pwc-archive/papers-with-abstracts huggingface dataset:
Some Tradeoffs of Competition in Grant Contests
Authors: Kyle R. Myers
Date: 2022-07-06 00:00:00

When funding public goods, resources are often allocated via mechanisms that resemble contests, especially in the case of research grants. A common critique of these contests is that they induce ``too much'' effort from participants. This need not be true if the effort in the contest is itself directed towards the public good. This papers analyzes survey data on scientists' time use and finds that scientists allocate their time in a way that is consistent with fundraising effort (e.g., grant writing) having inherent scientific value -- scientists who spend more time fundraising do not spent significantly less time on research even after conditioning on confounding factors. Theoretical models of contests are used to show that the presence of such a positive effort externality, where scientists generate social value when pursuing grants, changes the relationship between competition and the aggregate productivity of a grant contest. Ensuring that scientists exert socially valuable effort to obtain grants is increasingly important as grant contests become more competitive.
Reputation Effects under Short Memories
Authors: Harry Pei
Date: 2022-07-06 00:00:00

I analyze a novel reputation game between a patient seller and a sequence of myopic consumers, in which the consumers have limited memories and do not know the exact sequence of the seller's actions. I focus on the case where each consumer only observes the number of times that the seller took each of his actions in the last K periods. When payoffs are monotone-supermodular, I show that the patient seller can approximately secure his commitment payoff in all equilibria as long as K is at least one. I also show that the consumers can approximately attain their first-best welfare in all equilibria if and only if their memory length K is lower than some cutoff. Although a longer memory enables more consumers to punish the seller once the seller shirks, it weakens their incentives to punish the seller once they observe him shirking
Modeling the HEVC Encoding Energy Using the Encoder Processing Time
Authors: Geetha Ramasubbu, André Kaup, Christian Herglotz
Date: 2022-07-06 00:00:00
Tasks: Video Compression

The global significance of energy consumption of video communication renders research on the energy need of video coding an important task. To do so, usually, a dedicated setup is needed that measures the energy of the encoding and decoding system. However, such measurements are costly and complex. To this end, this paper presents the results of an exhaustive measurement series using the x265 encoder implementation of HEVC and analyzes the relation between encoding time and encoding energy. Finally, we introduce a simple encoding energy estimation model which employs the encoding time of a lightweight encoding process to estimate the encoding energy of complex encoding configurations. The proposed model reaches a mean estimation error of 11.35% when averaged over all presets. The results from this work are useful when the encoding energy estimate is required to develop new energy-efficient video compression algorithms.
VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization
Authors: Marius Laska, Till Schulz, Jan Grottke, Christoph Blut, Jörg Blankenbach
Date: 2022-07-06 00:00:00
Tasks: Indoor Localization, Simultaneous Localization and Mapping

Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality of collected labeled fingerprints within the calibration phase, which is a tedious process when done manually in a static fashion. We present VI-SLAM2tag, a system for auto-labeling of dynamically collected fingerprints using the visual-inertial simultaneous localization and mapping (VI-SLAM) module of ARCore. ARCore occasionally updates its internal coordinate system. Mapping the entire trajectory to a target coordinate system via a single transformation thus results in large drift effects. To solve this, we propose a strategy for determining locally optimal sub-trajectory transformations. Our system is evaluated with respect to the accuracy of the generated position labels using a geodetic tracking system. We achieve an average labeling error of roughly 50 cm for trajectories of up to 15 minutes, which is sufficient for fingerprinting-based localization. We demonstrate this by collecting a multi-floor dataset including WLAN and IMU data and show how it can be used to train neural network based models that achieve a positioning accuracy of roughly 2 m. VI-SLAM2tag and the dataset are made publicly available.
User Pairing and Power Allocation for FTN-based SC-NOMA and MIMO-NOMA Systems Considering User Fairness
Authors: Peiyang Song
Date: 2022-07-06 00:00:00
Tasks: Fairness

