pwc-archive/papers-with-abstracts huggingface dataset:
SVM Classifier on Chip for Melanoma Detection
Authors: Shereen Afifi, Hamid GholamHosseini, Roopak Sinha
Date: 2021-08-26 00:00:00
Tasks: Classification

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM- based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.
Resource allocation method using tug-of-war-based synchronization
Authors: Song-Ju Kim, Hiroyuki Yasuda, Ryoma Kitagawa, Mikio Hasegawa
Date: 2021-08-19 00:00:00
Tasks: Scheduling

We propose a simple channel-allocation method based on tug-of-war (TOW) dynamics, combined with the time scheduling based on nonlinear oscillator synchronization to efficiently use of the space (channel) and time resources in wireless communications. This study demonstrates that synchronization groups, where each node selects a different channel, are non-uniformly distributed in phase space such that every distance between groups is larger than the area of influence. New type of self-organized spatiotemporal patterns can be formed for resource allocation according to channel rewards.
Towards model predictive control of supercritical CO2 cycles
Authors: Viv Bone, Michael Kearney, Ingo Jahn
Date: 2021-08-27 00:00:00
Tasks: model, Model Predictive Control, Scheduling

Control of non-condensing non-ideal-gas power cycles is challenging because their output power dynamics depend on complex system interactions, non-ideal-gas effects complicate turbomachinery behavior, and state constraints must be respected. This article presents a control methodology for these systems, comprising a control modeling approach and model predictive control (MPC) strategy. This methodology is demonstrated on the high-pressure side of a simple supercritical CO2 cycle power block, composed of a variable-speed compressor, heat exchanger, and fixed-speed turbine. The control model is developed by applying timescale-separation arguments to a high-fidelity simulation model and locally linearizing non-ideal-gas turbomachinery performance maps. MPC is implemented by linearizing the control model online at each sampling instant. Closed-loop simulations with a full-order gas-dynamics truth model demonstrate the effectiveness of the proposed control methodology. In response to load changes, the controller maintains high turbine inlet temperatures while achieving net power output ramp rates in excess of 100% of nameplate output per minute. The controller often acts at the intersection of motor torque, compressor surge, and turbine inlet temperature constraints, and performs well from 35 to 105% of nameplate capacity with no parameter scheduling. Good performance and fast update rates are obtained via online linearization. The results demonstrate the suitability of MPC for the supercritical CO2 cycle, and the proposed methodology is extensible to more complex cycle variants such as the recuperated and recompression cycle.
A Numerical Verification Framework for Differential Privacy in Estimation
Authors: Yunhai Han, Sonia Martínez
Date: 2021-08-27 00:00:00

This work proposes an algorithmic method to verify differential privacy for estimation mechanisms with performance guarantees. Differential privacy makes it hard to distinguish outputs of a mechanism produced by adjacent inputs. While obtaining theoretical conditions that guarantee differential privacy may be possible, evaluating these conditions in practice can be hard. This is especially true for estimation mechanisms that take values in continuous spaces, as this requires checking for an infinite set of inequalities. Instead, our verification approach consists of testing the differential privacy condition for a suitably chosen finite collection of events at the expense of some information loss. More precisely, our data-driven, test framework for continuous range mechanisms first finds a highly-likely, compact event set, as well as a partition of this event, and then evaluates differential privacy wrt this partition. This results into a type of differential privacy with high confidence, which we are able to quantify precisely. This approach is then used to evaluate the differential-privacy properties of the recently proposed $W_2$ Moving Horizon Estimator. We confirm its properties, while comparing its performance with alternative approaches in simulation.
A Three-terminal Non-Volatile Ferroelectric Switch with an Insulator-Metal Transition Channel
Authors: Jaykumar Vaidya, R S Surya Kanthi, Shamiul Alam, Nazmul Amin, Ahmedullah Aziz, Nikhil Shukla
Date: 2021-08-27 00:00:00

Ferroelectrics offer a promising materials platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET operation entails a fundamental trade-off between the read and the program operations. To overcome this trade-off, we propose in this work, a novel device, Mott-FeFET, that aims to replace the Silicon channel of the FeFET with VO2- a material that exhibits an electrically driven insulator-metal phase transition. The Mott-FeFET design, which demonstrates a (ferroelectric) polarization-dependent threshold voltage, enables the read current distinguishability (i.e., the ratio of current sensed when the Mott-FeFET is in state 1 and 0, respectively) to be independent of the program voltage. This enables the device to be programmed at low voltages without affecting the ability to sense/read the state of the device. Our work provides a pathway to realize low-voltage and energy-efficient non-volatile memory solutions.
Interpreting the Tilt-and-Torsion Method to Express Shoulder Joint Kinematics
Authors: Félix Chénier, Ilona Alberca, Arnaud Faupin, Dany H Gagnon
Date: 2021-08-24 00:00:00

