pwc-archive/papers-with-abstracts huggingface dataset:
The Determinants of Foreign Direct Investment (FDI) A Panel Data Analysis for the Emerging Asian Economies
Authors: ATM Omor Faruq
Date: 2023-07-13 00:00:00

In this paper, we explore the economic, institutional, and political/governmental factors in attracting Foreign Direct Investment (FDI) inflows in the emerging twenty-four Asian economies. To examine the significant determinants of FDI, the study uses panel data for a period of seventeen years (2002-2018). The panel methodology enables us to deal with endogeneity and other issues. Multiple regression models are done for empirical evidence. The study focuses on a holistic approach and considers different variables under three broad areas: economic, institutional, and political aspects. The variables include Market Size, Trade Openness, Inflation, Natural Resource, Lending Rate, Capital Formation as economic factors and Business Regulatory Environment and Business Disclosure Index as institutional factors and Political Stability, Government Effectiveness, and Rule of Law as political factors. The empirical findings show most of the economic factors significantly affect FDI inflows whereas Business Disclosure is the only important institutional variable. Moreover, political stability has a significant positive impact in attracting foreign capital flow though the impact of government effectiveness is found insignificant. Overall, the economic factors prevail strongly compared to institutional and political factors.
Advertiser Learning in Direct Advertising Markets
Authors: Carl F. Mela, Jason M. T. Roos, Tulio Sousa
Date: 2023-07-13 00:00:00
Tasks: counterfactual

Direct buy advertisers procure advertising inventory at fixed rates from publishers and ad networks. Such advertisers face the complex task of choosing ads amongst myriad new publisher sites. We offer evidence that advertisers do not excel at making these choices. Instead, they try many sites before settling on a favored set, consistent with advertiser learning. We subsequently model advertiser demand for publisher inventory wherein advertisers learn about advertising efficacy across publishers' sites. Results suggest that advertisers spend considerable resources advertising on sites they eventually abandon--in part because their prior beliefs about advertising efficacy on those sites are too optimistic. The median advertiser's expected CTR at a new site is 0.23%, five times higher than the true median CTR of 0.045%. We consider how an ad network's pooling of advertiser information remediates this problem. As ads with similar visual elements garner similar CTRs, the network's pooling of information enables advertisers to better predict ad performance at new sites. Counterfactual analyses indicate that gains from pooling advertiser information are substantial: over six months, we estimate a median advertiser welfare gain of \$2,756 (a 15.5% increase) and a median publisher revenue gain of \$9,618 (a 63.9% increase).
Reproducing the Acoustic Velocity Vectors in a Spherical Listening Region
Authors: Jiarui Wang, Thushara Abhayapala, Jihui Aimee Zhang, Prasanga Samarasinghe
Date: 2023-07-14 00:00:00

Acoustic velocity vectors (AVVs) are related to the human's perception of sound at low frequencies and are widely used in Ambisonics. This paper proposes a spatial sound field reproduction algorithm called velocity matching, which reproduces the AVVs in the spherical listening region by matching the AVVs' spherical harmonic coefficients. Using the sound field translation formula, the spherical harmonic coefficients of the AVVs are derived from the spherical harmonic coefficients of the pressure, which can be measured by a higher-order microphone array. Unlike algorithms that only control the AVVs at discrete sweet spots, the proposed velocity matching algorithm manipulates the AVVs in the whole spherical listening region and allows the listener to move beyond the sweet spots. Simulations show the proposed velocity matching algorithm accurately reproduces the AVVs in the spherical listening region and requires fewer number of loudspeakers than pressure matching algorithm.
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection
Authors: Haijun Liu, Xi Su, Xiangfei Shen, Lihui Chen, Xichuan Zhou
Date: 2023-07-14 00:00:00
Tasks: Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their powerful ability to model the complicated background. However, for reconstruction tasks, DNNs tend to incorporate both background and anomalies into the estimated background, which is referred to as the identical mapping problem (IMP) and leads to significantly decreased performance. To address this limitation, we propose a model-independent binary mask-guided separation training strategy for DNNs, named BiGSeT. Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image. The background is preserved, while the potential anomalies are suppressed by using an efficient second-order Laplacian of Gaussian (LoG) operator, generating a pure background estimate. In order to maintain separability during training, we periodically update the mask using a robust proportion threshold estimated before the training. In our experiments, We adopt a vanilla AE as the network to validate our training strategy on several real-world datasets. Our results show superior performance compared to some state-of-the-art methods. Specifically, we achieved a 90.67% AUC score on the HyMap Cooke City dataset. Additionally, we applied our training strategy to other deep network structures, achieving improved detection performance compared to their original versions, demonstrating its effective transferability. The code of our method will be available at https://github.com/enter-i-username/BiGSeT.
Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework
Authors: Jinyu Guo, Feng Zhang, Hang Zhao, Baoxiang Pan, Linlu Mei
Date: 2023-07-14 00:00:00
Tasks: Super-Resolution

