pwc-archive/papers-with-abstracts huggingface dataset:
Variable length genetic algorithm with continuous parameters optimization of beam layout in proton therapy
Authors: François Smekens, Nicolas Freud, Bruno Sixou, Guillaume Beslon, Jean M Létang
Date: 2022-05-17 00:00:00

Proton therapy is a modality in fast development. Characterized by a maximum dose deposition at the end of the proton trajectory followed by a sharp fall-off, proton beams can deliver a highly conformal dose to the tumor while sparing organs at risk and surrounding healthy tissues. New treatment planning systems based on spot scanning techniques can now propose multi-field optimization. However, in most cases, this optimization only processes the field fluences whereas the choice of ballistics (field geometry) is left to the oncologist and medical physicist. In this work, we investigate a new optimization framework based on a genetic approach. This tool is intended to explore new irradiation schemes and to evaluate the potential of actual or future irradiation systems. We propose to optimize simultaneously the target points and beam incidence angles in a continuous manner and with a variable number of beams. No \textit{a priori} technological constraints are taken into account, \textit{i.e.}~the beam energy values, incidence directions and target points are free parameters. The proposed algorithm is based on a modified version of classical genetic operators: mutation, crossover and selection. We use the real coding associated with random perturbations of the parameters to obtain a continuous variation of the potential solutions. We also introduce a perturbation in the exchange points of the crossover to allow variations of the number of beams. These variations are controlled by introducing a beam fluence lower limit. In this paper, we present a complete description of the algorithm and of its behaviour in an elementary test case. The proposed method is finally assessed in a clinically-realistic test case.
Towards the optimization of ballistics in proton therapy using genetic algorithms: implementation issues
Authors: François Smekens, Nicolas Freud, Bruno Sixou, Guillaume Beslon, Jean M Létang
Date: 2022-05-17 00:00:00

The dose delivered to the planning target volume by proton beams is highly conformal, sparing organs at risk and normal tissues. New treatment planning systems adapted to spot scanning techniques have been recently proposed to simultaneously optimize several fields and thus improve dose delivery. In this paper, we investigate a new optimization framework based on a genetic algorithm approach. This tool is intended to make it possible to explore new schemes of treatment delivery, possibly with future enhanced technologies. The optimization framework is designed to be versatile and to account for many degrees of freedom, without any {\it a priori} technological constraint. To test the behavior of our algorithm, we propose in this paper, as an example, to optimize beam fluences, target points and irradiation directions at the same time. The proposed optimization routine takes typically into account several thousands of spots of fixed size. The evolution is carried out by the three standard genetic operators: mutation, crossover and selection. The figure-of-merit (or fitness) is based on an objective function relative to the dose prescription to the tumor and to the limits set for organs at risk and normal tissues. Fluence optimization is carried out via a specific scheme based on a plain gradient with analytical solution. Several specific genetic algorithm issues are addressed: (i) the mutation rate is tuned to balance the search and selection forces, (ii) the initial population is selected using a bootstrap technique and (iii) to scale down the computation time, dose calculations are carried out with a fast analytical ray tracing method and are multi-threaded. In this paper implementation issues of the optimization framework are thoroughly described. The behavior of the proposed genetic algorithm is illustrated in both elementary and clinically-realistic test cases.
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
Authors: Gautam Kamath, Argyris Mouzakis, Vikrant Singhal
Date: 2022-05-17 00:00:00
Tasks: LEMMA

We prove new lower bounds for statistical estimation tasks under the constraint of $(\varepsilon, \delta)$-differential privacy. First, we provide tight lower bounds for private covariance estimation of Gaussian distributions. We show that estimating the covariance matrix in Frobenius norm requires $\Omega(d^2)$ samples, and in spectral norm requires $\Omega(d^{3/2})$ samples, both matching upper bounds up to logarithmic factors. The latter bound verifies the existence of a conjectured statistical gap between the private and the non-private sample complexities for spectral estimation of Gaussian covariances. We prove these bounds via our main technical contribution, a broad generalization of the fingerprinting method to exponential families. Additionally, using the private Assouad method of Acharya, Sun, and Zhang, we show a tight $\Omega(d/(\alpha^2 \varepsilon))$ lower bound for estimating the mean of a distribution with bounded covariance to $\alpha$-error in $\ell_2$-distance. Prior known lower bounds for all these problems were either polynomially weaker or held under the stricter condition of $(\varepsilon, 0)$-differential privacy.
Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models
Authors: Arthur Câmara, Claudia Hauff
Date: 2022-05-17 00:00:00

