pwc-archive/papers-with-abstracts huggingface dataset:
Water Markets as a Coping Mechanism for Climate-Induced Water Changes on the Canadian Economy: A Computable General Equilibrium Approach
Authors: Jorge Garcia-Hernandez, Roy Brouwer
Date: 2023-07-23 00:00:00

Water markets represent a policy tool that aims at finding efficient water allocations among competing users by promoting reallocations from low-value to high-value uses. In Canada, water markets have been discussed and implemented at the provincial level; however, at the national level a study about the economic benefits of its implementation is still lacking. This paper fills this void by implementing a water market in Canada and examine how water endowment shocks would affect the economy under the assumptions of general equilibrium theory. Our results show a water market would damp the economic loss in case of reductions in water endowment, but it also cuts back on the economic expansion that would follow from an increase on it. These results provide new insights on the subject and will provide a novel look and reinvigorate informed discussions on the use of water markets in Canada as a potential tool to cope with climate-induced water supply changes.
Assessing the role of small farmers and households in agriculture and the rural economy and measures to support their sustainable development
Authors: Oleg Nivievskyi, Pavlo Iavorskyi, Oleksandr Donchenko
Date: 2023-04-29 00:00:00

The Ministry of Economy has an interest and demand in exploring how to increase the set of [legally registered] small family farmers in Ukraine and to examine more in details measures that could reduce the scale of the shadow agricultural market in Ukraine. Building upon the above political economy background and demand, we will be undertaking the analysis along the two separate but not totally independents streams of analysis, i.e. sustainable small scale (family) farming development and exploring the scale and measures for reducing the shadow agricultural market in Ukraine
CNN-based automatic segmentation of Lumen & Media boundaries in IVUS images using closed polygonal chains
Authors: Pavel Sinha, Ioannis Psaromiligkos, Zeljko Zilic
Date: 2023-09-29 00:00:00
Tasks: Decoder

We propose an automatic segmentation method for lumen and media with irregular contours in IntraVascular ultra-sound (IVUS) images. In contrast to most approaches that broadly label each pixel as either lumen, media, or background, we propose to approximate the lumen and media contours by closed polygonal chains. The chain vertices are placed at fixed angles obtained by dividing the entire 360\degree~angular space into equally spaced angles, and we predict their radius using an adaptive-subband-decomposition CNN. We consider two loss functions during training. The first is a novel loss function using the Jaccard Measure (JM) to quantify the similarities between the predicted lumen and media segments and the corresponding ground-truth image segments. The second loss function is the traditional Mean Squared Error. The proposed architecture significantly reduces computational costs by replacing the popular auto-encoder structure with a simple CNN as the encoder and the decoder is reduced to simply joining the consecutive predicted points. We evaluated our network on the publicly available IVUS-Challenge-2011 dataset using two performance metrics, namely JM and Hausdorff Distance (HD). The evaluation results show that our proposed network mostly outperforms the state-of-the-art lumen and media segmentation methods.
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective
Authors: Xin Li, Yiting Lu, Zhibo Chen
Date: 2023-09-29 00:00:00
Tasks: Blind Image Quality Assessment, Domain Adaptation, Image Quality Assessment, Unsupervised Domain Adaptation

Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted to design unsupervised domain adaptation (UDA) based schemes for BIQA, which intends to eliminate the domain shift through adversarial-based feature alignment. However, the feature alignment is usually taken at the low-frequency space of features since the global average pooling operation. This ignores the transferable perception knowledge in other frequency components and causes the sub-optimal solution for the UDA of BIQA. To overcome this, from a novel frequency perspective, we propose an effective alignment strategy, i.e., Frequency Alignment (dubbed FreqAlign), to excavate the perception-oriented transferability of BIQA in the frequency space. Concretely, we study what frequency components of features are more proper for perception-oriented alignment. Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment. To achieve a stable and effective frequency selection, we further propose the frequency movement with a sliding window to find the optimal frequencies for alignment, which is composed of three strategies, i.e., warm up with pre-training, frequency movement-based selection, and perturbation-based finetuning. Extensive experiments under different domain adaptation settings of BIQA have validated the effectiveness of our proposed method. The code will be released at https://github.com/lixinustc/Openworld-IQA.
D-Band 2D MIMO FMCW Radar System Design for Indoor Wireless Sensing
Authors: Subbarao Korlapati, Reza Nikandish
Date: 2023-09-29 00:00:00