The development of the industrial Internet of Things (IoT) calls for higher spectrum efficiency (SE). Faster than Nyquist (FTN) and non-orthogonal multiple access (NOMA) are both promising paradigms to improve the SE without any extra spectrum resources required. The combination of FTN and NOMA is an interesting issue and has been focused on recently. In the NOMA technology, user pairing and power allocation are key algorithms determining system capacity. This paper first proposes a joint user pairing and power allocation algorithm for the FTN-based single-carrier (SC) NOMA system. Then, the FTN-based multiple-input-multiple-output (MIMO) NOMA is studied and a dynamic user pairing and power allocation scheme is presented. In both scenarios, the maximum available sum rate (ASR) is the target. While based on the fairness principle, the user's SE in the NOMA system is guaranteed to be no less than that in the OMA system. Simulation results show the advantage of the FTN-based NOMA with the proposed scheme in ASR and quality of service (QoS) performance. As far as we know, this paper is the first solution to the issue of user pairing and power allocation in FTN-based NOMA, which proves the great advantage of the combination of these two state-of-the-art technologies.
Neural Network Equalization for Asynchronous Multitrack Detection in TDMR
Authors: Elnaz Banan Sadeghian
Date: 2022-07-06 00:00:00

The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly detect multiple asynchronous tracks. In this paper, we propose to replace the conventional communication-theoretic multiple-input multiple-output equalizer in the GPRML architecture with a neural network equalizer for better adaption to the nonlinearity of the underlying channel. We evaluate the proposed equalization strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed equalizer outperforms the conventional linear equalizer, by a 35% reduction in the bit-error rate.
Novel Spectrum Allocation Among Multiple Transmission Windows for Terahertz Communication Systems
Authors: Akram Shafie, Nan Yang, Chong Han, Josep M. Jornet
Date: 2022-07-06 00:00:00

This paper presents a novel spectrum allocation strategy for multiuser terahertz (THz) band communication systems when the to-be-allocated spectrum is composed of multiple transmission windows (TWs). This strategy explores the benefits of (i) allowing users to occupy sub-bands with unequal bandwidths and (ii) optimally avoiding using some spectra that exist at the edges of TWs where molecular absorption loss is high. To maximize the aggregated multiuser data rate, we formulate an optimization problem, with the primary focus on spectrum allocation. We then apply transformations and modifications to make the problem computationally tractable, and develop an iterative algorithm based on successive convex approximation to determine the optimal sub-band bandwidth and the unused spectra at the edges of TWs. Using numerical results, we show that a significantly higher data rate can be achieved by changing the sub-band bandwidth, as compared to equal sub-band bandwidth. We also show that a further data rate gain can be obtained by optimally determining the unused spectra at the edges of TWs, as compared to avoiding using pre-defined spectra at the edges of TWs.
Mesoscopic Collective Activity in Excitatory Neural Fields: Cross-frequency Coupling
Authors: Yu Qin, Alex Sheremet
Date: 2022-07-06 00:00:00

In the brain, cross-frequency coupling has been hypothesized to result from the activity of specialized microcircuits. For example, theta-gamma coupling is assumed to be generated by specialized cell pairs (PING and ING mechanisms), or special cells (e.g., fast bursting neurons). However, this implies that the generating mechanisms is uniquely specific to the brain. In fact, cross-scale coupling is a phenomenon encountered in the physics of all large, multi-scale systems: phase and amplitude correlations between components of different scales arise as a result of nonlinear interaction. Because the brain is a multi-scale system too, a similar mechanism must be active in the brain. Here, we represent brain activity as a superposition of nonlinearly interacting patterns of spatio-temporal activity (collective activity), supported by populations of neurons. Cross-frequency coupling is a direct consequence of the nonlinear interactions, and does not require specialized cells or cell pairs. It is therefore universal, and must be active in neural fields of any composition. To emphasize this, we demonstrate the phenomenon in excitatory fields. While there is no doubt that specialized cells play a role in theta-gamma coupling, our results suggest that the coupling mechanism is at the same time simpler and richer: simpler because it involves the universal principle of nonlinearity; richer, because nonlinearity of collective activity is likely modulated by specialized-cell populations in ways to be yet understood.
Can Multiple Phylogenetic Trees Be Displayed in a Tree-Child Network Simultaneously?
Authors: Yufeng Wu, Louxin Zhang
Date: 2022-07-06 00:00:00

