pwc-archive/papers-with-abstracts huggingface dataset:
Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted Object Detection
Authors: Zhenwei He, Lei Zhang, Xinbo Gao, David Zhang
Date: 2022-12-11 00:00:00
Proceeding: International Journal of Computer Vision 2022 12
Tasks: Domain Adaptation, Knowledge Distillation, object-detection, Object Detection

Recently, the cross-domain object detection task has been raised by reducing the domain disparity and learning domain invariant features. Inspired by the image-level discrepancy dominated in object detection, we introduce a Multi-Adversarial Faster-RCNN (MAF). Our proposed MAF has two distinct contributions: (1) The Hierarchical Domain Feature Alignment (HDFA) module is introduced to minimize the image-level domain disparity, where Scale Reduction Module (SRM) reduces the feature map size without information loss and increases the training efficiency. (2) Aggregated Proposal Feature Alignment (APFA) module integrates the proposal feature and the detection results to enhance the semantic alignment, in which a weighted GRL (WGRL) layer highlights the hard-confused features rather than the easily-confused features. However, MAF only considers the domain disparity and neglects domain adaptability. As a result, the label-agnostic and inaccurate target distribution leads to the source error collapse, which is harmful to domain adaptation. Therefore, we further propose a Paradigm Teacher (PT) with knowledge distillation and formulated an extensive Paradigm Teacher MAF (PT-MAF), which has two new contributions: (1) The Paradigm Teacher (PT) overcomes source error collapse to improve the adaptability of the model. (2) The Dual-Discriminator HDFA (D2
-HDFA) improves the marginal distribution and achieves better alignment compared to HDFA. Extensive experiments on numerous benchmark datasets, including the Cityscapes, Foggy Cityscapes, Pascal VOC, Clipart, Watercolor, etc. demonstrate the superiority of our approach over SOTA methods.
Short-term and long-term memory self-attention network for segmentation of tumours in 3D medical images
Authors: Mingwei Wen, Quan Zhou, Bo Tao, Pavel Shcherbakov, Yang Xu, Xuming Zhang
Date: 2023-02-16 00:00:00
Proceeding: CAAI 2023 2
Tasks: Inductive Bias, Segmentation

Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes. The popular deep learning-based segmentation algorithms generally rely on the convolutional neural network (CNN) and Transformer. The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data. The existing hybrid CNN-Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN. To address these issues, a short-term and long-term memory self-attention network is proposed. Firstly, a distinctive self-attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN. Then, the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours. Finally, the multi-layer reconstruction blocks will predict the tumour boundaries. Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation. Compared with the most competitive method, the proposed method provides Dice (82.4% vs. 76.6%) and Hausdorff distance 95% (HD95) (10.66 vs. 11.54 mm) on the KiTS19 as well as Dice (80.2% vs. 78.4%) and HD95 (9.632 vs. 12.17 mm) on the LiTS.
Many bioinformatics programming tasks can be automated with ChatGPT
Authors: Stephen R. Piccolo, Paul Denny, Andrew Luxton-Reilly, Samuel Payne, Perry G. Ridge
Date: 2023-03-07 00:00:00

Computer programming is a fundamental tool for life scientists, allowing them to carry out many essential research tasks. However, despite a variety of educational efforts, learning to write code can be a challenging endeavor for both researchers and students in life science disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such model -- OpenAI's ChatGPT -- can successfully complete basic- to moderate-level programming tasks. On its first attempt, ChatGPT solved 139 (75.5%) of the exercises. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have important implications for life-sciences research and education. For many programming tasks, researchers no longer need to write code from scratch. Instead, machine-learning models may produce usable solutions. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public.
OntoMath${}^{\mathbf{PRO}}$ 2.0 Ontology: Updates of the Formal Model
Authors: Alexander Kirillovich, Olga Nevzorova, Evgeny Lipachev
Date: 2023-03-17 00:00:00
Tasks: Management, Math, Recommendation Systems