In this article, we present system design of D-band multi-input multi-output (MIMO) frequency-modulated continuous-wave (FMCW) radar for indoor wireless sensing. A uniform rectangular array (URA) of radar elements is used for 2D direction-of-arrival (DOA) estimation. The DOA estimation accuracy of the MIMO radar array in the presence of noise is evaluated using the multiple-signal classification (MUSIC) and the minimum variance distortionless response (MVDR) algorithms. We investigate different scaling scenarios for the radar receiver (RX) SNR and the transmitter (TX) output power with the target distance. The DOA estimation algorithm providing the highest accuracy and shortest simulation time is shown to depend on the size of the radar array. Specifically, for a 64-element array, the MUSIC achieves lower root-mean-square error (RMSE) compared to the MVDR across 1--10\,m indoor distances and 0--30\,dB SNR (e.g., $\rm 0.8^{\circ}$/$\rm 0.3^{\circ}$ versus $\rm 1.0^{\circ}$/$\rm 0.5^{\circ}$ at 10/20\,dB SNR and 5\,m distance) and 0.5x simulation time. For a 16-element array, the two algorithms provide comparable performance, while for a 4-element array, the MVDR outperforms the MUSIC by a large margin (e.g., $\rm 8.3^{\circ}$/$\rm 3.8^{\circ}$ versus $\rm 62.2^{\circ}$/$\rm 48.8^{\circ}$ at 10/20\,dB SNR and 5\,m distance) and 0.8x simulation time. Furthermore, the TX output power requirement of the radar array is investigated in free-space and through-wall wireless sensing scenarios, and is benchmarked by the state-of-the-art D-band on-chip radars.
Tree based Single LED Indoor Visible Light Positioning Technique
Authors: Srivathsan Chakaravarthi Narasimman, Arokiaswami Alphones
Date: 2023-09-29 00:00:00

Visible light positioning(VLP) has gained prominence as a highly accurate indoor positioning technique. Few techniques consider the practical limitations of implementing VLP systems for indoor positioning. These limitations range from having a single LED in the field of view(FoV) of the image sensor to not having enough images for training deep learning techniques. Practical implementation of indoor positioning techniques needs to leverage the ubiquity of smartphones, which is the case with VLP using complementary metal oxide semiconductor(CMOS) sensors. Images for VLP can be gathered only after the lights in question have been installed making it a cumbersome process. These limitations are addressed in the proposed technique, which uses simulated data of a single LED to train machine learning models and test them on actual images captured from a similar experimental setup. Such testing produced mean three dimensional(3D) positioning error of 2.88 centimeters while training with real images achieves accuracy of less than one centimeter compared to 6.26 centimeters of the closest competitor.
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer
Authors: Fanwen Wang, Michael Tanzer, Mengyun Qiao, Wenjia Bai, Daniel Rueckert, Guang Yang, Sonia Nielles-Vallespin
Date: 2023-09-28 00:00:00
Tasks: Decoder, Quantitative MRI

Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We propose a deep learning method that incorporates a time dependency Latent Transformer module to model relationships between parameterised time frames for improved reconstruction from undersampled data. The module, implemented as a multi-resolution sequence-to-sequence transformer, is integrated into an encoder-decoder architecture to leverage the inherent temporal correlations in relaxation processes. The presented results for accelerated T1 and T2 mapping show the model recovers maps with higher fidelity by explicit incorporation of time dynamics. This work demonstrates the importance of temporal modelling for artifact-free reconstruction in quantitative MRI.
A rigorous benchmarking of methods for SARS-CoV-2 lineage abundance estimation in wastewater
Authors: Viorel Munteanu, Victor Gordeev, Michael Saldana, Eva Aßmann, Justin Maine Su, Nicolae Drabcinski, Oksana Zlenko, Maryna Kit, Felicia Iordachi, Khooshbu Kantibhai Patel, Abdullah Al Nahid, Likhitha Chittampalli, Yidian Xu, Pavel Skums, Shelesh Agrawal, Martin Hölzer, Adam Smith, Alex Zelikovsky, Serghei Mangul
Date: 2023-09-29 00:00:00
Tasks: Benchmarking

