pwc-archive/papers-with-abstracts huggingface dataset:
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface
Authors: Guodong Yin, Mufeng Zhou, Yiming Chen, Wenjun Tang, Zekun Yang, Mingyen Lee, Xirui Du, Jinshan Yue, Jiaxin Liu, Huazhong Yang, Yongpan Liu, Xueqing Li
Date: 2022-11-23 00:00:00
Tasks: Quantization

Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck. Computing-in-memory (CiM) is a promising mitigation approach by enabling parallel in-situ multiply-accumulate (MAC) operations within the memory with support from the peripheral interface and datapath. SRAM-based charge-domain CiM (CD-CiM) has shown its potential of enhanced power efficiency and computing accuracy. However, existing SRAM-based CD-CiM faces scaling challenges to meet the throughput requirement of high-performance multi-bit-quantization applications. This paper presents an SRAM-based high-throughput ReLU-optimized CD-CiM macro. It is capable of completing MAC and ReLU of two signed 8b vectors in one CiM cycle with only one A/D conversion. Along with non-linearity compensation for the analog computing and A/D conversion interfaces, this work achieves 51.2GOPS throughput and 10.3TOPS/W energy efficiency, while showing 88.6% accuracy in the CIFAR-10 dataset.
Identifying mechanisms driving the early response of triple negative breast cancer patients to neoadjuvant chemotherapy using a mechanistic model integrating in vitro and in vivo imaging data
Authors: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Vito Quaranta, Darren R. Tyson, Thomas E. Yankeelov
Date: 2022-12-08 00:00:00
Tasks: Sensitivity

Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To this end, a promising approach consists of obtaining \textsl{in silico} personalized forecasts of tumor response to NAC \textsl{via} computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acquired early during NAC. Here, we present a model featuring the essential mechanisms of TNBC growth and response to NAC, including an explicit description of drug pharmacodynamics and pharmacokinetics. As longitudinal \textsl{in vivo} MRI data for model calibration is limited, we perform a sensitivity analysis to identify the model mechanisms driving the response to two NAC drug combinations: doxorubicin with cyclophosphamide, and paclitaxel with carboplatin. The model parameter space is constructed by combining patient-specific MRI-based \textsl{in silico} parameter estimates and \textit{in vitro} measurements of pharmacodynamic parameters obtained using time-resolved microscopy assays of several TNBC lines. The sensitivity analysis is run in two MRI-based scenarios corresponding to a well-perfused and a poorly-perfused tumor. Out of the 15 parameters considered herein, only the baseline tumor cell net proliferation rate along with the maximum concentrations and effects of doxorubicin, carboplatin, and paclitaxel exhibit a relevant impact on model forecasts (total effect index, $S_T>$0.1). These results dramatically limit the number of parameters that require \textsl{in vivo} MRI-constrained calibration, thereby facilitating the clinical application of our model.
Encrypted machine learning of molecular quantum properties
Authors: Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Date: 2022-12-05 00:00:00
Tasks: Federated Learning, molecular representation

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. However, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs.
Device identification using optimized digital footprints
Authors: Rajarshi Roy Chowdhury, Azam Che Idris, Pg Emeroylariffion Abas
Date: 2022-12-04 00:00:00
Tasks: Attribute

The rapidly increasing number of internet of things (IoT) and non-IoT devices has imposed new security challenges to network administrators. Accurate device identification in the increasingly complex network structures is necessary. In this paper, a device fingerprinting (DFP) method has been proposed for device identification, based on digital footprints, which devices use for communication over a network. A subset of nine features have been selected from the network and transport layers of a single transmission control protocol/internet protocol packet based on attribute evaluators in Weka, to generate device-specific signatures. The method has been evaluated on two online datasets, and an experimental dataset, using different supervised machine learning (ML) algorithms. Results have shown that the method is able to distinguish device type with up to 100% precision using the random forest (RF) classifier, and classify individual devices with up to 95.7% precision. These results demonstrate the applicability of the proposed DFP method for device identification, in order to provide a more secure and robust network.
A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification
Authors: Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon Chiu, Yutong Liu
Date: 2022-12-04 00:00:00

Privacy protection raises great attention on both legal levels and user awareness. To protect user privacy, countries enact laws and regulations requiring software privacy policies to regulate their behavior. However, privacy policies are written in natural languages with many legal terms and software jargon that prevent users from understanding and even reading them. It is desirable to use NLP techniques to analyze privacy policies for helping users understand them. Furthermore, existing datasets ignore law requirements and are limited to English. In this paper, we construct the first Chinese privacy policy dataset, namely CA4P-483, to facilitate the sequence labeling tasks and regulation compliance identification between privacy policies and software. Our dataset includes 483 Chinese Android application privacy policies, over 11K sentences, and 52K fine-grained annotations. We evaluate families of robust and representative baseline models on our dataset. Based on baseline performance, we provide findings and potential research directions on our dataset. Finally, we investigate the potential applications of CA4P-483 combing regulation requirements and program analysis.
The Impact of Socioeconomic Factors on Health Disparities
Authors: Krish Khanna, Jeffrey Lu, Jay Warrier
Date: 2022-12-01 00:00:00

High-quality healthcare in the US can be cost-prohibitive for certain socioeconomic groups. In this paper, we examined data from the US Census and the CDC to determine the degree to which specific socioeconomic factors correlate with both specific and general health metrics. We employed visual analysis to find broad trends and predictive modeling to identify more complex relationships between variables. Our results indicate that certain socioeconomic factors, like income and educational attainment, are highly correlated with aggregate measures of health.
A Characterization of Maximum Nash Welfare for Indivisible Goods
Authors: Warut Suksompong
Date: 2022-12-08 00:00:00
Tasks: Fairness

In the allocation of indivisible goods, the maximum Nash welfare (MNW) rule, which chooses an allocation maximizing the product of the agents' utilities, has received substantial attention for its fairness. We characterize MNW as the only additive welfarist rule that satisfies envy-freeness up to one good. Our characterization holds even in the simplest setting of two agents.
Closed loop BCI System for Cybathlon 2020
Authors: Csaba Köllőd, András Adolf, Gergely Márton, Moutz Wahdow, Ward Fadel, István Ulbert
Date: 2022-12-08 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

We present our Brain-Computer Interface (BCI) System, developed for the BCI discipline of Cybathlon 2020 competition. In the BCI discipline, subjects with tetraplegia are required to control a computer game with mental commands. The absolute of the Fast-Fourier Transformation amplitude was calculated as a feature (FFTabs) from one-second-long electroencephalographic (EEG) signals. To extract the final features, we introduced two methods, namely the Feature Average, where the average of the FFTabs for a specific frequency band was calculated, and the Feature Range, which was based on generating multiple Feature Averages for non-overlapping 2 Hz wide frequency bins. The resulting features were fed to a Support Vector Machine classifier. The algorithms were tested on the PhysioNet database and our dataset, which contains 16 offline experiments recorded with 2 tetraplegic subjects. 27 gameplay trials (out of 59) with our tetraplegic subjects reached the 240-second qualification time limit. The Feature Average of canonical frequency bands (alpha, beta, gamma, and theta) were compared with our suggested range30 and range40 methods. On the PhysioNet dataset, the range40 method combined with an ensemble SVM classifier significantly reached the highest accuracy level (0.4607), with a 4-class classification, and outperformed the state-of-the-art EEGNet.
DECO2 An Open-source Energy System Decarbonisation Planning Software Including Negative Emissions Technologies
Authors: Purusothmn Nair S. Bhasker Nair, Raymond R. Tan, Dominic C. Y. Foo, Disni Gamaralalage, Michael Short
Date: 2022-12-08 00:00:00
Tasks: Management

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meet the net-zero target by year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. Results demonstrated the potential of fuel substitutions for low-carbon alternatives in existing coal and natural gas power plants. Additionally, once NETs are mature and are available for commercial deployment, their deployment is crucial in aiding CO2 removal in minimal investment costs scenarios. Overall, the newly developed open-source software demonstrates the importance of determining the optimal deployment of mitigation technologies in meeting climate change targets for each period.
Investigation of Minerals Using Hyperspectral Satellite Imagery in Bangladesh
Authors: Nazmul Hasan, Kazi Mahmudul Hasan, Md. Tahsinul Islam, Shahnewaz Siddique
Date: 2022-12-08 00:00:00

Mineral identification using remote sensing technologies is becoming more dominant in this field since it saves time by demonstrating a more effective way for land resources survey. In such remote sensing technologies, hyperspectral remote sensing (HSRS) technology has increased gradually for its efficient manner. This technology is usually used from an airborne platform, i.e., satellite. Hence, satellite imagery remote sensing technology is now more capable of providing accuracy in mineral identification, and mapping. Hyperspectral satellite imagery can identify minerals more accurately compared to traditional technologies in remote sensing by constructing a complete reflectance of the spectrum from each pixel with its advanced imaging sensor. Bangladesh is a developing country with an area of 1,50,000 square kilometers located in Southeast Asia. Though it is a small country, it is enriched with several mineral resources through rivers, forests, hills, and the Bay of Bengal. In this study, hyperspectral imaging technology is employed on some major identical areas (Maheshkhali, Netrokona, Panchagarh, and Patuakhali) of Bangladesh to identify minerals there. As there are no studies done in Bangladesh using hyperspectral imaging yet, it is a good opportunity to explore the potentiality of HS imagery in this field. In this study, the FLAASH (Fast Line-of-sight Atmospheric Analysis) module with necessary parameter settings is used to filter the data, and finally, mineral identification is done by the spectral matched filtering method. Our investigation resulted in finding some potential minerals in those areas including Stariolite, Diasphore, Zircon, Alunite, Quartz, and so on. This indicates that there still is enormous potential for further exploration of minerals in Bangladesh by Hyperspectral Satellite Imagery.
On Attack Detection and Identification for the Cyber-Physical System using Lifted System Model
Authors: Dawei Sun, Minhyun Cho, Inseok Hwang
Date: 2022-12-08 00:00:00

Motivated by the safety and security issues related to cyber-physical systems with potentially multi-rate, delayed, and nonuniformly sampled measurements, we investigate the attack detection and identification using the lifted system model in this paper. Attack detectability and identifiability based on the lifted system model are formally defined and rigorously characterized in a novel approach. The method of checking detectability is discussed, and a residual design problem for attack detection is formulated in a general way. For attack identification, we define and characterize it by generalizing the concept of mode discernibility for switched systems, and a method for identifying the attack is discussed based on the theoretical analysis. An illustrative example of an unmanned aircraft system (UAS) is provided to validate the main results.
Scalable, low-cost, and versatile system design for air pollution and traffic density monitoring and analysis
Authors: Thinh Gia Tran, Dat Thanh Vo, Long Chau Tran, Hoang Viet Pham, Chuong Dinh Le, An Dinh Le, Duy Anh Pham, Hien Bich Vo
Date: 2022-12-08 00:00:00

Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.
Electromagnetic Environment Analysis of High-Power Wireless Charging Device
Authors: Zhengyang Zhang, Zhihui Liu, Wenjin Zhang, Rui Zhang, Xiang Xiao
Date: 2022-12-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Objective Aiming at the problems of many interference factors in the electromagnetic radiation simulation of electric vehicles, a field measurement scheme for charging devices is designed. Through the monitoring of wireless charging equipment, the radiation level and distribution of the electric field value and magnetic field value around the charging equipment is explored, to analyze the influence law of the electromagnetic environment. Method This paper introduces the principle and development status of electric vehicle charging, and analyzes the classification and methods of wireless charging. In this paper, the electric field and magnetic field of cars and minibuses are monitored, at positions such as: around the body, attenuation section, inside the car and inside the minibus. Result The range of electric field strength is 0.9 V/m to 48.1 V/m for cars, and 0.8 V/m to 74.7 V/m for minibuses. The electric field strength decays rapidly with the increase of the distance from the vehicle body, and the law is obvious. The range of magnetic induction intensity is 0.12{\mu}T-12.70{\mu}T for cars, and 0.15{\mu}T-27.06{\mu}T for minibuses. The magnetic induction intensity decays rapidly with the increase of the distance from the vehicle body, and the law is obvious. Conclusion This paper explores the radiation level and distribution of the electromagnetic field around this type of charging equipment. It is recommended that manufacturers of wireless charging devices for electric vehicles strengthen research on electromagnetic radiation shielding and take corresponding measures to control the level of electromagnetic radiation in areas accessible to the public.
No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles
Authors: Wenhao Yu, Chengxiang Zhao, Jiaxin Liu, Yingkai Yang, Xiaohan Ma, Jun Li, Weida Wang, Hong Wang, Ding Zhao, Xiaosong Hu
Date: 2022-12-08 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making

Defined traffic laws must be respected by all vehicles. However, it is essential to know which behaviors violate the current laws, especially when a responsibility issue is involved in an accident. This brings challenges of digitizing human-driver-oriented traffic laws and monitoring vehicles' behaviors continuously. To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles. This paper introduces a layered trigger domain-based traffic law digitization architecture with digitization-classified discussions and detailed atomic propositions for online monitoring. The principal laws on a highway and at an intersection are taken as examples, and the corresponding logic and atomic propositions are introduced in detail. Finally, the digitized traffic laws are verified on the Chinese highway and intersection datasets, and defined thresholds are further discussed according to the driving behaviors in the considered dataset. This study can help manufacturers and the government in defining specifications and laws and can also be used as a useful reference in traffic laws compliance decision-making. Source code is available on https://github.com/SOTIF-AVLab/DOTL.
Safe and Efficient Switching Controller Design for Partially Observed Linear-Gaussian Systems
Authors: Yiwen Lu, Yilin Mo
Date: 2022-12-08 00:00:00

Switching control strategies that unite a potentially high-performance but uncertified controller and a stabilizing albeit conservative controller are shown to be able to balance safety with efficiency, but have been less studied under partial observation of state. To address this gap, we propose a switching control strategy for partially observed linear-Gaussian systems with provable performance guarantees. We show that the proposed switching strategy is both safe and efficient, in the sense that: (1) the linear-quadratic cost of the system is always bounded even if the original uncertified controller is destabilizing; (2) in the case when the uncertified controller is stabilizing, the performance loss induced by the conservativeness of switching converges super-exponentially to zero. The effectiveness of the switching strategy is also demonstrated via numerical simulation on the Tennessee Eastman Process.
Analyzing At-Scale Distribution Grid Response to Extreme Temperatures
Authors: Sarmad Hanif, Monish Mukherjee, Shiva Poudel, Rohit A Jinsiwale, Min Gyung Yu, Trevor Hardy, Hayden Reeve
Date: 2022-12-07 00:00:00

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit1. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.
Optimal Model Selection in RDD and Related Settings Using Placebo Zones
Authors: Nathan Kettlewell, Peter Siminski
Date: 2022-12-08 00:00:00
Tasks: Model Selection, regression

We propose a new model-selection algorithm for Regression Discontinuity Design, Regression Kink Design, and related IV estimators. Candidate models are assessed within a 'placebo zone' of the running variable, where the true effects are known to be zero. The approach yields an optimal combination of bandwidth, polynomial, and any other choice parameters. It can also inform choices between classes of models (e.g. RDD versus cohort-IV) and any other choices, such as covariates, kernel, or other weights. We outline sufficient conditions under which the approach is asymptotically optimal. The approach also performs favorably under more general conditions in a series of Monte Carlo simulations. We demonstrate the approach in an evaluation of changes to Minimum Supervised Driving Hours in the Australian state of New South Wales. We also re-evaluate evidence on the effects of Head Start and Minimum Legal Drinking Age. Our Stata commands implement the procedure and compare its performance to other approaches.