This paper is devoted to the problems of ontology-based mathematical knowledge management and representation. The main attention is paid to the development of a formal model for the representation of mathematical statements in the Open Linked Data cloud. The proposed model is intended for applications that extract mathematical facts from natural language mathematical texts and represent these facts as Linked Open Data. The model is used in development of a new version of the OntoMath${}^{\mathrm{PRO}}$ ontology of professional mathematics is described. OntoMath${}^{\mathrm{PRO}}$ underlies a semantic publishing platform, that takes as an input a collection of mathematical papers in LaTeX format and builds their ontology-based Linked Open Data representation. The semantic publishing platform, in turn, is a central component of OntoMath digital ecosystem, an ecosystem of ontologies, text analytics tools, and applications for mathematical knowledge management, including semantic search for mathematical formulas and a recommender system for mathematical papers. According to the new model, the ontology is organized into three layers: a foundational ontology layer, a domain ontology layer and a linguistic layer. The domain ontology layer contains language-independent math concepts. The linguistic layer provides linguistic grounding for these concepts, and the foundation ontology layer provides them with meta-ontological annotations. The concepts are organized in two main hierarchies: the hierarchy of objects and the hierarchy of reified relationships.
Dynamic Combinatorial Assignment
Authors: Thành Nguyen, Alexander Teytelboym, Shai Vardi
Date: 2023-03-24 00:00:00

We study a model of dynamic combinatorial assignment of indivisible objects without money. We introduce a new solution concept called ``dynamic approximate competitive equilibrium from equal incomes'' (DACEEI), which stipulates that markets must approximately clear in almost all time periods. A naive repeated application of approximate competitive equilibrium from equal incomes (Budish, 2011) does not yield a desirable outcome because the approximation error in market-clearing compounds quickly over time. We therefore develop a new version of the static approximate competitive equilibrium from carefully constructed random budgets which ensures that, in expectation, markets clear exactly. We then use it to design the ``online combinatorial assignment mechanism'' (OCAM) which implements a DACEEI with high probability. The OCAM is (i) group-strategyproof up to one object (ii) envy-free up to one object for almost all agents (iii) approximately market-clearing in almost all periods with high probability when the market is large and arrivals are random. Applications include refugee resettlement, daycare assignment, and airport slot allocation.
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Authors: Awni Altabaa, Bora Yongacoglu, Serdar Yüksel
Date: 2023-03-16 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning

Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL). Recent advances in MARL have focused primarily on games with finitely many states. In this work, we study multi-agent learning in stochastic games with general state spaces and an information structure in which agents do not observe each other's actions. In this context, we propose a decentralized MARL algorithm and we prove the near-optimality of its policy updates. Furthermore, we study the global policy-updating dynamics for a general class of best-reply based algorithms and derive a closed-form characterization of convergence probabilities over the joint policy space.
Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars
Authors: Bo Zhang, Boyu Jiang, Rong Zheng, XiaoPing Zhang, Jun Li, Qiang Xu
Date: 2023-03-24 00:00:00
Tasks: Template Matching

Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this paper, we address these limitations and present "Pi-ViMo", a non-contact Physiology-inspired Robust Vital Sign Monitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar's field of view. We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs.
Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices
Authors: Anu Jagannath, Zackary Kane, Jithin Jagannath
Date: 2023-03-15 00:00:00

RF fingerprinting is emerging as a physical layer security scheme to identify illegitimate and/or unauthorized emitters sharing the RF spectrum. However, due to the lack of publicly accessible real-world datasets, most research focuses on generating synthetic waveforms with software-defined radios (SDRs) which are not suited for practical deployment settings. On other hand, the limited datasets that are available focus only on chipsets that generate only one kind of waveform. Commercial off-the-shelf (COTS) combo chipsets that support two wireless standards (for example WiFi and Bluetooth) over a shared dual-band antenna such as those found in laptops, adapters, wireless chargers, Raspberry Pis, among others are becoming ubiquitous in the IoT realm. Hence, to keep up with the modern IoT environment, there is a pressing need for real-world open datasets capturing emissions from these combo chipsets transmitting heterogeneous communication protocols. To this end, we capture the first known emissions from the COTS IoT chipsets transmitting WiFi and Bluetooth under two different time frames. The different time frames are essential to rigorously evaluate the generalization capability of the models. To ensure widespread use, each capture within the comprehensive 72 GB dataset is long enough (40 MSamples) to support diverse input tensor lengths and formats. Finally, the dataset also comprises emissions at varying signal powers to account for the feeble to high signal strength emissions as encountered in a real-world setting.
Enhancing Peak Network Traffic Prediction via Time-Series Decomposition
Authors: Tucker Stewart, Bin Yu, Anderson Nascimento, Juhua Hu
Date: 2023-03-09 00:00:00
Tasks: Prediction, Time Series, Traffic Prediction

For network administration and maintenance, it is critical to anticipate when networks will receive peak volumes of traffic so that adequate resources can be allocated to service requests made to servers. In the event that sufficient resources are not allocated to servers, they can become prone to failure and security breaches. On the contrary, we would waste a lot of resources if we always allocate the maximum amount of resources. Therefore, anticipating peak volumes in network traffic becomes an important problem. However, popular forecasting models such as Autoregressive Integrated Moving Average (ARIMA) forecast time-series data generally, thus lack in predicting peak volumes in these time-series. More than often, a time-series is a combination of different features, which may include but are not limited to 1) Trend, the general movement of the traffic volume, 2) Seasonality, the patterns repeated over some time periods (e.g. daily and monthly), and 3) Noise, the random changes in the data. Considering that the fluctuation of seasonality can be harmful for trend and peak prediction, we propose to extract seasonalities to facilitate the peak volume predictions in the time domain. The experiments on both synthetic and real network traffic data demonstrate the effectiveness of the proposed method.
Continuity of Thresholded Mode-Switched ODEs and Digital Circuit Delay Models
Authors: Arman Ferdowsi, Matthias Függer, Thomas Nowak, Ulrich Schmid
Date: 2023-03-24 00:00:00

Thresholded mode-switched ODEs are restricted dynamical systems that switch ODEs depending on digital input signals only, and produce a digital output signal by thresholding some internal signal. Such systems arise in recent digital circuit delay models, where the analog signals within a gate are governed by ODEs that change depending on the digital inputs. We prove the continuity of the mapping from digital input signals to digital output signals for a large class of thresholded mode-switched ODEs. This continuity property is known to be instrumental for ensuring the faithfulness of the model w.r.t. propagating short pulses. We apply our result to several instances of such digital delay models, thereby proving them to be faithful.
Exactly Optimal Quickest Change Detection of Markov Chains
Authors: Jason J. Ford, Justin M. Kennedy, Caitlin Tompkins, Jasmin James, Aaron McFadyen
Date: 2023-03-24 00:00:00
Tasks: Change Detection

This paper establishes that an exactly optimal rule for Bayesian Quickest Change Detection (QCD) of Markov chains is a threshold test on the no change posterior. We also provide a computationally efficient scalar filter for the no change posterior whose effort is independent of the dimension of the chains. We establish that an (undesirable) weak practical super-martingale phenomenon can be exhibited by the no change posterior when the before and after chains are too close in a relative entropy rate sense. The proposed detector is examined in simulation studies.
Point Identification of LATE with Two Imperfect Instruments
Authors: Rui Wang
Date: 2023-03-24 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper characterizes point identification results of the local average treatment effect (LATE) using two imperfect instruments. The classical approach (Imbens and Angrist (1994)) establishes the identification of LATE via an instrument that satisfies exclusion, monotonicity, and independence. However, it may be challenging to find a single instrument that satisfies all these assumptions simultaneously. My paper uses two instruments but imposes weaker assumptions on both instruments. The first instrument is allowed to violate the exclusion restriction and the second instrument does not need to satisfy monotonicity. Therefore, the first instrument can affect the outcome via both direct effects and a shift in the treatment status. The direct effects can be identified via exogenous variation in the second instrument and therefore the local average treatment effect is identified. An estimator is proposed, and using Monte Carlo simulations, it is shown to perform more robustly than the instrumental variable estimand.
The State of Food Systems Worldwide: Counting Down to 2030
Authors: Kate Schneider, Jessica Fanzo, Lawrence Haddad, Mario Herrero, Jose Rosero Moncayo, Anna Herforth, Roseline Reman, Alejandro Guarin, Danielle Resnick, Namukolo Covic, Christophe Béné, Andrea Cattaneo, Nancy Aburto, Ramya Ambikapathi, Destan Aytekin, Simon Barquera, Jane Battersby-Lennard, Ty Beal, Paulina Bizzoto Molina, Carlo Cafiero, Christine Campeau, Patrick Caron, Piero Conforti, Kerstin Damerau, Michael DiGirolamo, Fabrice DeClerck, Deviana Dewi, Ismahane Elouafi, Carola Fabi, Pat Foley, Ty Frazier, Jessica Gephart, Christopher Golden, Carlos Gonzalez Fischer, Sheryl Hendriks, Maddalena Honorati, Jikun Huang, Gina Kennedy, Amos Laar, Rattan Lal, Preetmoninder Lidder, Brent Loken, Quinn Marshall, Yuta Masuda, Rebecca McLaren, Lais Miachon, Hernán Muñoz, Stella Nordhagen, Naina Qayyum, Michaela Saisana, Diana Suhardiman, Rashid Sumaila, Maximo Torrero Cullen, Francesco Tubiello, Jose-Luis Vivero-Pol, Patrick Webb, Keith Wiebe
Date: 2023-03-23 00:00:00
Tasks: Nutrition

