pwc-archive/papers-with-abstracts huggingface dataset:
A Semi-Blind Multiuser SIMO GFDM System in the Presence of CFOs and IQ Imbalances
Authors: Yujie Liu, Xu Zhu, Eng Gee Lim, Yufei Jiang, Yi Huang
Date: 2020-10-14 00:00:00

In this paper, we investigate an open topic of a multiuser single-input-multiple-output (SIMO) generalized frequency division multiplexing (GFDM) system in the presence of carrier frequency offsets (CFOs) and in-phase/quadrature-phase (IQ) imbalances. A low-complexity semi-blind joint estimation scheme of multiple channels, CFOs and IQ imbalances is proposed. By utilizing the subspace approach, CFOs and channels corresponding to U users are first separated into U groups. For each individual user, CFO is extracted by minimizing the smallest eigenvalue whose corresponding eigenvector is utilized to estimate channel blindly. The IQ imbalance parameters are estimated jointly with channel ambiguities by very few pilots. The proposed scheme is feasible for a wider range of receive antennas number and has no constraints on the assignment scheme of subsymbols and subcarriers, modulation type, cyclic prefix length and the number of subsymbols per GFDM symbol. Simulation results show that the proposed scheme significantly outperforms the existing methods in terms of bit error rate, outage probability, mean-square-errors of CFO estimation, channel and IQ imbalance estimation, while at much higher spectral efficiency and lower computational complexity. The Cramer-Rao lower bound is derived to verify the effectiveness of the proposed scheme, which is shown to be close to simulation results.
Long term dynamics of poverty transitions in India
Authors: Anand Sahasranaman
Date: 2020-10-14 00:00:00

We model the dynamics of poverty using a stochastic model of Geometric Brownian Motion with reallocation (RGBM) and explore both transient and persistent poverty over 1952-2006. We find that annual transitions in and out of poverty are common and show a rising trend, with the rise largely being driven by transitions out of poverty. Despite this promising trend, even toward the end of the time frame, there is a non-trivial proportion of individuals still transitioning annually into poverty, indicative of the economic fragility of those near the poverty line. We also find that there is still a marked persistence of poverty over time, though the probability of poverty persistence is slowly declining. Particularly concerning in this context are the poverty trajectories of those at the very bottom of the income distribution. The choice of poverty line appears to impact the dynamics, with higher poverty lines corresponding to lower transitions and higher persistence probabilities. The distinct nature of emergent transient and persistence dynamics suggests that the approaches to counter these phenomena need to be different, possibly incorporating both missing financial markets and state action.
Passive RIS vs.Hybrid RIS: A Comparative Study on Channel Estimation
Authors: Rafaela Schroeder, Jiguang He, Markku Juntti
Date: 2020-10-14 00:00:00

The reconfigurable intelligent surface (RIS) plays an important role in maintaining the connectivity in millimeter wave (mmWave) MIMO systems when the direct channel between the transceivers is blocked. However, it is difficult to acquire the channel state information (CSI), which is essential for the design of RIS phase control matrix and beamforming vectors at the transceivers. In this paper, we compare the channel estimation (CE) performance and achieved spectral efficiency (SE) of the purely passive and hybrid RIS architectures. CE is done via atomic norm minimization (ANM). For the purely passive RIS, we follow a two-stage procedure to sequentially estimate the channel parameters, while for the hybrid RIS we estimate the individual channels at the RIS based on the observations from active RIS elements assuming alternating uplink and downlink training. The simulation results show that the purely passive RIS brings better CE and SE performance compared to the hybrid RIS under the same training overhead. We further consider different setups for the hybrid RIS and study the tradeoffs among them.
Understanding Twitter Engagement with a Click-Through Rate-based Method
Authors: Fiandro Andrea, Francois Jeanpierre, Oliveri Isabeau, Leonardi Simone, Senese Matteo A., Crepaldi Giorgio, Benincasa Alberto, Rizzo Giuseppe
Date: 2020-09-30 00:00:00

This paper presents the POLINKS solution to the RecSys Challenge 2020 that
ranked 6th in the final leaderboard. We analyze the performance of our solution
that utilizes the click-through rate value to address the challenge task, we
compare it with a gradient boosting model, and we report the quality indicators
utilized for computing the final leaderboard.
On the Problem of Underranking in Group-Fair Ranking
Authors: Sruthi Gorantla, Amit Deshpande, Anand Louis
Date: 2020-09-24 00:00:00
Tasks: Fairness, Learning-To-Rank, Recommendation Systems

Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, people. Credit ratings, standardized tests, risk assessments output only a score, but are also used implicitly for ranking. Bias in such ranking systems, especially among the top ranks, can worsen social and economic inequalities, polarize opinions, and reinforce stereotypes. On the other hand, a bias correction for minority groups can cause more harm if perceived as favoring group-fair outcomes over meritocracy. In this paper, we formulate the problem of underranking in group-fair rankings, which was not addressed in previous work. Most group-fair ranking algorithms post-process a given ranking and output a group-fair ranking. We define underranking based on how close the group-fair rank of each item is to its original rank, and prove a lower bound on the trade-off achievable for simultaneous underranking and group fairness in ranking. We give a fair ranking algorithm that takes any given ranking and outputs another ranking with simultaneous underranking and group fairness guarantees comparable to the lower bound we prove. Our algorithm works with group fairness constraints for any number of groups. Our experimental results confirm the theoretical trade-off between underranking and group fairness, and also show that our algorithm achieves the best of both when compared to the state-of-the-art baselines.
Learning Representations of Hierarchical Slates in Collaborative Filtering
Authors: Elahi Ehtsham, Chandrashekar Ashok
Date: 2020-09-25 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Representation Learning

We are interested in building collaborative filtering models for
recommendation systems where users interact with slates instead of individual
items. These slates can be hierarchical in nature. The central idea of our
approach is to learn low dimensional embeddings of these slates. We present a
novel way to learn these embeddings by making use of the (unknown) statistics
of the underlying distribution generating the hierarchical data. Our
representation learning algorithm can be viewed as a simple composition rule
that can be applied recursively in a bottom-up fashion to represent arbitrarily
complex hierarchical structures in terms of the representations of its
constituent components. We demonstrate our ideas on two real world
recommendation systems datasets including the one used for the RecSys 2019
challenge. For that dataset, we improve upon the performance achieved by the
winning team's model by incorporating embeddings as features generated by our
approach in their solution.
Using satellite imagery to understand and promote sustainable development
Authors: Burke Marshall, Driscoll Anne, Lobell David B., Ermon Stefano
Date: 2020-09-23 00:00:00
Tasks: BIG-bench Machine Learning

Accurate and comprehensive measurements of a range of sustainable development
outcomes are fundamental inputs into both research and policy. We synthesize
the growing literature that uses satellite imagery to understand these
outcomes, with a focus on approaches that combine imagery with machine
learning. We quantify the paucity of ground data on key human-related outcomes
and the growing abundance and resolution (spatial, temporal, and spectral) of
satellite imagery. We then review recent machine learning approaches to
model-building in the context of scarce and noisy training data, highlighting
how this noise often leads to incorrect assessment of models' predictive
performance. We quantify recent model performance across multiple sustainable
development domains, discuss research and policy applications, explore
constraints to future progress, and highlight key research directions for the
field.
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
Date: 2020-10-14 00:00:00
Tasks: whole slide images

Purpose: In this work, we present a collaboration to create a validation
dataset of pathologist annotations for algorithms that process whole slide
images (WSIs). We focus on data collection and evaluation of algorithm
performance in the context of estimating the density of stromal tumor
infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64
glass slides of hematoxylin- and eosin-stained ductal carcinoma core biopsies
prepared at a single clinical site. We created training materials and workflows
to crowdsource pathologist image annotations on two modes: an optical
microscope and two digital platforms. The workflows collect the ROI type, a
decision on whether the ROI is appropriate for estimating the density of sTILs,
and if appropriate, the sTIL density value for that ROI. Results: The pilot
study yielded an abundant number of cases with nominal sTIL infiltration.
Furthermore, we found that the sTIL densities are correlated within a case, and
there is notable pathologist variability. Consequently, we outline plans to
improve our ROI and case sampling methods. We also outline statistical methods
to account for ROI correlations within a case and pathologist variability when
validating an algorithm. Conclusion: We have built workflows for efficient data
collection and tested them in a pilot study. As we prepare for pivotal studies,
we will consider what it will take for the dataset to be fit for a regulatory
purpose: study size, patient population, and pathologist training and
qualifications. To this end, we will elicit feedback from the FDA via the
Medical Device Development Tool program and from the broader digital pathology
and AI community. Ultimately, we intend to share the dataset, statistical
methods, and lessons learned.
Combining detection and reconstruction of correlational and quasi-periodic motifs in viral genomic sequences with transitional genome mapping: Application to COVID-19
Authors: Vladimr R. Chechetkin, Vasily V. Lobzin
Date: 2020-10-14 00:00:00

A method of Transitional Automorphic Mapping of the Genome on Itself (TAMGI) is aimed at combining detection and reconstruction of correlational and quasi-periodic motifs in the viral genomic RNA/DNA sequences. The motifs reconstructed by TAMGI are robust with respect to indels and point mutations and can be tried as putative therapeutic targets. We developed and tested the relevant theory and statistical criteria for TAMGI applications. The applications of TAMGI are illustrated by the study of motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2 (the latter coronavirus SARS-CoV-2 being responsible for the COVID-19 pandemic) packaged within filament-like helical capsid. Such ribonucleocapsid is transported into spherical membrane envelope with incorporated spike glycoproteins. Two other examples concern the genomes of viruses with icosahedral capsids, satellite tobacco mosaic virus (STMV) and bacteriophage PHIX174. A part of the quasi-periodic motifs in these viral genomes was evolved due to weakly specific cooperative interaction between genomic ssRNA/ssDNA and nucleocapsid proteins. The symmetry of the capsids leads to the natural selection of specific quasi-periodic motifs in the related genomic sequences. Generally, TAMGI provides a convenient tool for the study of numerous molecular mechanisms with participation of both quasi-periodic motifs and complete repeats, the genome organization, contextual analysis of cis/trans regulatory elements, data mining, and correlations in the genomic sequences.
Basic principles and concept design of a real-time clinical decision support system for managing medical emergencies on missions to Mars
Authors: Juan M Garcia-Gomez
Date: 2020-09-29 00:00:00
Tasks: Decision Making, Ethics, Fairness

Space agencies and private companies prepare the beginning of human space exploration for the 2030s with missions to put the first human on the Mars surface. The absence of gravity and radiation, along with distance, isolation and hostile environments, are expected to increase medical events where previously unseen manifestations may arise. The current healthcare strategy based on telemedicine and the possibility to stabilize and transport the injured crewmember to a terrestrial definitive medical facility is not applicable in exploration class missions. Therefore, the need for deploying the full autonomous capability to solve medical emergencies may guide the design of future onboard healthcare systems. We present ten basic principles and concept design of a software suite to bring onboard decision support to help the crew dealing with medical emergencies taking into consideration physiological disturbances in space and spaceflight restrictions. 1) give real-time support for emergency medical decision making, 2) give patient-specific advice for executive problem-solving, 3) take into account available information from life support and monitoring of crewmembers, 4) be fully autonomous from remote facilities, 5) continuously adapt predictions to physiological disturbance and changing conditions, 6) optimize emergency medical decision making in terms of mission fundamental priorities, 7) take into account medical supplies and equipment on board, 8) apply health standards for the level of care V, 9) implement ethics responsibilities for spaceflights, and 10) apply ethical standards for artificial intelligence. Based on these principles, we propose an autonomous clinical decision support system (CDSS) to provide real-time advice for emergency medical interventions on board of space exploration missions.
Soil moisture map construction using microwave remote sensors and sequential data assimilation
Authors: Bernard T. Agyeman, Song Bo, Soumya R. Sahoo, Xunyuan Yin, Jinfeng Liu, Sirish L. Shah
Date: 2020-09-28 00:00:00
Tasks: Form

Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to obtain soil moisture information, in the form of water content maps, for the implementation of closed-loop irrigation. Major challenges such as significant time delays in the soil moisture measurements, the inability of the sensors to provide soil moisture information in instances where the center pivot is stationary, and the inability of the sensors to provide soil moisture information in the root zone reduce the usability of the water content maps in the effective implementation of closed-loop irrigation. In this paper, we seek to address the aforementioned challenges and consequently describe a water content map construction procedure that is suitable for the implementation of closed-loop irrigation. Firstly, we propose the cylindrical coordinates version of the Richards equation (field model) which naturally models fields equipped with a center pivot irrigation system. Secondly, measurements obtained from the microwave sensors are assimilated into the field model using the extended Kalman filter to form an information fusion system, which will provide frequent soil moisture estimates and predictions in the form of moisture content maps. The utility of the proposed information fusion system is first investigated with simulated microwave sensor measurements. The information fusion system is then applied to a real large-scale agriculture field where we demonstrate the its ability to address the challenges. Three performance evaluation criteria are used to validate the soil moisture estimates and predictions provided by the proposed information fusion system.
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
Authors: Barkan Oren, Fuchs Yonatan, Caciularu Avi, Koenigstein Noam
Date: 2020-09-26 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Two main challenges in recommender systems are modeling users with
heterogeneous taste, and providing explainable recommendations. In this paper,
we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF)
model as a unified solution for both problems. AMP-CF breaks down the user to
several latent 'personas' (profiles) that identify and discern the different
tastes and inclinations of the user. Then, the revealed personas are used to
generate and explain the final recommendation list for the user. AMP-CF models
users as an attentive mixture of personas, enabling a dynamic user
representation that changes based on the item under consideration. We
demonstrate AMP-CF on five collaborative filtering datasets from the domains of
movies, music, video games and social networks. As an additional contribution,
we propose a novel evaluation scheme for comparing the different items in a
recommendation list based on the distance from the underlying distribution of
"tastes" in the user's historical items. Experimental results show that AMP-CF
is competitive with other state-of-the-art models. Finally, we provide
qualitative results to showcase the ability of AMP-CF to explain its
recommendations.
Channel Type Recognition in Wireless Communications: A Deep Learning Approach
Authors: Shu Sun, Xiaofeng Li, Sungho Moon
Date: 2020-10-12 00:00:00
Tasks: Deep Learning, Diversity, Multi-Task Learning, Vocal Bursts Type Prediction

In this paper, we propose two novel and practical deep-learning-based algorithms to solve the wireless channel type (WCT) recognition problem. Specifically, the WCT recognition problem is recast as a classification problem in deep learning due to their similarities, where a deep neural network (DNN) is trained off-line with a diversity of typical WCTs for fifth-generation (5G) and beyond-5G wireless communications, which is then utilized to perform online WCT determination. In the first algorithm, one WCT is regarded as a single task. While in the second scheme, one WCT is jointly characterized by several independent features, each of which is treated as a task and is classified respectively by training a DNN in a multi-task-learning manner, and the final WCT is identified by the combination of those channel features. Simulation results show that the proposed algorithms can classify various WCTs instantaneously with high accuracy, result in satisfactory block error rate and throughput, and outperform a representative baseline WCT determination scheme.
Exact neural mass model for synaptic-based working memory
Authors: Halgurd Taher, Alessandro Torcini, Simona Olmi
Date: 2020-10-14 00:00:00
Tasks: Object Recognition

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to get insight on Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are associated to stimulus-locked transient oscillations followed by a steady-state activity in the $\beta-\gamma$ band, thus resembling what observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the $\gamma$ power increases with the number of loaded items, as reported in many experiments, while $\theta$ and $\beta$ power reveal non monotonic behaviours.
Breaking Symmetries of the Reservoir Equations in Echo State Networks
Authors: Joschka Herteux, Christoph Räth
Date: 2020-09-21 00:00:00
Tasks: Time Series, Time Series Analysis

Reservoir computing has repeatedly been shown to be extremely successful in the prediction of nonlinear time-series. However, there is no complete understanding of the proper design of a reservoir yet. We find that the simplest popular setup has a harmful symmetry, which leads to the prediction of what we call mirror-attractor. We prove this analytically. Similar problems can arise in a general context, and we use them to explain the success or failure of some designs. The symmetry is a direct consequence of the hyperbolic tangent activation function. Further, four ways to break the symmetry are compared numerically: A bias in the output, a shift in the input, a quadratic term in the readout, and a mixture of even and odd activation functions. Firstly, we test their susceptibility to the mirror-attractor. Secondly, we evaluate their performance on the task of predicting Lorenz data with the mean shifted to zero. The short-time prediction is measured with the forecast horizon while the largest Lyapunov exponent and the correlation dimension are used to represent the climate. Finally, the same analysis is repeated on a combined dataset of the Lorenz attractor and the Halvorsen attractor, which we designed to reveal potential problems with symmetry. We find that all methods except the output bias are able to fully break the symmetry with input shift and quadratic readout performing the best overall.
Deep brain stimulation for movement disorder treatment: Exploring frequency-dependent efficacy in a computational network model
Authors: Konstantinos Spiliotis, Jens Starke, Denise Franz, Angelika Richter, Rüdiger Köhling
Date: 2020-10-14 00:00:00

A large scale computational model of the basal ganglia (BG) network is proposed to describes movement disorder including deep brain stimulation (DBS). The model of this complex network considers four areas of the basal ganglia network: the subthalamic nucleus (STN) as target area of DBS, globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus (THA). Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities can be derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities switch from normal conditions to parkinsonian. Simulating DBS on the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.