pwc-archive/papers-with-abstracts huggingface dataset:
Distributed VC Firms: The Next Iteration of Venture Capital
Authors: Mohib Jafri, Andy Wu
Date: 2023-04-07 00:00:00

Using a combination of incentive modeling and empirical meta-analyses, this paper provides a pointed critique at the incentive systems that drive venture capital firms to optimize their practices towards activities that increase General Partner utility yet are disjoint from improving the underlying asset of startup equity. We propose a "distributed venture firm" powered by software automations and governed by a set of functional teams called "Pods" that carry out specific tasks with immediate and long-term payouts given on a deal-by-deal basis. Avenues are provided for further research to validate this model and discover likely paths to implementation.
Echo disappears: momentum term structure and cyclic information in turnover
Authors: Haoyu Wang, Junpeng Di, Yuegu Xie
Date: 2023-04-07 00:00:00

We extract cyclic information in turnover and find it can explain the momentum echo. The reversal in recent month momentum is the key factor that cancels out the recent month momentum and excluding it makes the echo regress to a damped shape. Both rational and behavioral theories can explain the reversal. This study is the first explanation of the momentum echo in U.S. stock markets.
The Dynamics of Leverage and the Belief Distribution of Wealth
Authors: Bikramaditya Datta, Rajiv Sethi
Date: 2023-04-07 00:00:00

The scale and terms of aggregate borrowing in an economy depend on the manner in which wealth is distributed across potential creditors with heterogeneous beliefs about the future. This distribution evolves over time as uncertainty is resolved, in favour of optimists if loans are repaid in full, and in favour of pessimists if there is widespread default. We model this process in an economy with two assets - risky bonds and risk-free cash. Within periods, given the inherited distribution of wealth across belief types, the scale and terms of borrowing are endogenously determined. Following good states, aggregate borrowing and the face value of debt both rise, and the interest rate falls. In the absence of noise, wealth converges to beliefs that differ systematically from the objective probability governing state realisations, with greater risk-aversion associated with greater optimism. In the presence of noise, the economy exhibits periods of high performance, punctuated by periods of crisis and stagnation.
Leveraging policy instruments and financial incentives to reduce embodied carbon in energy retrofits
Authors: Haonan Zhang
Date: 2023-04-06 00:00:00

The existing buildings and building construction sectors together are responsible for over one-third of the total global energy consumption and nearly 40% of total greenhouse gas (GHG) emissions. GHG emissions from the building sector are made up of embodied emissions and operational emissions. Recognizing the importance of reducing energy use and emissions associated with the building sector, governments have introduced policies, standards, and design guidelines to improve building energy performance and reduce GHG emissions associated with operating buildings. However, policy initiatives that reduce embodied emissions of the existing building sector are lacking. This research aims to develop policy strategies to reduce embodied carbon emissions in retrofits. In order to achieve this goal, this research conducted a literature review and identification of policies and financial incentives in British Columbia (BC) for reducing overall GHG emissions from the existing building sector. Then, this research analyzed worldwide policies and incentives that reduce embodied carbon emissions in the existing building sector. After reviewing the two categories of retrofit policies, the author identified links and opportunities between existing BC strategies, tools, and incentives, and global embodied emission strategies. Finally, this research compiled key findings from all resources and provided policy recommendations for reducing embodied carbon emissions in retrofits in BC.
An unsupervised segmentation of vocal breath sounds
Authors: Shivani Yadav, Dipanjan Gope, Uma Maheswari K., Prasanta Kumar Ghosh
Date: 2023-04-07 00:00:00
Tasks: Boundary Detection

Breathing is an essential part of human survival, which carries information about a person's physiological and psychological state. Generally, breath boundaries are marked by experts before using for any task. An unsupervised algorithm for breath boundary detection has been proposed for breath sounds recorded at the mouth also referred as vocal breath sounds (VBS) in this work. Breath sounds recorded at the mouth are used in this work because they are easy and contactless to record than tracheal breath sounds and lung breath sounds. The periodic nature of breath signal energy is used to segment the breath boundaries. Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy, 29 patients) having 307 breaths are predicted. With the proposed method, M ($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed method shows better performance than the baselines used in this work. Even the classification performance between asthmatic and healthy subjects using estimated boundaries by the proposed method is comparable with the ground truth boundaries.
Temperature Detection from Images Using Smartphones
Authors: Kamrul H Foysal, Bipasha Kundu, Jo Woon Chong
Date: 2023-04-07 00:00:00