Transforming food systems is essential to bring about a healthier, equitable, sustainable, and resilient future, including achieving global development and sustainability goals. To date, no comprehensive framework exists to track food systems transformation and their contributions to global goals. In 2021, the Food Systems Countdown to 2030 Initiative (FSCI) articulated an architecture to monitor food systems across five themes: 1 diets, nutrition, and health; 2 environment, natural resources, and production; 3 livelihoods, poverty, and equity; 4 governance; and 5 resilience and sustainability. Each theme comprises three-to-five indicator domains. This paper builds on that architecture, presenting the inclusive, consultative process used to select indicators and an application of the indicator framework using the latest available data, constructing the first global food systems baseline to track transformation. While data are available to cover most themes and domains, critical indicator gaps exist such as off-farm livelihoods, food loss and waste, and governance. Baseline results demonstrate every region or country can claim positive outcomes in some parts of food systems, but none are optimal across all domains, and some indicators are independent of national income. These results underscore the need for dedicated monitoring and transformation agendas specific to food systems. Tracking these indicators to 2030 and beyond will allow for data-driven food systems governance at all scales and increase accountability for urgently needed progress toward achieving global goals.
The effect of the Austrian-German bidding zone split on unplanned cross-border flows
Authors: Theresa Graefe
Date: 2023-03-23 00:00:00

In 2013, TSOs from the Central European Region complained to the Agency for the Cooperation of Energy Regulators because of increasing unplanned flows that were presumed to be caused by a joint German-Austrian bidding zone in the European electricity market. This paper empirically analyses the effects of the split of this bidding zone in 2018 on planned and unplanned cross-border flows between Germany, Austria, Poland, the Czech Republic, Slovakia, and Hungary. For all bidding zones, apart from the German-Austrian one, planned flows increased. Further, I find that around the policy intervention between 2017 and 2019, unplanned flows between Germany and Austria as well as for the Czech Republic and Slovakia decreased. However, for Poland increasing unplanned flows are found.
Attention-based Speech Enhancement Using Human Quality Perception Modelling
Authors: Khandokar Md. Nayem, Donald S. Williamson
Date: 2023-03-23 00:00:00
Tasks: Language Modeling, Language Modelling, Speech Enhancement

Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optimize performance of deep-learning-based speech enhancement algorithms. These objective functions, however, do not always strongly correlate with a listener's assessment of perceptual quality, so optimizing with these measures often results in poorer performance in real-world scenarios. In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech. The MOS prediction model estimates the perceptual MOS of speech quality, as assessed by human listeners, directly from the audio signal. The enhancement module also employs a quantized language model that enforces spectral constraints for better speech realism and performance. We train the model using real-world noisy speech data that has been captured in everyday environments and test it using unseen corpora. The results show that our proposed approach significantly outperforms other approaches that are optimized with objective measures, where the predicted quality scores strongly correlate with human judgments.
Interferometer response characterization algorithm for multi-aperture Fabry-Perot imaging spectrometers
Authors: Daniele Picone, Silvére Gousset, Mauro Dalla-Mura, Yann Ferrec, Etienne le-Coarer
Date: 2023-03-24 00:00:00

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based on division of aperture. These systems have the advantage of capturing snapshot acquisitions while maintaining a compact design. However, they require a careful calibration to operate properly. In this work, we present the interferometer response characterization algorithm (IRCA), a robust three-step procedure designed to characterize the transmittance response of multi-aperture imaging spectrometers based on the interferometry of Fabry-Perot. Additionally, we propose a formulation of the image formation model for such devices suitable to estimate the parameters of interest by considering the model under various regimes of finesse. The proposed algorithm processes the image output obtained from a set of monochromatic light sources and refines the results using nonlinear regression after an ad-hoc initialization. Through experimental analysis conducted on four different prototypes from the Image SPectrometer On Chip (ImSPOC) family, we validate the performance of our approach for characterization. The associated source code for this paper is available at https://github.com/danaroth83/irca.