A binary phylogenetic network on a taxon set $X$ is a rooted acyclic digraph in which the degree of each nonleaf node is three and its leaves (i.e.degree-one nodes) are uniquely labeled with the taxa of $X$. It is tree-child if each nonleaf node has at least one child of indegree one. A set of binary phylogenetic trees may or may not be simultaneously displayed in a binary tree-child network. Necessary conditions for multiple phylogenetic trees being simultaneously displayed in a tree-child network are given here. In particular, it is proved that any two phylogenetic trees can always simultaneously be displayed in some tree-child network on the same taxa set. It is also proved that any set of multiple binary phylogenetic trees can always simultaneously be displayed in some non-binary tree-child network on the same taxa set, where each nonleaf node is of either indegree one and outdegree two or indegree at least two and outdegree out.
Spatiotemporal patterns of Covid-19 pandemic in India: Inferences of pandemic dynamics from data analysis
Authors: Preet Mishra, R. K. Brojen Singh
Date: 2022-07-06 00:00:00

Modeling and analysis of the large scale Covid-19 pandemic data can yield inferences about it's dynamics and characteristics of disease propagation. These inferences can then be correlated with contextual factors like population density, effects of strategic interventions, heterogeneous disease propagation etc, and such set of validated inferences can serve as precedents for designing of subsequent mitigation strategies. In this work, we present the analysis of Covid-19 pandemic data in Indian context using growth functions fitting procedure and harmonic analysis method. Our results of growth function fitting to the data indicate that the growth function parameters are quite sensitive to the growth of the infected population indicating positive impact of lockdown strategy, identification of inflection point and nearly synchronous statistical features of disease spreading. The harmonic analysis of the data shows the countrywide synchronous incident features due to simultaneous implementation of control strategies. However, if one analyzes the data from each state of the India, one can see various forms of travelling waves in the countrywide wave pattern. Hence, one needs to do these analysis from time to time to understand the effectiveness of any control strategy and to closely look at the disease propagation to devise the required type of mitigation strategies.
A systematic procedure for incorporating separable static heterogeneity into compartmental epidemic models
Authors: Odo Diekmann, Hisashi Inaba
Date: 2022-07-05 00:00:00

In this paper, we show how to modify a compartmental epidemic model, without changing the dimension, such that separable static heterogeneity is taken into account. The derivation is based on the Kermack-McKendrick renewal equation.
Integrative omics framework for characterization of coral reef ecosystems from the Tara Pacific expedition
Authors: Caroline Belser, Julie Poulain, Karine Labadie, Frederick Gavory, Adriana Alberti, Julie Guy, Quentin Carradec, Corinne Cruaud, Corinne da Silva, Stefan Engelen, Paul Mielle, Aude Perdereau, Gaelle Samson, Shahinaz Gaz, Genoscope Technical Team, Christian R Voolstra, Pierre E Galand, J. Michel Flores, Benjamin Cc Hume, Gabriela Perna, Maren Ziegler, Hans-Joachim Ruscheweyh, Emilie Boissin, Sarah Romac, Guillaume Bourdin, Guillaume Iwankow, Clémentine Moulin, David A Paz García, Tara Pacific Consortium Coordinators, Claude Scarpelli, E Krame Jacoby, Pedro H Oliveira, Jean-Marc Aury, Denis Allemand, Serge Planes, Patrick Wincker
Date: 2022-07-06 00:00:00

Coral reef science is a fast-growing field propelled by the need to better understand coral health and resilience to devise strategies to slow reef loss resulting from environmental stresses. Key to coral resilience are the symbiotic interactions established within a complex holobiont, i.e. the multipartite assemblages comprising the host coral organism, endosymbiotic dinoflagellates, bacteria, archaea, fungi, and viruses. Tara Pacific is an ambitious project built upon the experience of previous Tara Oceans expeditions, and leveraging state-of-the-art sequencing technologies and analyses to dissect the biodiversity and biocomplexity of the coral holobiont screened across most archipelagos spread throughout the entire Pacific Ocean. Here we detail the Tara Pacific workflow for multi-omics data generation, from sample handling to nucleotide sequence data generation and deposition. This unique multidimensional framework also includes a large amount of concomitant metadata collected side-by-side that provide new assessments of coral reef biodiversity including micro-biodiversity and shape future investigations of coral reef dynamics and their fate in the Anthropocene.
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation
Authors: Jungyu Ahn, Sungwoo Park, Jiwoon Kim, Ju-Hong Lee
Date: 2022-07-06 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Time Series, Time Series Analysis