Since late 2019, the global spread of COVID-19 has affected people's daily life. Temperature is an early and common symptom of Covid. Therefore, a convenient and remote temperature detection method is needed. In this paper, a non-contact method for detecting body temperature is proposed. Our developed algorithm based on blackbody radiation calculates the body temperature of a user-selected area from an obtained image. The findings were confirmed using a FLIR Thermal Camera with an accuracy of 97%.
Fast Marching based Tissue Adaptive Delay Estimation for Aberration Corrected Delay and Sum Beamforming in Ultrasound Imaging
Authors: M. S. Asif, Gayathri Malamal, A. N. Madhavanunni, Vikram Melapudi, V Rahul, Abhijit PATIL, Rajesh Langoju, Mahesh Raveendranatha Panicker
Date: 2023-04-07 00:00:00
Tasks: Image Reconstruction

Conventional ultrasound (US) imaging employs the delay and sum (DAS) receive beamforming with dynamic receive focus for image reconstruction due to its simplicity and robustness. However, the DAS beamforming follows a geometrical method of delay estimation with a spatially constant speed-of-sound (SoS) of 1540 m/s throughout the medium irrespective of the tissue in-homogeneity. This approximation leads to errors in delay estimations that accumulate with depth and degrades the resolution, contrast and overall accuracy of the US image. In this work, we propose a fast marching based DAS for focused transmissions which leverages the approximate SoS map to estimate the refraction corrected propagation delays for each pixel in the medium. The proposed approach is validated qualitatively and quantitatively for imaging depths of upto ~ 11 cm through simulations, where fat layer induced aberration is employed to alter the SoS in the medium. To the best of authors' knowledge, this is the first work considering the effect of SoS on image quality for deeper imaging.
Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity
Authors: Brian Mcreynolds, Rui Graca, Tobi Delbruck
Date: 2023-04-07 00:00:00
Tasks: Sensitivity

Dynamic Vision Sensors (DVS) record "events" corresponding to pixel-level brightness changes, resulting in data-efficient representation of a dynamic visual scene. As DVS expand into increasingly diverse applications, non-ideal behaviors in their output under extreme sensing conditions are important to consider. Under low illumination (below ~10 lux) their output begins to be dominated by shot noise events (SNEs) which increase the data output and obscure true signal. SNE rates can be controlled to some degree by tuning circuit parameters to reduce sensitivity or temporal response bandwidth at the cost of signal loss. Alternatively, an improved understanding of SNE statistics can be leveraged to develop novel techniques for minimizing uninformative sensor output. We first explain a fundamental observation about sequential pairing of opposite polarity SNEs based on pixel circuit logic and validate our theory using DVS recordings and simulations. Finally, we derive a practical result from this new understanding and demonstrate two novel biasing techniques to reduce SNEs by 50% and 80% respectively while still retaining sensitivity and/or temporal resolution.
mcLARO: Multi-Contrast Learned Acquisition and Reconstruction Optimization for simultaneous quantitative multi-parametric mapping
Authors: Jinwei Zhang, Thanh D. Nguyen, Eddy Solomon, Chao Li, Qihao Zhang, Jiahao Li, Hang Zhang, Pascal Spincemaille, Yi Wang
Date: 2023-04-07 00:00:00
Tasks: Image Reconstruction

Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM mapping in a single scan using multi-contrast Learned Acquisition and Reconstruction Optimization (mcLARO). Methods: A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1, T2, T2* and magnetic susceptibility. The proposed mcLARO used a deep learning framework to optimize both the multi-contrast k-space under-sampling pattern and the image reconstruction based on image feature fusion. The proposed mcLARO method with R=8 under-sampling was validated in a retrospective ablation study using fully sampled data as reference and evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard. Results: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without multi-contrast sampling pattern optimization or image feature fusion, and negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values were obtained by the under-sampled reconstructions compared to the fully sampled reconstruction. The prospective study showed small or negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). Conclusion: mcLARO enabled fast sub-millimeter T1, T2, T2* and QSM mapping in a single scan.
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment
Authors: Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik
Date: 2023-04-06 00:00:00
Tasks: All, Computational Efficiency, Video Compression, Video Quality Assessment