When training neural rankers using Large Language Models, it's expected that a practitioner would make use of multiple GPUs to accelerate the training time. By using more devices, deep learning frameworks, like PyTorch, allow the user to drastically increase the available VRAM pool, making larger batches possible when training, therefore shrinking training time. At the same time, one of the most critical processes, that is generally overlooked when running data-hungry models, is how data is managed between disk, main memory and VRAM. Most open source research implementations overlook this memory hierarchy, and instead resort to loading all documents from disk to main memory and then allowing the framework (e.g., PyTorch) to handle moving data into VRAM. Therefore, with the increasing sizes of datasets dedicated to IR research, a natural question arises: s this the optimal solution for optimizing training time? We here study how three different popular approaches to handling documents for IR datasets behave and how they scale with multiple GPUs. Namely, loading documents directly into memory, reading documents directly from text files with a lookup table and using a library for handling IR datasets (ir_datasets) differ, both in performance (i.e. samples processed per second) and memory footprint. We show that, when using the most popular libraries for neural ranker research (i.e. PyTorch and Hugging Face's Transformers), the practice of loading all documents into main memory is not always the fastest option and is not feasible for setups with more than a couple GPUs. Meanwhile, a good implementation of data streaming from disk can be faster, while being considerably more scalable. We also show how popular techniques for improving loading times, like memory pining, multiple workers, and RAMDISK usage, can reduce the training time further with minor memory overhead.
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Authors: Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, Hongxia Yang
Date: 2022-05-17 00:00:00
Tasks: Computational Efficiency, Conversational Recommendation, Explanation Generation, Language Modelling, Recommendation Systems, Retrieval

Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production. The mainstream approach so far is to develop individual algorithms for each domain and each task. In this paper, we explore the possibility of developing a unified foundation model to support \emph{open-ended domains and tasks} in an industrial recommender system, which may reduce the demand on downstream settings' data and can minimize the carbon footprint by avoiding training a separate model from scratch for every task. Deriving a unified foundation is challenging due to (i) the potentially unlimited set of downstream domains and tasks, and (ii) the real-world systems' emphasis on computational efficiency. We thus build our foundation upon M6, an existing large-scale industrial pretrained language model similar to GPT-3 and T5, and leverage M6's pretrained ability for sample-efficient downstream adaptation, by representing user behavior data as plain texts and converting the tasks to either language understanding or generation. To deal with a tight hardware budget, we propose an improved version of prompt tuning that outperforms fine-tuning with negligible 1\% task-specific parameters, and employ techniques such as late interaction, early exiting, parameter sharing, and pruning to further reduce the inference time and the model size. We demonstrate the foundation model's versatility on a wide range of tasks such as retrieval, ranking, zero-shot recommendation, explanation generation, personalized content creation, and conversational recommendation, and manage to deploy it on both cloud servers and mobile devices.
PaddleRec
Authors: Wenhui Zhang, Zhihua Wu, Haofeng Yin
Date: 2021-11-19 00:00:00
Proceeding: WSDM 2021 11
Tasks: Click-Through Rate Prediction, Recommendation Systems

A quick start tool of search & recommendation algorithm based on PaddlePaddle  
A complete solution of recommendation system for beginners, developers and researchers.  
Recommendation algorithm library including content-understanding, match, recall, rank, multi-task, re-rank etc.
Harmonic analysis of the arctangent function regarding the angular error introduced by superimposed Fourier series for application in sine/cosine angle encoders
Authors: Stefan Kuntz, Robert Dauth, Gerald Gerlach, Peter Ott, Sina Fella
Date: 2022-05-17 00:00:00