Asset allocation using reinforcement learning has advantages such as flexibility in goal setting and utilization of various information. However, existing asset allocation methods do not consider the following viewpoints in solving the asset allocation problem. First, State design without considering portfolio management and financial market characteristics. Second, Model Overfitting. Third, Model training design without considering the statistical structure of financial time series data. To solve the problem of the existing asset allocation method using reinforcement learning, we propose a new reinforcement learning asset allocation method. First, the state of the portfolio managed by the model is considered as the state of the reinforcement learning agent. Second, Monte Carlo simulation data are used to increase training data complexity to prevent model overfitting. These data can have different patterns, which can increase the complexity of the data. Third, Monte Carlo simulation data are created considering various statistical structures of financial markets. We define the statistical structure of the financial market as the correlation matrix of the assets constituting the financial market. We show experimentally that our method outperforms the benchmark at several test intervals.
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Authors: Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
Date: 2022-06-14 00:00:00
Tasks: Transfer Learning

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).
Planning Courses for Student Success at the American College of Greece
Authors: Ioannis T. Christou, Evgenia Vagianou, George Vardoulias
Date: 2022-06-16 00:00:00

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.
Service Provisioning Framework for RAN Slicing: User Admissibility, Slice Association and Bandwidth Allocation
Authors: Yao Sun, Shuang Qin, Member, Gang Feng, Lei Zhang, and Muhammad Ali Imran, SeniorMember, IEEE
Date: 2020-06-08 00:00:00
Proceeding: IEEE Transactions on Mobile Computing 2020 6

Network slicing (NS) has been identiﬁed as one of the most promising architectural technologies for future mobile network
systems to meet the extremely diversiﬁed service requirements of users. In radio access networks (RAN) slicing, service provisioning for
slice users becomes much more complicated than that in traditional mobile networks, as the constraints of both user physical association
with base station (BS) and logical association with NS should be considered. In other words, the user-BS-NS three layer association
relationship should be addressed in provisioning tailored service for diversiﬁed use cases with various quality of service (QoS)
requirements. Therefore, service provisioning in RAN slicing becomes an essential yet challenging issue for 5G and beyond systems.
In this paper, we propose a uniﬁed framework for service provisioning in RAN slicing with aim of maximizing resource utilization while
guaranteeing QoS of users. The framework consists of two steps. The ﬁrst step is to identify a set of slice users whoseQoS can be
satisﬁed simultaneously; while the second step performs joint slice association and bandwidth allocation with aim to minimize bandwidth
consumption. Numerical results show that in typical scenarios, our proposed service provisioning framework can achieve signiﬁcant
performance gain in terms of the number of serving users and wireless bandwidth utilization compared with traditional schemes.
Leveraging Human Computation for Quality Assurance in Open Source Communities
Authors: Johannah Sprinz
Date: 2022-01-29 00:00:00
Proceeding: Open Access LMU 2022 1
Tasks: Manufacturing Quality Control

Software developed under the open source development model (OSSD) has risen to significant importance over the recent decades. With more and more critical components being developed under the OSSD, the need for extensive quality assurance (QA) increases. This thesis investigates any potential for conducting formalized user testing through inexperienced volunteer community members under the OSSD. A human computation platform to aggregate such test results was designed and named open crowdsourced user-testing suite (OPEN-CUTS). A usability study of a prototype of OPEN-CUTS confirms the viability of this approach and points to potential future research questions.
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search
Authors: Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Yiming Zeng, Xiaowei Li
Date: 2021-11-23 00:00:00
Proceeding: BMVC 2021 11
Tasks: Neural Architecture Search

Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://github.com/ShunLu91/DU-DARTS.