pwc-archive/papers-with-abstracts huggingface dataset:
Energy Efficiency Maximization in IRS-enabled Phase Cooperative PS-SWIPT based Self-sustainable IoT Network
Authors: Haleema Sadia, Ahmad Kamal Hassan, ZIAUL HAQ ABBAS, GHULAM ABBAS, Thar Baker
Date: 2024-01-10 00:00:00

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To address this challenge, intelligent reflecting surfaces (IRSs) are introduced in PS-SWIPT based SS-IoT networks to improve network efficiency by controlling signal reflections. In this article, an IRS-enabled phase cooperative framework is proposed to improve energy efficiency (EE) of the IoT network $({\mathtt {I}}^{net})$ using phase shifts of the user network $({\mathtt {U}^{net})}$, without constraining hardware resources at ${\mathtt {U}^{net}}$. We exploit transmit beamforming (BF) at access points (APs) and phase shifts optimization at the IRS end with phase effective cooperation between APs to enhance ${\mathtt {I}}^{net}$ EE performance. The maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the ${\mathtt {U}^{net}}$ using low computational complexity heuristic BF approaches, namely, transmit minimum-mean-square-error and zero-forcing BF, and phase optimization is performed using semidefinite relaxation (SDR) approach. To combat the computational complexity of AO, we also propose an alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. Next, EE maximization is solved for the ${\mathtt {I}^{net}}$ by optimizing the PS ratio and active BF vectors by exploiting optimal phase shifts of the ${\mathtt {U}}^{net}$. Simulation results confirm that employing IRS phase cooperation in PS-SWIPT based SS-IoT networks can significantly improve EE performance of ${\mathtt {I}^{net}}$ without constraining resources.
Model-Driven Dataset Generation for Data-Driven Battery SOH Models
Authors: Khaled Sidahmed Sidahmed Alamin, Francesco Daghero, Giovanni Pollo, Daniele Jahier Pagliari, Yukai Chen, Enrico Macii, Massimo Poncino, Sara Vinco
Date: 2024-01-10 00:00:00
Tasks: Dataset Generation

Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it at run time, a model is required for its estimation. Recently, several data-driven SOH models have been proposed, whose accuracy heavily relies on the quality of the datasets used for their training. Since these datasets are obtained from measurements, they are limited in the variety of the charge/discharge profiles. To address this scarcity issue, we propose generating datasets by simulating a traditional battery model (e.g., a circuit-equivalent one). The primary advantage of this approach is the ability to use a simulatable battery model to evaluate a potentially infinite number of workload profiles for training the data-driven model. Furthermore, this general concept can be applied using any simulatable battery model, providing a fine spectrum of accuracy/complexity tradeoffs. Our results indicate that using simulated data achieves reasonable accuracy in SOH estimation, with a 7.2% error relative to the simulated model, in exchange for a 27X memory reduction and a =2000X speedup.
Super-hedging-pricing formulas and Immediate-Profit arbitrage for market models under random horizon
Authors: Tahir Choulli, Emmanuel Lepinette
Date: 2024-01-11 00:00:00

In this paper, we consider the discrete-time setting, and the market model described by (S,F,T)$. Herein F is the ``public" flow of information which is available to all agents overtime, S is the discounted price process of d-tradable assets, and T is an arbitrary random time whose occurrence might not be observable via F. Thus, we consider the larger flow G which incorporates F and makes T an observable random time. This framework covers the credit risk theory setting, the life insurance setting and the setting of employee stock option valuation. For the stopped model (S^T,G) and for various vulnerable claims, based on this model, we address the super-hedging pricing valuation problem and its intrinsic Immediate-Profit arbitrage (IP hereafter for short). Our first main contribution lies in singling out the impact of change of prior and/or information on conditional essential supremum, which is a vital tool in super-hedging pricing. The second main contribution consists of describing as explicit as possible how the set of super-hedging prices expands under the stochasticity of T and its risks, and we address the IP arbitrage for (S^T,G) as well. The third main contribution resides in elaborating as explicit as possible pricing formulas for vulnerable claims, and singling out the various informational risks in the prices' dynamics.
Target search in the CRISPR/Cas9 system: Facilitated diffusion with target cues
Authors: Qiao Lu, Simone Pigolotti
Date: 2024-01-11 00:00:00

We study how Cas9, a central component of the CRISPR/Cas9 system, searches for a target sequence on the DNA. We propose a model that includes as key ingredients 3D diffusion, 1D sliding along the DNA, and the effect of short binding sequences preceding the target (protospacer adjacent sequences -- PAMs). This latter aspect constitutes the main difference with traditional facilitated diffusion of transcription factors. We solve our model, obtaining an expression for the average search time of Cas9 for its target. We find that experimentally measured kinetic parameters are close to the values yielding an optimal search time. Our results rationalize the role of PAMs in guiding the search process, and show that Cas9 searches for its targets in a nearly optimal way.
Boundary conditions at infinity for Black-Scholes equations
Authors: Yukihiro Tsuzuki
Date: 2024-01-10 00:00:00