We present a rigorous analytical method for harmonic analysis of the angular error of rotary and linear encoders with sine/cosine output signals in quadrature that are distorted by superimposed Fourier series. To calculate the angle from measured sine and cosine encoder channels in quadrature, the arctangent function is commonly used. The hence non-linear relation between raw signals and calculated angle -- often thought of as a black box -- complicates the estimation of the angular error and its harmonic decomposition. By means of a Taylor series expansion of the harmonic amplitudes, our method allows for quantification of the impact of harmonic signal distortions on the angular error in terms of harmonic order, magnitude and phase, including an upper bound on the remaining error term -- without numerical evaluation of the arctangent function. The same approximation is achieved with an intuitive geometric approximation in the complex plane, validating the results. Additionally, interaction effects between harmonics in the signals are considered by higher-order Taylor expansion. The approximations show an excellent agreement with the exact calculation in numerical examples even in case of large distortion amplitudes, leading to practicable estimates for the angular error decomposition.
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION
Authors: Xingjian Du, Ke Chen, Zijie Wang, Bilei Zhu, Zejun Ma
Date: 2022-04-27 00:00:00
Proceeding: ICASSP 2022 4
Tasks: Cover song identification, Dimensionality Reduction

Convolutional neural network (CNN)-based methods have
dominated the recent research of cover song identification
(CSI). A typical example is the ByteCover system we proposed, which has achieved state-of-the-art results on all the
mainstream datasets of CSI. In this paper, we propose an upgraded version of ByteCover, termed ByteCover2, which further improves ByteCover in both identification performance
and efficiency. Compared with ByteCover, ByteCover2 is
designed with an additional PCA-FC module, which integrates the capability of principal component analysis (PCA)
and fully-connected (FC) neural network for dimensionality reduction of the audio embedding, allowing ByteCover2
to perform CSI in a more precise and efficient way. We
evaluated ByteCover2 on multiple datasets in different dimension sizes and training settings, where ByteCover2 beat
all the compared methods including ByteCover, even with a
dimension size of 128, which is 15 times smaller than that of
ByteCover.
Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis
Authors: Mohammed Alser, Joel Lindegger, Can Firtina, Nour Almadhoun, Haiyu Mao, Gagandeep Singh, Juan Gomez-Luna, Onur Mutlu
Date: 2022-05-16 00:00:00
Tasks: scientific discovery

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent. The analysis script and data used in our experimental evaluation are available at: https://github.com/CMU-SAFARI/Molecules2Variations
Power and Skew Reduction Using Resonant Energy Recycling in 14-nm FinFET Clocks
Authors: Dhandeep Challagundla, Mehedi Galib, Ignatius Bezzam, Riadul Islam
Date: 2022-05-16 00:00:00

As the demand for high-performance microprocessors increases, the circuit complexity and the rate of data transfer increases resulting in higher power consumption. We propose a clocking architecture that uses a series LC resonance and inductor matching technique to address this bottleneck. By employing pulsed resonance, the switching power dissipated is recycled back. The inductor matching technique aids in reducing the skew, increasing the robustness of the clock network. This new resonant architecture saves over 43% power and 91% skew clocking a range of 1--5 GHz, compared to a conventional primary-secondary flip-flop-based CMOS architecture.
Cyber Risk Assessment for Capital Management
Authors: Wing Fung Chong, Runhuan Feng, Hins Hu, Linfeng Zhang
Date: 2022-05-17 00:00:00
Tasks: Decision Making, Management

This paper introduces a two-pillar cyber risk management framework to address the pervasive challenges in managing cyber risk. The first pillar, cyber risk assessment, combines insurance frequency-severity models with cybersecurity cascade models to capture the unique nature of cyber risk. The second pillar, cyber capital management, facilitates informed allocation of capital for a balanced cyber risk management strategy, including cybersecurity investments, insurance coverage, and reserves. A case study, based on historical cyber incident data and realistic assumptions, demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies with competing objectives in cyber risk management. In addition, sensitivity analysis highlights the dependence of the optimal strategy on factors such as the price of cybersecurity controls and their effectiveness. The framework's implementation across a diverse range of companies yields general insights on cyber risk management.
Btech thesis report on adversarial attack detection and purification of adverserially attacked images
Authors: Dvij Kalaria
Date: 2022-05-09 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, BIG-bench Machine Learning