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2\% to 5.3\%, and computational efficiency, by factors of 3.8 to 11 times!
Joint Block-Sparse Recovery Using Simultaneous BOMP/BOLS
Authors: Liyang Lu, Zhaocheng Wang, Sheng Chen
Date: 2023-04-07 00:00:00
Tasks: compressed sensing, valid

We consider the greedy algorithms for the joint recovery of high-dimensional sparse signals based on the block multiple measurement vector (BMMV) model in compressed sensing (CS). To this end, we first put forth two versions of simultaneous block orthogonal least squares (S-BOLS) as the baseline for the OLS framework. Their cornerstone is to sequentially check and select the support block to minimize the residual power. Then, parallel performance analysis for the existing simultaneous block orthogonal matching pursuit (S-BOMP) and the two proposed S-BOLS algorithms is developed. It indicates that under the conditions based on the mutual incoherence property (MIP) and the decaying magnitude structure of the nonzero blocks of the signal, the algorithms select all the significant blocks before possibly choosing incorrect ones. In addition, we further consider the problem of sufficient data volume for reliable recovery, and provide its MIP-based bounds in closed-form. These results together highlight the key role of the block characteristic in addressing the weak-sparse issue, i.e., the scenario where the overall sparsity is too large. The derived theoretical results are also universally valid for conventional block-greedy algorithms and non-block algorithms by setting the number of measurement vectors and the block length to 1, respectively.
Synthesis of Mathematical programs from Natural Language Specifications
Authors: Ganesh Prasath, Shirish Karande
Date: 2023-03-30 00:00:00
Tasks: AutoML, Data Augmentation

Several decision problems that are encountered in various business domains can be modeled as mathematical programs, i.e. optimization problems. The process of conducting such modeling often requires the involvement of experts trained in operations research and advanced algorithms. Surprisingly, despite the significant advances in the methods for program and code synthesis, AutoML, learning to optimize etc., there has been little or no attention paid to automating the task of synthesizing mathematical programs. We imagine a scenario where the specifications for modeling, i.e. the objective and constraints are expressed in an unstructured form in natural language (NL) and the mathematical program has to be synthesized from such an NL specification. In this work we evaluate the efficacy of employing CodeT5 with data augmentation and post-processing of beams. We utilize GPT-3 with back translation for generation of synthetic examples. Further we apply rules of linear programming to score beams and correct beams based on common error patterns. We observe that with these enhancements CodeT5 base gives an execution accuracy of 0.73 which is significantly better than zero-shot execution accuracy of 0.41 by ChatGPT and 0.36 by Codex.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT
Authors: Jiawei Zhang
Date: 2023-04-10 00:00:00
Proceeding: Preprint 2023 4
Tasks: Community Detection, Graph Classification, Graph Learning, In-Context Learning, Knowledge Graphs, Language Modelling, Large Language Model, Node Classification, Recommendation Systems

In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing multi-step logic reasoning, precise mathematical calculation and perception about the spatial and temporal factors.

To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools. Specifically, we will investigate to teach Graph-ToolFormer to handle various graph data reasoning tasks in this paper, including both (1) very basic graph data loading and graph property reasoning tasks, ranging from simple graph order and size to the graph diameter and periphery, and (2) more advanced reasoning tasks on real-world graph data, such as bibliographic networks, protein molecules, sequential recommender systems, social networks and knowledge graphs. 