We propose a numerical procedure for computing the prices of European options, in which the underlying asset price is a Markovian strict local martingale. If the underlying process is a strict local martingale and the payoff is of linear growth, multiple solutions exist for the corresponding Black-Scholes equations. When numerical schemes such as finite difference methods are applied, a boundary condition at infinity must be specified, which determines a solution among the candidates. The minimal solution, which is considered as the derivative price, is obtained by our boundary condition. The stability of our procedure is supported by the fact that our numerical solution satisfies a discrete maximum principle. In addition, its accuracy is demonstrated through numerical experiments in comparison with the methods proposed in the literature.
Quantum Probability Theoretic Asset Return Modeling: A Novel Schrödinger-Like Trading Equation and Multimodal Distribution
Authors: Li Lin
Date: 2024-01-11 00:00:00

Quantum theory provides a comprehensive framework for quantifying uncertainty, often applied in quantum finance to explore the stochastic nature of asset returns. This perspective likens returns to microscopic particle motion, governed by quantum probabilities akin to physical laws. However, such approaches presuppose specific microscopic quantum effects in return changes, a premise criticized for lack of guarantee. This paper diverges by asserting that quantum probability is a mathematical extension of classical probability to complex numbers. It isn't exclusively tied to microscopic quantum phenomena, bypassing the need for quantum effects in returns.By directly linking quantum probability's mathematical structure to traders' decisions and market behaviors, it avoids assuming quantum effects for returns and invoking the wave function. The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns.Utilizing Fourier decomposition, we derive a Schr\"odinger-like trading equation, where each term explicitly corresponds to implications of market trading. The equation indicates discrete energy levels in financial trading, with returns following a normal distribution at the lowest level. As the market transitions to higher trading levels, a phase shift occurs in the return distribution, leading to multimodality and fat tails. Empirical research on the Chinese stock market supports the existence of energy levels and multimodal distributions derived from this quantum probability asset returns model.
Introduction of L0 norm and application of L1 and C1 norm in the study of time-series
Authors: Victor Ujaldon Garcia
Date: 2023-12-30 00:00:00
Tasks: Time Series, Topological Data Analysis

Four markets are considered: Cryptocurrencies / South American exchange rate / Spanish Banking indices and European Indices and studied using TDA (Topological Data Analysis) tools. These tools are used to predict and showcase both strengths and weakness of the current TDA tools. In this paper a new tool $L0$ norm is defined and complemented with the already existing $C1$ norm.
Multiple-bubble testing in the cryptocurrency market: a case study of bitcoin
Authors: Sanaz Behzadi, Mahmonir Bayanati, Hamed Nozari
Date: 2023-12-29 00:00:00

Economic periods and financial crises have highlighted the importance of evaluating financial markets to investors and researchers in recent decades.
Seamless and multi-resolution energy forecasting
Authors: Chenxi Wang, Pierre Pinson, Yi Wang
Date: 2023-12-28 00:00:00
Tasks: Scheduling

Energy forecasting is pivotal in energy systems, by providing fundamentals for operation, with different horizons and resolutions. Though energy forecasting has been widely studied for capturing temporal information, very few works concentrate on the frequency information provided by forecasts. They are consequently often limited to single-resolution applications (e.g., hourly). Here, we propose a unified energy forecasting framework based on Laplace transform in the multi-resolution context. The forecasts can be seamlessly produced at different desired resolutions without re-training or post-processing. Case studies on both energy demand and supply data show that the forecasts from our proposed method can provide accurate information in both time and frequency domains. Across the resolutions, the forecasts also demonstrate high consistency. More importantly, we explore the operational effects of our produced forecasts in the day-ahead and intra-day energy scheduling. The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed. Significant operational benefits are obtained.
Recurrent Partial Kernel Network for Efficient Optical Flow Estimation
Authors: Morimitsu, Henrique; Zhu, Xiaobin; Ji, Xiangyang; Yin, Xu-Cheng
Date: 2024-02-01 00:00:00
Proceeding: The 38th Annual AAAI Conference on Artificial Intelligence (AAAI) 2024 2
Tasks: Optical Flow Estimation