This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regression etc. By training, weights are adjusted such that the model performs the task well not only on training examples judged by a certain metric but has an excellent ability to generalize on other unseen examples as well which are typically called the test data. Despite the huge success of machine learning models on a wide range of tasks, security has received a lot less attention along the years. Robustness along various potential cyber attacks also should be a metric for the accuracy of the machine learning models. These cyber attacks can potentially lead to a variety of negative impacts in the real world sensitive applications for which machine learning is used such as medical and transportation systems. Hence, it is a necessity to secure the system from such attacks. Int this report, I focus on a class of these cyber attacks called the adversarial attacks in which the original input sample is modified by small perturbations such that they still look visually the same to human beings but the machine learning models are fooled by such inputs. In this report I discuss 2 novel ways to counter the adversarial attack using AutoEncoders, 1) by detecting the presence of adversaries and 2) purifying these adversaries to make target classification models robust against such attacks.
Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning
Authors: Laura Pryor, Jacob mallet, Rushit Dave, Naeem Seliya, Mounika Vanamala, Evelyn Sowells Boone
Date: 2022-05-07 00:00:00
Tasks: BIG-bench Machine Learning

The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement, and we evaluate the performance of different single-modal and multi-modal combinations of the two biometrics. Using two publicly available datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study uses seven common machine learning algorithms to evaluate performance. The algorithms used in the evaluation include Random Forest, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy rates reaching as high as 86%.
The Fairness of Machine Learning in Insurance: New Rags for an Old Man?
Authors: Laurence Barry, Arthur Charpentier
Date: 2022-05-17 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Since the beginning of their history, insurers have been known to use data to classify and price risks. As such, they were confronted early on with the problem of fairness and discrimination associated with data. This issue is becoming increasingly important with access to more granular and behavioural data, and is evolving to reflect current technologies and societal concerns. By looking into earlier debates on discrimination, we show that some algorithmic biases are a renewed version of older ones, while others show a reversal of the previous order. Paradoxically, while the insurance practice has not deeply changed nor are most of these biases new, the machine learning era still deeply shakes the conception of insurance fairness.
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Authors: Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
Date: 2022-05-05 00:00:00
Tasks: BIG-bench Machine Learning

Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible. Despite these benefits, the machine learning (ML) research community lacks well-developed norms around disclosing and discussing limitations. To address this gap, we conduct an iterative design process with 30 ML and ML-adjacent researchers to develop and test REAL ML, a set of guided activities to help ML researchers recognize, explore, and articulate the limitations of their research. Using a three-stage interview and survey study, we identify ML researchers' perceptions of limitations, as well as the challenges they face when recognizing, exploring, and articulating limitations. We develop REAL ML to address some of these practical challenges, and highlight additional cultural challenges that will require broader shifts in community norms to address. We hope our study and REAL ML help move the ML research community toward more active and appropriate engagement with limitations.
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
Authors: Han Wang, Zhou Huang, Xiao Zhou, Ganmin Yin, Yi Bao, Yi Zhang
Date: 2022-05-05 00:00:00
Tasks: Representation Learning

Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches tend to rarely address three challenges: 1) how to represent the intricate semantic intentions of mobility inexpensively; 2) complex and weak spatial-temporal dependencies due to the sparsity and heterogeneity of the trajectory data; 3) route selection preferences and their correlation to driving behavior. In this paper, we propose a novel multimodal fusion model, DouFu, for trajectory representation joint learning, which applies multimodal learning and attention fusion module to capture the internal characteristics of trajectories. We first design movement, route, and global features generated from the trajectory data and urban functional zones and then analyze them respectively with the attention encoder or feed forward network. The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding. With the global semantic feature, DouFu produces a comprehensive embedding for each trajectory. We evaluate representations generated by our method and other baseline models on classification and clustering tasks. Empirical results show that DouFu outperforms other models in most of the learning algorithms like the linear regression and the support vector machine by more than 10%.