In light of the continuous transmission and evolution of SARS-CoV-2 coupled with a significant decline in clinical testing, there is a pressing need for scalable, cost-effective, long-term, passive surveillance tools to effectively monitor viral variants circulating in the population. Wastewater genomic surveillance of SARS-CoV-2 has arrived as an alternative to clinical genomic surveillance, allowing to continuously monitor the prevalence of viral lineages in communities of various size at a fraction of the time, cost, and logistic effort and serving as an early warning system for emerging variants, critical for developed communities and especially for underserved ones. Importantly, lineage prevalence estimates obtained with this approach aren't distorted by biases related to clinical testing accessibility and participation. However, the relative performance of bioinformatics methods used to measure relative lineage abundances from wastewater sequencing data is unknown, preventing both the research community and public health authorities from making informed decisions regarding computational tool selection. Here, we perform comprehensive benchmarking of 18 bioinformatics methods for estimating the relative abundance of SARS-CoV-2 (sub)lineages in wastewater by using data from 36 in vitro mixtures of synthetic lineage and sublineage genomes. In addition, we use simulated data from 78 mixtures of lineages and sublineages co-occurring in the clinical setting with proportions mirroring their prevalence ratios observed in real data. Importantly, we investigate how the accuracy of the evaluated methods is impacted by the sequencing technology used, the associated error rate, the read length, read depth, but also by the exposure of the synthetic RNA mixtures to wastewater, with the goal of capturing the effects induced by the wastewater matrix, including RNA fragmentation and degradation.
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Authors: Declan Campbell, Sreejan Kumar, Tyler Giallanza, Jonathan D. Cohen, Thomas L. Griffiths
Date: 2023-09-29 00:00:00

Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One illustration of this is in the visual perception of geometric forms. Studies have shown a uniquely human bias toward geometric regularity, with task performance enhanced for more regular and symmetric forms compared to their geometrically irregular counterparts. Such studies conclude that this behavior implies the existence of discrete symbolic structure in human mental representations, and that replicating such behavior in neural network architectures will require mechanisms for symbolic processing. In this study, we argue that human biases towards geometric regularity can be reproduced in neural networks, without explicitly providing them with symbolic machinery, by augmenting them with an architectural constraint that enables the system to discover and manipulate relational structure. When trained with the appropriate curriculum, this model exhibits human-like biases towards symmetry and regularity in two distinct tasks involving abstract geometric reasoning. Our findings indicate that neural networks, when equipped with the necessary training objectives and architectural elements, can exhibit human-like regularity biases and generalization. This approach provides insights into the neural mechanisms underlying geometric reasoning and offers an alternative to prevailing symbolic "Language of Thought" models in this domain.
Handling missing data in Burundian sovereign bond market
Authors: Irène Irakoze, Rédempteur Ntawiratsa, David Niyukuri
Date: 2023-09-29 00:00:00
Tasks: Imputation, Missing Values, regression