Background: Kinematics is studied by practitioners and researchers in different fields of practice. It is therefore critically important to adhere to a taxonomy that explicitly describes positions and movements. However, current representation methods such as cardan and Euler angles fail to report shoulder angles in a way that is easily and correctly interpreted by practitioners, and that is free from numerical instability such as gimbal lock. Methods: In this paper, we comprehensively describe the recent Tilt-and-Torsion method and compare it to the Euler YXY method currently recommended by the International Society of Biomechanics. While using the same three rotations (plane of elevation, elevation, humeral rotation), the Tilt-and-Torsion method reports humeral rotation independently from the plane of elevation. We assess how it can be used to describe shoulder angles (1) in a simulated assessment of humeral rotation with the arm at the side, which constitutes a gimbal lock position, and (2) during an experimental functional task, with 10 wheelchair basketball athletes who sprint in straight line using a sports wheelchair. Findings: In the simulated gimbal lock experiment, the Tilt-and-Torsion method provided both humeral elevation and rotation measurements, contrary to the Euler YXY method. During the wheelchair sprints, humeral rotation ranged from 14{\deg} (externally) to 13{\deg} (internally), which is consistent with typical maximal ranges of humeral rotation, compared to 65{\deg} to 50{\deg} with the Euler YXY method. Interpretation: Based on our results, we recommend that shoulder angles be expressed using Tilt-and-Torsion angles instead of Euler YXY.
Revisiting Event Study Designs: Robust and Efficient Estimation
Authors: Kirill Borusyak, Xavier Jaravel, Jann Spiess
Date: 2021-08-27 00:00:00
Tasks: Imputation

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive "imputation" form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter - about half as large as benchmark estimates used to calibrate macroeconomic models - and predominantly occurs in the first month after the rebate.
An economic decision-making model of anticipated surprise with dynamic expectation
Authors: Ho Ka Chan, Taro Toyoizumi
Date: 2021-08-27 00:00:00
Tasks: Decision Making

When making decisions under risk, people often exhibit behaviors that classical economic theories cannot explain. Newer models that attempt to account for these irrational behaviors often lack neuroscience bases and require the introduction of subjective and problem-specific constructs. Here, we present a decision-making model inspired by the prediction error signals and introspective neuronal replay reported in the brain. In the model, decisions are chosen based on anticipated surprise, defined by a nonlinear average of the differences between individual outcomes and a reference point. The reference point is determined by the expected value of the possible outcomes, which can dynamically change during the mental simulation of decision-making problems involving sequential stages. Our model elucidates the contribution of each stage to the appeal of available options in a decision-making problem. This allows us to explain several economic paradoxes and gambling behaviors. Our work could help bridge the gap between decision-making theories in economics and neurosciences.
A continuous space model of new economic geography with a quasi-linear log utility function
Authors: Kensuke Ohtake
Date: 2021-08-27 00:00:00

We consider the extension of a tractable NEG model with a quasi-linear log utility to continuous space, and investigate the behavior of its solution mathematically. The model is a system of nonlinear integral and differential equations describing the market equilibrium and the time evolution of the spatial distribution of population density. A unique global solution is constructed and a homogeneous stationary solution with evenly distributed population is shown to be unstable. Furthermore, it is shown numerically that the destabilized homogeneous stationary solution eventually forms spiky spatial distributions. The number of the spikes decreases as the preference for variety increases or the transport cost decreases.
Who Cares More? Allocation with Diverse Preference Intensities
Authors: Pietro Ortoleva, Evgenii Safonov, Leeat Yariv
Date: 2021-08-26 00:00:00

Goods and services -- public housing, medical appointments, schools -- are often allocated to individuals who rank them similarly but differ in their preference intensities. We characterize optimal allocation rules when individual preferences are known and when they are not. Several insights emerge. First-best allocations may involve assigning some agents "lotteries" between high- and low-ranked goods. When preference intensities are private information, second-best allocations always involve such lotteries and, crucially, may coincide with first-best allocations. Furthermore, second-best allocations may entail disposal of services. We discuss a market-based alternative and show how it differs.
Stationary Multi-source AI-powered Real-time Tomography (SMART)
Authors: Weiwen Wu, Yaohui Tang, Tianling Lv, Chuang Niu, Cheng Wang, Yiyan Guo, Yunheng Chang, Ge Wang, Yan Xi
Date: 2021-08-27 00:00:00
Tasks: Computed Tomography (CT)