Optical flow estimation is a challenging task consisting of predicting per-pixel motion vectors between images. Recent methods have employed larger and more complex models to improve the estimation accuracy. However, this impacts the widespread adoption of optical flow methods and makes it harder to train more general models since the optical flow data is hard to obtain. This paper proposes a small and efficient model for optical flow estimation. We design a new spatial recurrent encoder that extracts discriminative features at a significantly reduced size. Unlike standard recurrent units, we utilize Partial Kernel Convolution (PKConv) layers to produce variable multi-scale features with a single shared block. We also design efficient Separable Large Kernels (SLK) to capture large context information with low computational cost. Experiments on public benchmarks show that we achieve state-of-the-art generalization performance while requiring significantly fewer parameters and memory than competing methods. Our model ranks first in the Spring benchmark without finetuning, improving the results by over 10% while requiring an order of magnitude fewer FLOPs and over four times less memory than the following published method without finetuning.
An Expert System to Diagnose Pneumonia Using Fuzzy Logic
Authors: Leila Akramian Arani, Frahnaz Sadoughi, Mustafa Langarizadeh
Date: 2019-06-27 00:00:00
Proceeding: Research paper 2019 6

Introduction: Pneumonia is the most common and widespread killing disease of respiratory system
which is difficult to diagnose due to identical clinical signs of respiratory system. Aim: In this research,
to diagnose this, a structure of a fuzzy expert system has been oﬀered. This is done in order to help
general physicians and the patients make decision and also diﬀerentiate among chronic bronchitis,
tuberculosis, asthma, embolism, lung cancer. Methods: This system has been created using fuzzy
expert system and it has been created in 4 stages: defnition of knowledge system, design of knowledge
system, implementation of system, system testing using prototype life cycle methodology. Results:
The system has 97 percent sensitivity, 85 percent specifcity, 93 percent accuracy to diagnose the
disease. Conclusion: Framework of the knowledge of specialist physicians using fuzzy model and
its rules can help diagnose the disease correctly.
HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset
Authors: Gašper Podobnik, Primož Strojan, Primož Peterlin, Bulat Ibragimov, Tomaž Vrtovec
Date: 2023-01-03 00:00:00
Proceeding: Medical Physics 2023 1
Tasks: Benchmarking, Computed Tomography (CT), Segmentation

Purpose
For the cancer in the head and neck (HaN), radiotherapy (RT) represents an important treatment modality. Segmentation of organs-at-risk (OARs) is the starting point of RT planning, however, existing approaches are focused on either computed tomography (CT) or magnetic resonance (MR) images, while multimodal segmentation has not been thoroughly explored yet. We present a dataset of CT and MR images of the same patients with curated reference HaN OAR segmentations for an objective evaluation of segmentation methods.

Acquisition and validation methods
The cohort consists of HaN images of 56 patients that underwent both CT and T1-weighted MR imaging for image-guided RT. For each patient, reference segmentations of up to 30 OARs were obtained by experts performing manual pixel-wise image annotation. By maintaining the distribution of patient age and gender, and annotation type, the patients were randomly split into training Set 1 (42 cases or 75%) and test Set 2 (14 cases or 25%). Baseline auto-segmentation results are also provided by training the publicly available deep nnU-Net architecture on Set 1, and evaluating its performance on Set 2.

Data format and usage notes
The data are publicly available through an open-access repository under the name HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Dataset. Images and reference segmentations are stored in the NRRD file format, where the OAR filenames correspond to the nomenclature recommended by the American Association of Physicists in Medicine, and OAR and demographics information is stored in separate comma-separated value  files.

Potential applications
The HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched in parallel with the dataset release to promote the development of automated techniques for OAR segmentation in the HaN. Other potential applications include out-of-challenge algorithm development and benchmarking, as well as external validation of the developed algorithms.
Minuet: Accelerating 3D Sparse Convolutions on GPUs
Authors: Jiacheng Yang, Christina Giannoula, Jun Wu, Mostafa Elhoushi, James Gleeson, Gennady Pekhimenko
Date: 2023-12-01 00:00:00
Tasks: GPU

Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point cloud by only allowing outputs to specific locations. To efficiently compute SC, prior SC engines first use hash tables to build a kernel map that stores the necessary General Matrix Multiplication (GEMM) operations to be executed (Map step), and then use a Gather-GEMM-Scatter process to execute these GEMM operations (GMaS step). In this work, we analyze the shortcomings of prior state-of-the-art SC engines, and propose Minuet, a novel memory-efficient SC engine tailored for modern GPUs. Minuet proposes to (i) replace the hash tables used in the Map step with a novel segmented sorting double-traversed binary search algorithm that highly utilizes the on-chip memory hierarchy of GPUs, (ii) use a lightweight scheme to autotune the tile size in the Gather and Scatter operations of the GMaS step, such that to adapt the execution to the particular characteristics of each SC layer, dataset, and GPU architecture, and (iii) employ a padding-efficient GEMM grouping approach that reduces both memory padding and kernel launching overheads. Our evaluations show that Minuet significantly outperforms prior SC engines by on average $1.74\times$ (up to $2.22\times$) for end-to-end point cloud network executions. Our novel segmented sorting double-traversed binary search algorithm achieves superior speedups by $15.8\times$ on average (up to $26.8\times$) over prior SC engines in the Map step. The source code of Minuet is publicly available at https://github.com/UofT-EcoSystem/Minuet.
SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction
Authors: Ali Saeizadeh, Douglas Schonholtz, Daniel Uvaydov, Raffaele Guida, Emrecan Demirors, Pedram Johari, Jorge M. Jimenez, Joseph S. Neimat, Tommaso Melodia
Date: 2024-01-12 00:00:00
Tasks: Seizure prediction, Specificity

In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems have been developed that can notify such patients of an impending seizure, allowing them to take precautionary measures. SeizNet leverages DL techniques and combines data from multiple recordings, specifically intracranial electroencephalogram (iEEG) and electrocardiogram (ECG) sensors, that can significantly improve the specificity of seizure prediction while preserving very high levels of sensitivity. SeizNet DL algorithms are designed for efficient real-time execution at the edge, minimizing data privacy concerns, data transmission overhead, and power inefficiencies associated with cloud-based solutions. Our results indicate that SeizNet outperforms traditional single-modality and non-personalized prediction systems in all metrics, achieving up to 99% accuracy in predicting seizure, offering a promising new avenue in refractory epilepsy treatment.
APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding
Authors: Mingdao Liu, Aohan Zeng, Bowen Wang, Peng Zhang, Jie Tang, Yuxiao Dong
Date: 2024-01-12 00:00:00

The massive adoption of large language models (LLMs) demands efficient deployment strategies. However, the auto-regressive decoding process, which is fundamental to how most LLMs generate text, poses challenges to achieve efficient serving. In this work, we introduce a parallel auto-regressive generation method. By instruct-tuning on general domain data that contains hierarchical structures, we enable LLMs to independently plan their generation process and perform auto-parallel auto-regressive (APAR) generation, significantly reducing the number of generation steps. APAR alone can achieve up to 2x speed-up, and when combined with speculative decoding, the speed-up can reach up to 4x. In addition, APAR reduces the key-value cache consumption and attention computation during generation. This leads to a throughput increase of 20-70% and a latency reduce of 20-35% in high-throughput scenarios, compared to state-of-the-art serving frameworks.
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction
Authors: Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera, Antonio M. Lopez
Date: 2024-01-12 00:00:00
Tasks: Autonomous Driving, Prediction, Synthetic Data Generation

Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe and comfortable maneuvers. Creating accurate and fast models that predict such intentions from sequential images is challenging. A factor contributing to this is the lack of datasets with diverse crossing and non-crossing (C/NC) scenarios. We address this scarceness by introducing a framework, named ARCANE, which allows programmatically generating synthetic datasets consisting of C/NC video clip samples. As an example, we use ARCANE to generate a large and diverse dataset named PedSynth. We will show how PedSynth complements widely used real-world datasets such as JAAD and PIE, so enabling more accurate models for C/NC prediction. Considering the onboard deployment of C/NC prediction models, we also propose a deep model named PedGNN, which is fast and has a very low memory footprint. PedGNN is based on a GNN-GRU architecture that takes a sequence of pedestrian skeletons as input to predict crossing intentions.
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
Authors: Peter Hase, Mohit Bansal, Peter Clark, Sarah Wiegreffe
Date: 2024-01-12 00:00:00
Tasks: General Knowledge, In-Context Learning, Question Answering

How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the scalable oversight problem and has drawn increasing attention as language models have continually improved. In this paper, we present the surprising conclusion that current pretrained language models often generalize relatively well from easy to hard data, even performing as well as oracle models finetuned on hard data. We demonstrate this kind of easy-to-hard generalization using simple finetuning methods like in-context learning, linear classifier heads, and QLoRA for seven different measures of datapoint hardness, including six empirically diverse human hardness measures (like grade level) and one model-based measure (loss-based). Furthermore, we show that even if one cares most about model performance on hard data, it can be better to collect easy data rather than hard data for finetuning, since hard data is generally noisier and costlier to collect. Our experiments use open models up to 70b in size and four publicly available question-answering datasets with questions ranging in difficulty from 3rd grade science questions to college level STEM questions and general-knowledge trivia. We conclude that easy-to-hard generalization in LMs is surprisingly strong for the tasks studied. Our code is available at: https://github.com/allenai/easy-to-hard-generalization