Technically, to build Graph-ToolFormer, we propose to handcraft both the instruction and a small-sized of prompt templates for each of the graph reasoning tasks, respectively. Via in-context learning, based on such instructions and prompt template examples, we adopt ChatGPT to annotate and augment a larger graph reasoning statement dataset with the most appropriate calls of external API functions. Such augmented prompt datasets will be post-processed with selective filtering and used for fine-tuning existing pre-trained causal LLMs, such as the GPT-J, to teach them how to use graph reasoning tools in the output generation. To demonstrate the effectiveness of Graph-ToolFormer, we conduct some preliminary experimental studies on various graph reasoning datasets and tasks, and will launch a LLM demo online with various graph reasoning abilities.
SGDP: A Stream-Graph Neural Network Based Data Prefetcher
Authors: Yiyuan Yang, Rongshang Li, Qiquan Shi, Xijun Li, Gang Hu, Xing Li, Mingxuan Yuan
Date: 2023-04-07 00:00:00
Tasks: Graph Neural Network

Data prefetching is important for storage system optimization and access performance improvement. Traditional prefetchers work well for mining access patterns of sequential logical block address (LBA) but cannot handle complex non-sequential patterns that commonly exist in real-world applications. The state-of-the-art (SOTA) learning-based prefetchers cover more LBA accesses. However, they do not adequately consider the spatial interdependencies between LBA deltas, which leads to limited performance and robustness. This paper proposes a novel Stream-Graph neural network-based Data Prefetcher (SGDP). Specifically, SGDP models LBA delta streams using a weighted directed graph structure to represent interactive relations among LBA deltas and further extracts hybrid features by graph neural networks for data prefetching. We conduct extensive experiments on eight real-world datasets. Empirical results verify that SGDP outperforms the SOTA methods in terms of the hit ratio by 6.21%, the effective prefetching ratio by 7.00%, and speeds up inference time by 3.13X on average. Besides, we generalize SGDP to different variants by different stream constructions, further expanding its application scenarios and demonstrating its robustness. SGDP offers a novel data prefetching solution and has been verified in commercial hybrid storage systems in the experimental phase. Our codes and appendix are available at https://github.com/yyysjz1997/SGDP/.
Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market
Authors: Ke Zhang
Date: 2023-03-29 00:00:00
Tasks: Management

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China's CSI 300 and CSI 500 universe which represent China's large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index.
Counterfactual Explanations of Neural Network-Generated Response Curves
Authors: Giorgio Morales, John Sheppard
Date: 2023-04-08 00:00:00
Tasks: counterfactual, Crop Yield Prediction

Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as "active feature") might depend on the values of the other input features (referred to as "passive features"). In this work, we consider the case of systems whose response is approximated using regression neural networks. We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes. CFEs are generated by a genetic algorithm-based approach that solves a multi-objective optimization problem. In particular, given a response curve generated for an active feature, a CFE finds the minimum combination of passive features that need to be modified to alter the shape of the response curve. We tested our method on a synthetic dataset with 1-D inputs and two crop yield prediction datasets with 2-D inputs. The relevance ranking of features and feature combinations obtained on the synthetic dataset coincided with the analysis of the equation that was used to generate the problem. Results obtained on the yield prediction datasets revealed that the impact on fertilizer responsivity of passive features depends on the terrain characteristics of each field.
A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection
Authors: Seyed Mohammad Hossein Abedy Nejad, Mohammad Amin Behzadi, Abdolrahim Taheri
Date: 2023-04-08 00:00:00
Tasks: Fault Detection

Overloading in DC servo motors is a major concern in industries, as many companies face the problem of finding expert operators, and also human monitoring may not be an effective solution. Therefore, this paper proposed an embedded Artificial intelligence (AI) approach using a Convolutional Neural Network (CNN) using a new transformation to extract faults from real-time input signals without human interference. Our main purpose is to extract as many as possible features from the input signal to achieve a relaxed dataset that results in an effective but compact network to provide real-time fault detection even in a low-memory microcontroller. Besides, fault detection method a synchronous dual-motor system is also proposed to take action in faulty events. To fulfill this intention, a one-dimensional input signal from the output current of each DC servo motor is monitored and transformed into a 3d stack of data and then the CNN is implemented into the processor to detect any fault corresponding to overloading, finally experimental setup results in 99.9997% accuracy during testing for a model with nearly 8000 parameters. In addition, the proposed dual-motor system could achieve overload reduction and provide a fault-tolerant system and it is shown that this system also takes advantage of less energy consumption.