Over the past decades, the development of CT technologies has been largely driven by the needs for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical micro-CT since small animals, as human cardiac disease models, have much higher heart rates than human. To address this challenge, here we report a Stationary Multi-source AI-based Real-time Tomography (SMART) micro-CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect x-ray signals in parallel, enabling instantaneous tomography in principle. Given the multi-source architecture, the field-of-view only covers a cardiac region. To solve this interior problem, an AI-empowered interior tomography approach is developed to synergize sparsity-based regularization and learning-based reconstruction. To demonstrate the performance and utilities of the SMART system, extensive results are obtained in physical phantom experiments and animal studies, including dead and live rats as well as live rabbits. The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac micro-CT with the unprecedented temporal resolution of 30ms, which is an order of magnitude higher than the state of the art.
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV
Authors: Gianluca Fontanesi, Anding Zhu, Hamed Ahmadi
Date: 2021-08-26 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

The choice of the transmitting frequency to provide cellular-connected Unmanned Aerial Vehicle (UAV) reliable connectivity and mobility support introduce several challenges. Conventional sub-6 GHz networks are optimized for ground Users (UEs). Operating at the millimeter Wave (mmWave) band would provide high-capacity but highly intermittent links. To reach the destination while minimizing a weighted function of traveling time and number of radio failures, we propose in this paper a UAV joint trajectory and band switch approach. By leveraging Double Deep Q-Learning we develop two different approaches to learn a trajectory besides managing the band switch. A first blind approach switches the band along the trajectory anytime the UAV-UE throughput is below a predefined threshold. In addition, we propose a smart approach for simultaneous learning-based path planning of UAV and band switch. The two approaches are compared with an optimal band switch strategy in terms of radio failure and band switches for different thresholds. Results reveal that the smart approach is able in a high threshold regime to reduce the number of radio failures and band switches while reaching the desired destination.
Spliced Leader Trapping Reveals Widespread Alternative Splicing Patterns in the Highly Dynamic Transcriptome of Trypanosoma brucei
Authors: Daniel Nilsson, Kapila Gunasekera, Jan Mani, Magne Osteras, Laurent Farinelli, Loic Baerlocher, Isabel Roditi, Torsten Ochsenreiter
Date: 2021-08-27 00:00:00
Tasks: Diversity

Trans-splicing of leader sequences onto the 59ends of mRNAs is a widespread phenomenon in protozoa, nematodes and some chordates. Using parallel sequencing we have developed a method to simultaneously map 59splice sites and analyze the corresponding gene expression profile, that we term spliced leader trapping (SLT). The method can be applied to any organism with a sequenced genome and trans-splicing of a conserved leader sequence. We analyzed the expression profiles and splicing patterns of bloodstream and insect forms of the parasite Trypanosoma brucei. We detected the 59splice sites of 85% of the annotated protein-coding genes and, contrary to previous reports, found up to 40% of transcripts to be differentially expressed. Furthermore, we discovered more than 2500 alternative splicing events, many of which appear to be stage-regulated. Based on our findings we hypothesize that alternatively spliced transcripts present a new means of regulating gene expression and could potentially contribute to protein diversity in the parasite. The entire dataset can be accessed online at TriTrypDB or through: http://splicer.unibe.ch/.
Representation learning with reward prediction errors
Authors: William H. Alexander, Samuel J. Gershman
Date: 2021-08-27 00:00:00
Tasks: Hippocampus, Prediction, Representation Learning

The Reward Prediction Error hypothesis proposes that phasic activity in the midbrain dopaminergic system reflects prediction errors needed for learning in reinforcement learning. Besides the well-documented association between dopamine and reward processing, dopamine is implicated in a variety of functions without a clear relationship to reward prediction error. Fluctuations in dopamine levels influence the subjective perception of time, dopamine bursts precede the generation of motor responses, and the dopaminergic system innervates regions of the brain, including hippocampus and areas in prefrontal cortex, whose function is not uniquely tied to reward. In this manuscript, we propose that a common theme linking these functions is representation, and that prediction errors signaled by the dopamine system, in addition to driving associative learning, can also support the acquisition of adaptive state representations. In a series of simulations, we show how this extension can account for the role of dopamine in temporal and spatial representation, motor response, and abstract categorization tasks. By extending the role of dopamine signals to learning state representations, we resolve a critical challenge to the Reward Prediction Error hypothesis of dopamine function.
A System-on-Chip for Closed-loop Optogenetic Sleep Modulation
Authors: Xilin Liu, Andrew G. Richardson
Date: 2021-07-17 00:00:00
Tasks: Specificity

Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a fully integrated, low-power system-on-chip (SoC) for real-time sleep stage classification and stage-specific optical stimulation. The SoC consists of a 4-channel analog front-end for recording polysomnography signals, a mixed-signal machine-learning (ML) core, and a 16-channel optical stimulation back-end. A novel ML algorithm and innovative circuit design techniques improved the online classification performance while minimizing power consumption. The SoC was designed and simulated in 180 nm CMOS technology. In an evaluation using an expert labeled sleep database with 20 subjects, the SoC achieves a high sensitivity of 0.806 and a specificity of 0.947 in discriminating 5 sleep stages. Overall power consumption in continuous operation is 97 uW.
Proteome remodelling during development from blood to insect-form Trypanosoma brucei quantified by SILAC and mass spectrometry
Authors: Kapila Gunasekera, Daniel Wüthrich, Sophie Braga-Lagache, Manfred Heller, Torsten Ochsenreiter
Date: 2021-08-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Form

Background: Trypanosoma brucei is the causative agent of human African sleeping sickness and Nagana in cattle. In addition to being an important pathogen T. brucei has developed into a model system in cell biology. Results: Using Stable Isotope Labelling of Amino acids in Cell culture (SILAC) in combination with mass spectrometry we determined the abundance of >1600 proteins in the long slender (LS), short stumpy (SS) mammalian bloodstream form stages relative to the procyclic (PC) insect-form stage. In total we identified 2645 proteins, corresponding to ~30% of the total proteome and for the first time present a comprehensive overview of relative protein levels in three life stages of the parasite. Conclusions: We can show the extent of pre-adaptation in the SS cells, especially at the level of the mitochondrial proteome. The comparison to a previously published report on monomorphic in vitro grown bloodstream and procyclic T. brucei indicates a loss of stringent regulation particularly of mitochondrial proteins in these cells when compared to the pleomorphic in vivo situation. In order to better understand the different levels of gene expression regulation in this organism we compared mRNA steady state abundance with the relative protein abundance-changes and detected moderate but significant correlation indicating that trypanosomes possess a significant repertoire of translational and posttranslational mechanisms to regulate protein abundance.
Price modelling under generalized fractional Brownian motion
Authors: Axel A. Araneda
Date: 2021-08-26 00:00:00
Tasks: LEMMA

The Generalized fractional Brownian motion (gfBm) is a stochastic process that acts as a generalization for both fractional, sub-fractional, and standard Brownian motion. Here we study its use as the main driver for price fluctuations, replacing the standard Brownian Brownian motion in the well-known Black-Scholes model. By the derivation of the generalized fractional Ito's lemma and the related effective Fokker-Planck equation, we discuss its application to both the option pricing problem valuing European options, and the computation of Value-at-Risk and Expected Shortfall. Moreover, the option prices are computed for a CEV-type model driven by gfBm.
Asymptotically optimal strategies in a diffusion approximation of a repeated betting game
Authors: Mikhail Zhitlukhin
Date: 2021-08-26 00:00:00

We construct a diffusion approximation of a repeated game in which agents make bets on outcomes of i.i.d. random vectors and their strategies are close to an asymptotically optimal strategy. This model can be interpreted as trading in an asset market with short-lived assets. We obtain sufficient conditions for a strategy to maintain a strictly positive share of total wealth over the infinite time horizon. For the game with two players, we find necessary and sufficient conditions for the wealth share process to be transient or recurrent in this model, and also in its generalization with Markovian regime switching.
JUWELS Booster -- A Supercomputer for Large-Scale AI Research
Authors: Stefan Kesselheim, Andreas Herten, Kai Krajsek, Jan Ebert, Jenia Jitsev, Mehdi Cherti, Michael Langguth, Bing Gong, Scarlet Stadtler, Amirpasha Mozaffari, Gabriele Cavallaro, Rocco Sedona, Alexander Schug, Alexandre Strube, Roshni Kamath, Martin G. Schultz, Morris Riedel, Thomas Lippert
Date: 2021-06-30 00:00:00

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI) research and applications. We detail its system architecture, parallel, distributed model training, and benchmarks indicating its outstanding performance. We exemplify its potential for research application by presenting large-scale AI research highlights from various scientific fields that require such a facility.