Satellite-collected nighttime light provides a unique perspective on human activities, including urbanization, population growth, and epidemics. Yet, long-term and fine-grained nighttime light observations are lacking, leaving the analysis and applications of decades of light changes in urban facilities undeveloped. To fill this gap, we developed an innovative framework and used it to design a new super-resolution model that reconstructs low-resolution nighttime light data into high resolution. The validation of one billion data points shows that the correlation coefficient of our model at the global scale reaches 0.873, which is significantly higher than that of other existing models (maximum = 0.713). Our model also outperforms existing models at the national and urban scales. Furthermore, through an inspection of airports and roads, only our model's image details can reveal the historical development of these facilities. We provide the long-term and fine-grained nighttime light observations to promote research on human activities. The dataset is available at \url{https://doi.org/10.5281/zenodo.7859205}.
Flexible Beamforming in B5G for Improving Tethered UAV Coverage over Smart Environments
Authors: Abdu Saif, Nor Shahida Mohd Shah, Soreen Ameen Fattah, Saeed Hamood Alsamhi, Santosh Kumar, Ali Saad Al khuraib
Date: 2023-07-14 00:00:00

Unmanned Aerial Vehicles (UAVs) are being used for wireless communications in smart environments. However, the need for mobility, scalability of data transmission over wide areas, and the required coverage area make UAV beamforming essential for better coverage and user experience. To this end, we propose a flexible beamforming approach to improve tethered UAV coverage quality and maximize the number of users experiencing the minimum required rate in any target environment. Our solution demonstrates a significant achievement in flexible beamforming in smart environments, including urban, suburban, dense, and high-rise urban. Furthermore, the beamforming gain is mainly concentrated in the target to improve the coverage area based on various scenarios. Simulation results show that the proposed approach can achieve a significantly received flexible power beam that focuses the transmitted signal towards the receiver and improves received power by reducing signal power spread. In the case of no beamforming, signal power spreads out as distance increases, reducing the signal strength. Furthermore, our proposed solution is suitable for improving UAV coverage and reliability in smart and harsh environments.
The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platforms
Authors: Yuyang Du, Hongyu Deng, Soung Chang Liew, Kexin Chen, Yulin Shao, He Chen
Date: 2023-07-14 00:00:00
Tasks: In-Context Learning, Scheduling

Large language models (LLMs) have garnered significant attention across various research disciplines, including the wireless communication community. There have been several heated discussions on the intersection of LLMs and wireless technologies. While recent studies have demonstrated the ability of LLMs to generate hardware description language (HDL) code for simple computation tasks, developing wireless prototypes and products via HDL poses far greater challenges because of the more complex computation tasks involved. In this paper, we aim to address this challenge by investigating the role of LLMs in FPGA-based hardware development for advanced wireless signal processing. We begin by exploring LLM-assisted code refactoring, reuse, and validation, using an open-source software-defined radio (SDR) project as a case study. Through the case study, we find that an LLM assistant can potentially yield substantial productivity gains for researchers and developers. We then examine the feasibility of using LLMs to generate HDL code for advanced wireless signal processing, using the Fast Fourier Transform (FFT) algorithm as an example. This task presents two unique challenges: the scheduling of subtasks within the overall task and the multi-step thinking required to solve certain arithmetic problem within the task. To address these challenges, we employ in-context learning (ICL) and Chain-of-Thought (CoT) prompting techniques, culminating in the successful generation of a 64-point Verilog FFT module. Our results demonstrate the potential of LLMs for generalization and imitation, affirming their usefulness in writing HDL code for wireless communication systems. Overall, this work contributes to understanding the role of LLMs in wireless communication and motivates further exploration of their capabilities.
A Comparative Study of Polynomial Chaos Expansion-Based Methods for Global Sensitivity Analysis in Power System Uncertainty Control
Authors: Xiaoting Wang, Rong-Peng Liu, Xiaozhe Wang, François Bouffard
Date: 2023-07-13 00:00:00
Tasks: Management, Sensitivity

In this letter, we compare three polynomial chaos expansion (PCE)-based methods for ANCOVA (ANalysis of COVAriance) indices based global sensitivity analysis for correlated random inputs in two power system applications. Surprisingly, the PCE-based models built with independent inputs after decorrelation may not give the most accurate ANCOVA indices, though this approach seems to be the most correct one and was applied in [1] in the field of civil engineering. In contrast, the PCE model built using correlated random inputs directly yields the most accurate ANCOVA indices for global sensitivity analysis. Analysis and discussions about the errors of different PCE-based models will also be presented. These results provide important guidance for uncertainty management and control in power system operation and security assessment.
Approximately optimal trade execution strategies under fast mean-reversion
Authors: David Evangelista, Yuri Thamsten
Date: 2023-07-13 00:00:00

In a fixed time horizon, appropriately executing a large amount of a particular asset -- meaning a considerable portion of the volume traded within this frame -- is challenging. Especially for illiquid or even highly liquid but also highly volatile ones, the role of "market quality" is quite relevant in properly designing execution strategies. Here, we model it by considering uncertain volatility and liquidity; hence, moments of high or low price impact and risk vary randomly throughout the trading period. We work under the central assumption: although there are these uncertain variations, we assume they occur in a fast mean-reverting fashion. We thus employ singular perturbation arguments to study approximations to the optimal strategies in this framework. By using high-frequency data, we provide estimation methods for our model in face of microstructure noise, as well as numerically assess all of our results.
Inverse Optimization for Routing Problems
Authors: Pedro Zattoni Scroccaro, Piet van Beek, Peyman Mohajerin Esfahani, Bilge Atasoy
Date: 2023-07-14 00:00:00

We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision-makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks 2nd compared with the 48 models that qualified for the final round of the challenge. Our examples and results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers' decisions in routing problems.
Optimal contract design via relaxation: application to the problem of brokerage fee for a client with private signal
Authors: Guillermo Alonso Alvarez, Sergey Nadtochiy
Date: 2023-07-13 00:00:00

In this paper we show how the relaxation techniques can be used to establish the existence of an optimal contract in presence of information asymmetry. The method we illustrate was initially motivated by the problem of designing optimal brokerage fees, but it does apply to other optimal contract problems, in which (i) the agent controls linearly the drift of a diffusion process, (ii) the direct dependence of the principal's and the agent's objectives on the strategy of the agent is of a special form, and (iii) the space of admissible contracts is compact. This method is then applied to establish existence of an optimal brokerage fee in a market model with a private trading signal observed by the broker's client but not by the broker.
Understanding AKT-mediated chemoresistance: the relationship between ion channels and AKT activation
Authors: Rédoane Daoudi
Date: 2023-07-13 00:00:00

Overcoming chemoresistance is a challenge for multiple chemotherapeutics agents like cisplatin. ABC transporters such as MDR1 or MRPs and PI3K/AKT pathway have been proposed as actors of chemoresistance in several cancers. In this review we describe two downstream targets of Akt: c-myc and p53 in the chemoresistance. We suggest a potential link between p53, c-myc and ABC transporters expression. Consequently a link between Akt and ABC transporters-mediated chemoresistance may exist. Finally we show that Akt activation may be Orai-dependent and/or TRPC-dependent, suggesting that these ion channels could constitute a therapeutic target in cancer.
TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling
Authors: Ke Deng, Zhiyuan He, Hao Zhang, Haohan Lin, DeSheng Wang
Date: 2023-06-16 00:00:00
Tasks: Edge-computing, Scheduling

In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms.
A Rubik's Cube inspired approach to Clifford synthesis
Authors: Ning Bao, Gavin S. Hartnett
Date: 2023-07-17 00:00:00
Tasks: Rubik's Cube

The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis. Drawing inspiration from similarities between this and the famous Rubik's Cube problem, we develop a machine learning approach for Clifford synthesis based on learning an approximation to the distance to the identity. This approach is probabilistic and computationally intensive. However, when a decomposition is successfully found, it often involves fewer gates than existing synthesis algorithms. Additionally, our approach is much more flexible than existing algorithms in that arbitrary gate sets, device topologies, and gate fidelities may incorporated, thus allowing for the approach to be tailored to a specific device.
Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
Authors: Cheng Chen, Yifan Zhao, Jia Li
Date: 2023-07-15 00:00:00
Tasks: Contrastive Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Recognition

Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing literature mainly focuses on label completion and co-occurrence learning while facing difficulties with the most common single-positive label manner. To tackle this problem, we present a semantic contrastive bootstrapping (Scob) approach to gradually recover the cross-object relationships by introducing class activation as semantic guidance. With this learning guidance, we then propose a recurrent semantic masked transformer to extract iconic object-level representations and delve into the contrastive learning problems on multi-label classification tasks. We further propose a bootstrapping framework in an Expectation-Maximization fashion that iteratively optimizes the network parameters and refines semantic guidance to alleviate possible disturbance caused by wrong semantic guidance. Extensive experimental results demonstrate that the proposed joint learning framework surpasses the state-of-the-art models by a large margin on four public multi-label image recognition benchmarks. Codes can be found at https://github.com/iCVTEAM/Scob.
Fast model inference and training on-board of Satellites
Authors: Vít Růžička, Gonzalo Mateo-García, Chris Bridges, Chris Brunskill, Cormac Purcell, Nicolas Longépé, Andrew Markham
Date: 2023-07-17 00:00:00
Tasks: CPU, Decision Making

Artificial intelligence onboard satellites has the potential to reduce data transmission requirements, enable real-time decision-making and collaboration within constellations. This study deploys a lightweight foundational model called RaVAEn on D-Orbit's ION SCV004 satellite. RaVAEn is a variational auto-encoder (VAE) that generates compressed latent vectors from small image tiles, enabling several downstream tasks. In this work we demonstrate the reliable use of RaVAEn onboard a satellite, achieving an encoding time of 0.110s for tiles of a 4.8x4.8 km$^2$ area. In addition, we showcase fast few-shot training onboard a satellite using the latent representation of data. We compare the deployment of the model on the on-board CPU and on the available Myriad vision processing unit (VPU) accelerator. To our knowledge, this work shows for the first time the deployment of a multi-task model on-board a CubeSat and the on-board training of a machine learning model.
Pair then Relation: Pair-Net for Panoptic Scene Graph Generation
Authors: Jinghao Wang, Zhengyu Wen, Xiangtai Li, Zujin Guo, Jingkang Yang, Ziwei Liu
Date: 2023-07-17 00:00:00
Tasks: Graph Generation, Panoptic Scene Graph Generation, Panoptic Segmentation, Relation, Scene Graph Generation

Panoptic Scene Graph (PSG) is a challenging task in Scene Graph Generation (SGG) that aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Compared to SGG, PSG has several challenging problems: pixel-level segment outputs and full relationship exploration (It also considers thing and stuff relation). Thus, current PSG methods have limited performance, which hinders downstream tasks or applications. The goal of this work aims to design a novel and strong baseline for PSG. To achieve that, we first conduct an in-depth analysis to identify the bottleneck of the current PSG models, finding that inter-object pair-wise recall is a crucial factor that was ignored by previous PSG methods. Based on this and the recent query-based frameworks, we present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network (PPN) to learn and filter sparse pair-wise relationships between subjects and objects. Moreover, we also observed the sparse nature of object pairs for both Motivated by this, we design a lightweight Matrix Learner within the PPN, which directly learns pair-wised relationships for pair proposal generation. Through extensive ablation and analysis, our approach significantly improves upon leveraging the segmenter solid baseline. Notably, our method achieves over 10\% absolute gains compared to our baseline, PSGFormer. The code of this paper is publicly available at https://github.com/king159/Pair-Net.
COLLIE: Systematic Construction of Constrained Text Generation Tasks
Authors: Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang, Karthik Narasimhan
Date: 2023-07-17 00:00:00
Tasks: Logical Reasoning, Sentence, Text Generation

Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models. However, existing benchmarks for constrained generation usually focus on fixed constraint types (e.g.,generate a sentence containing certain words) that have proved to be easy for state-of-the-art models like GPT-4. We present COLLIE, a grammar-based framework that allows the specification of rich, compositional constraints with diverse generation levels (word, sentence, paragraph, passage) and modeling challenges (e.g.,language understanding, logical reasoning, counting, semantic planning). We also develop tools for automatic extraction of task instances given a constraint structure and a raw text corpus. Using COLLIE, we compile the COLLIE-v1 dataset with 2080 instances comprising 13 constraint structures. We perform systematic experiments across five state-of-the-art instruction-tuned language models and analyze their performances to reveal shortcomings. COLLIE is designed to be extensible and lightweight, and we hope the community finds it useful to develop more complex constraints and evaluations in the future.