Constructing an accurate yield curve is essential for evaluating financial instruments and analyzing market trends in the bond market. However, in the case of the Burundian sovereign bond market, the presence of missing data poses a significant challenge to accurately constructing the yield curve. In this paper, we explore the limitations and data availability constraints specific to the Burundian sovereign market and propose robust methodologies to effectively handle missing data. The results indicate that the Linear Regression method, and the Previous value method perform consistently well across variables, approximating a normal distribution for the error values. The non parametric Missing Value Imputation using Random Forest (miss-Forest) method performs well for coupon rates but poorly for bond prices, and the Next value method shows mixed results. Ultimately, the Linear Regression (LR) method is recommended for imputing missing data due to its ability to approximate normality and predictive capabilities. However, filling missing values with previous values has high accuracy, thus, it will be the best choice when we have less information to be able to increase accuracy for LR. This research contributes to the development of financial products, trading strategies, and overall market development in Burundi by improving our understanding of the yield curve dynamics.
Covariance matrix filtering and portfolio optimisation: the Average Oracle vs Non-Linear Shrinkage and all the variants of DCC-NLS
Authors: Christian Bongiorno, Damien Challet
Date: 2023-09-29 00:00:00
Tasks: All

The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC+NLS). We pit all the known variants of DCC+NLS (quadratic shrinkage, gross-leverage or turnover limitations, and factor-augmented NLS) against the Average Oracle in large-scale randomized experiments. We find generically that while some variants of DCC+NLS sometimes yield the lowest average realized volatility, albeit with a small improvement, their excessive gross leverage and investment concentration, and their 10-time larger turnover contribute to smaller average portfolio returns, which mechanically result in smaller realized Sharpe ratios than the Average Oracle. We also provide simple analytical arguments about the origin of the advantage of the Average Oracle over NLS in a changing world.
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Authors: Senkang Hu, Zhengru Fang, Haonan An, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang
Date: 2023-09-15 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via communications. Despite advances in previous approaches, challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity. Our novelties lie in three aspects. We first design a transmission delay minimization method, which can construct the communication graph and minimize the transmission delay according to different channel information state. We then propose an adaptive data reconstruction mechanism, which can dynamically adjust the rate-distortion trade-off to enhance perception efficiency. Moreover, it minimizes the temporal redundancy during data transmissions. Finally, we conceive a domain alignment scheme to align the data distribution from different vehicles, which can mitigate the domain gap between different vehicles and improve the performance of the target task. Comprehensive experiments demonstrate the effectiveness of our method in comparison to the existing state-of-the-art works.
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation
Authors: Zhen Liu, Hang Gao, Hao Ma, Shuo Cai, Yunfeng Hu, Ting Qu, Hong Chen, Xun Gong
Date: 2023-09-29 00:00:00
Tasks: Reinforcement Learning (RL)

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Authors: Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Date: 2023-09-29 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model, NetHack, Reinforcement Learning (RL)

Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent. Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning. We evaluate Motif's performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif's intrinsic reward with the environment reward, our method significantly outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations. Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.
Improving Planning with Large Language Models: A Modular Agentic Architecture
Authors: Taylor Webb, Shanka Subhra Mondal, Ida Momennejad
Date: 2023-09-30 00:00:00
Tasks: In-Context Learning, Reinforcement Learning (RL), StrategyQA

Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require multi-step reasoning or goal-directed planning. Both cognitive neuroscience and reinforcement learning (RL) have proposed a number of interacting functional components that together implement search and evaluation in multi-step decision making. These components include conflict monitoring, state prediction, state evaluation, task decomposition, and orchestration. To improve planning with LLMs, we propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of the specialized modules mentioned above, each implemented using an LLM. MAP improves planning through the interaction of specialized modules that break down a larger problem into multiple brief automated calls to the LLM. We evaluate MAP on three challenging planning tasks -- graph traversal, Tower of Hanoi, and the PlanBench benchmark -- as well as an NLP task requiring multi-step reasoning (strategyQA). We find that MAP yields significant improvements over both standard LLM methods (zero-shot prompting, in-context learning) and competitive baselines (chain-of-thought, multi-agent debate, and tree-of-thought), can be effectively combined with smaller and more cost-efficient LLMs (Llama3-70B), and displays superior transfer across tasks. These results suggest the benefit of a modular and multi-agent approach to planning with LLMs.
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Authors: Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio
Date: 2023-09-30 00:00:00
Tasks: Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Zero-shot Generalization

Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper's significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods.