively determine platoon size in response to varying traffic conditions; and (3) completely avoid deadlocks at the intersection. By comparison with other control methods, the model manifests its superiority of adopting adaptive platooning and DRL-based coordination strategies. Also, the model outperforms several state-of-the-art methods on reducing travel time and fuel consumption in different traffic conditions.
Origin of life from a maker's perspective -- focus on protocellular compartments in bottom-up synthetic biology
Authors: Ivan Ivanov, Stoyan K. Smoukov, Ehsan Nourafkan, Katharina Landfester, Petra Schwille
Date: 2022-07-14 00:00:00

The origin of life is shrouded in mystery, with few surviving clues, obscured by evolutionary competition. Previous reviews have touched on the complementary approaches of top-down and bottom-up synthetic biology to augment our understanding of living systems. Here we point out the synergies between these fields, especially between bottom-up synthetic biology and origin of life research. We explore recent progress made in artificial cell compartmentation in line with the crowded cell, its metabolism, as well as cycles of growth and division, and how those efforts are starting to be combined. Though the complexity of current life is among its most striking characteristics, none of life's essential features require it, and they are unlikely to have emerged thus complex from the beginning. Rather than recovering the one true origin lost in time, current research converges towards reproducing the emergence of minimal life, by teasing out how complexity and evolution may arise from a set of essential components.
The DKU-OPPO System for the 2022 Spoofing-Aware Speaker Verification Challenge
Authors: Xingming Wang, Xiaoyi Qin, Yikang Wang, Yunfei Xu, Ming Li
Date: 2022-07-15 00:00:00
Tasks: Speaker Verification

This paper describes our DKU-OPPO system for the 2022 Spoofing-Aware Speaker Verification (SASV) Challenge. First, we split the joint task into speaker verification (SV) and spoofing countermeasure (CM), these two tasks which are optimized separately. For ASV systems, four state-of-the-art methods are employed. For CM systems, we propose two methods on top of the challenge baseline to further improve the performance, namely Embedding Random Sampling Augmentation (ERSA) and One-Class Confusion Loss(OCCL). Second, we also explore whether SV embedding could help improve CM system performance. We observe a dramatic performance degradation of existing CM systems on the domain-mismatched Voxceleb2 dataset. Third, we compare different fusion strategies, including parallel score fusion and sequential cascaded systems. Compared to the 1.71% SASV-EER baseline, our submitted cascaded system obtains a 0.21% SASV-EER on the challenge official evaluation set.
PoLyScriber: Integrated Fine-tuning of Extractor and Lyrics Transcriber for Polyphonic Music
Authors: Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Date: 2022-07-15 00:00:00

Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility. Typically, lyrics transcription can be performed by a two-step pipeline, i.e. a singing vocal extraction front end, followed by a lyrics transcriber back end, where the front end and back end are trained separately. Such a two-step pipeline suffers from both imperfect vocal extraction and mismatch between front end and back end. In this work, we propose a novel end-to-end integrated fine-tuning framework, that we call PoLyScriber, to globally optimize the vocal extractor front end and lyrics transcriber back end for lyrics transcription in polyphonic music. The experimental results show that our proposed PoLyScriber achieves substantial improvements over the existing approaches on publicly available test datasets.
Design of conformal antenna for aircraft application
Authors: Laxminarayen N V, Lokeshwaran G, Murugan K S, Jothilakshmi P
Date: 2022-07-15 00:00:00

The most challenging thing in the real world is communicating with aircraft, even though several communication technologies have been adopted for tracking and monitoring the aircraft there is no cent per cent efficiency, for the implementation of a conformal antenna for transmission and reception of the signal is preferred. In radio communication and avionics, a conformal antenna or conformal array is a flat radio antenna which is designed to conform to or follow some prescribed shape, for example, a curved conformal antenna is designed and is mounted on or embedded in a curved surface. The conformal antenna is a collection of a large number of smaller antennas (PAA) each one is connected to a phase shifter. The phased array antenna will have high directivity in the desired application. Conformal arrays are typically limited to high frequencies in the UHF or microwave range, where the wavelength of the waves is small enough that small antennas can be used. The main objective of this project is to embed this conformal antenna on the surface of the aircraft with increased gain. To implement the above-mentioned problem we are using CST microwave software. Conventional methods now used in aircraft are done by using the conformal antenna to save space and even military applications to be anonymous. The antenna stands to be an interface between the transmitter and the receiver. By working on the software and hardware features of antennas we can develop an antenna with better gain. By adopting better gain, the antenna will be more efficient. Thus by implementing this conformal antenna on the aircraft surface with an increased gain high degree of accuracy, clarity and effective communication link can be achieved.
Energy Storage State-of-Charge Market Model
Authors: Ningkun Zheng, Xin Qin, Di wu, Gabe Murtaugh, Bolun Xu
Date: 2022-07-14 00:00:00
Tasks: model

This paper introduces and rationalizes a new model for bidding and clearing energy storage resources in wholesale energy markets. Charge and discharge bids in this model depend on the storage state-of-charge (SoC). In this setting, storage participants submit different bids for each SoC segment. The system operator monitors the storage SoC and updates their bids accordingly in market clearings. Combined with an optimal bidding design algorithm using dynamic programming, our paper shows that the SoC segment market model provides more accurate representations of the opportunity costs of energy storage compared to existing power-based bidding models. The new model also captures the inherent SoC-dependent operational characteristics of energy storage. We benchmark the SoC segment market model against an existing single-segment model in price-taker and price-influencer simulations. The simulation results show that compared to the existing power-based bidding model, the proposed model improves profits by 10-56% in the price-taker case study; the model also improves total system cost reduction from storage by around 5%, and helps reduce price volatilities in the price-influencer case study.
Passivity-based control of underactuated mechanical systems with Coulomb friction: Application to earthquake prevention
Authors: Diego Gutierrez-Oribio, Ioannis Stefanou, Franck Plestan
Date: 2022-07-14 00:00:00
Tasks: Friction

Passivity property gives a sense of energy balance. The classical definitions and theorems of passivity in dynamical systems require time invariance and locally Lipschitz functions. However, these conditions are not met in many systems. A characteristic example is nonautonomous and discontinuous systems due to presence of Coulomb friction. This paper presents an extended result for the negative feedback connection of two passive nonautonomous systems with set-valued right-hand side based on an invariance-like principle. Such extension is the base of a structural passivity-based control synthesis for underactuated mechanical systems with Coulomb friction. The first step consists in designing the control able to restore the passivity in the considered friction law, achieving stabilization of the system trajectories to a domain with zero velocities. Then, an integral action is included to improve the latter result and perform a tracking over a constant reference (regulation). At last, the control is designed considering dynamics in the actuation. These control objectives are obtained using fewer control inputs than degrees of freedom, as a result of the underactuated nature of the plant. The presented control strategy is implemented in an earthquake prevention scenario, where a mature seismogenic fault represents the considered frictional underactuated mechanical system. Simulations are performed to show how the seismic energy can be slowly dissipated by tracking a slow reference, thanks to fluid injection far from the fault, accounting also for the slow dynamics of the fluid's diffusion.
Designing, Building, and Characterizing RF Switch-based Reconfigurable Intelligent Surfaces
Authors: Marco Rossanese, Placido Mursia, Andres Garcia-Saavedra, Vincenzo Sciancalepore, Arash Asadi, Xavier Costa-Perez
Date: 2022-07-14 00:00:00

In this paper, we present our experience designing, prototyping, and empirically characterizing RF Switch-based Reconfigurable Intelligent Surfaces (RIS). Our RIS design comprises arrays of patch antennas, delay lines and programmable radio-frequency (RF) switches that enable passive 3D beamforming, i.e., without active RF components. We implement this design using PCB technology and low-cost electronic components, and thoroughly validate our prototype in a controlled environment with high spatial resolution codebooks. Finally, we make available a large dataset with a complete characterization of our RIS and present the costs associated with reproducing our design.
Non-separability of Physical Systems as a Foundation of Consciousness
Authors: Anton Arkhipov
Date: 2022-06-28 00:00:00

A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and feedforward systems have zero consciousness, whereas most systems of interacting particles appear to have low non-separability and consciousness. By contrast, brain circuits exhibit high complexity and weak but tightly coordinated interactions, which appear to support high non-separability and therefore high amount of consciousness. The hypothesis applies to both classical and quantum cases, and we highlight the formalism employing the Wigner function (which in the classical limit becomes the Liouville density function) as a potentially fruitful framework for characterizing non-separability and, thus, the amount of consciousness in a system. The hypothesis appears to be consistent with both the Integrated Information Theory and the Orchestrated Objective Reduction Theory and may help reconcile the two. It offers a natural explanation for the physical properties underlying the amount of consciousness and points to methods of estimating the amount of non-separability as promising ways of characterizing the amount of consciousness.
AlphaFold predicts the most complex protein knot and composite protein knots
Authors: Maarten A. Brems, Robert Runkel, Todd O. Yeates, Peter Virnau
Date: 2022-07-15 00:00:00

The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions for cases where the protein backbone exhibits rare topological complexity, i.e. knotting. Amongst others, we discovered a $7_1$-knot, the most topologically complex knot ever found in a protein, as well several 6-crossing composite knots comprised of two methyltransferase or carbonic anhydrase domains, each containing a simple trefoil knot. These deeply embedded composite knots occur evidently by gene duplication and interconnection of knotted dimers. Finally, we report two new five-crossing knots including the first $5_1$-knot. Our list of analyzed structures forms the basis for future experimental studies to confirm these novel knotted topologies and to explore their complex folding mechanisms.
Does Twitter know your political views? POLiTweets dataset and semi-automatic method for political leaning discovery
Authors: Joanna Baran, Michał Kajstura, Maciej Ziółkowski, Krzysztof Rajda
Date: 2022-06-14 00:00:00
Proceeding: PoliticalNLP (LREC) 2022 6

Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.
OASYS: Domain-Agnostic Automated System for Constructing Knowledge Base from Unstructured Text
Authors: Minsang Kim, Sang-hyun Je, Eunjoo Park
Date: 2022-06-29 00:00:00
Tasks: Knowledge Base Construction

In recent years, creating and managing knowledge bases have become crucial to the retail product and enterprise domains. We present an automatic knowledge base construction system that mines data from documents. This system can generate training data during the training process without human intervention. Therefore, it is domain-agnostic trainable using only the target domain text corpus and a pre-defined knowledge base. This system is called OASYS and is the first system built with the Korean language in mind. In addition, we also have constructed a new human-annotated benchmark dataset of the Korean Wikipedia corpus paired with a Korean DBpedia to aid system evaluation. The system performance results on human-annotated benchmark test dataset are meaningful and show that the generated knowledge base from OASYS trained on only auto-generated data is useful. We provide both a human-annotated test dataset and an auto-generated dataset.