pwc-archive/papers-with-abstracts huggingface dataset:
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Authors: Phu Khanh Huynh, M. Lakshmi Varshika, Ankita Paul, Murat Isik, Adarsha Balaji, Anup Das
Date: 2022-02-17 00:00:00
Tasks: BIG-bench Machine Learning

Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology fronts, programming such systems to admit and execute a machine learning application is becoming increasingly challenging. Additionally, neuromorphic systems are required to guarantee real-time performance, consume lower energy, and provide tolerance to logic and memory failures. Consequently, there is a clear need for system software frameworks that can implement machine learning applications on current and emerging neuromorphic systems, and simultaneously address performance, energy, and reliability. Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design. We highlight challenges and opportunities that the future holds in the area of system software technology for neuromorphic computing.
Clustering by Hill-Climbing: Consistency Results
Authors: Ery Arias-Castro, Wanli Qiao
Date: 2022-02-18 00:00:00
Tasks: Clustering

We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's. We study both continuous-space and discrete-space (i.e., medoid) variants and establish their consistency.
Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables
Authors: Samuele Centorrino, Jean-Pierre Florens, Jean-Michel Loubes
Date: 2022-02-16 00:00:00
Tasks: BIG-bench Machine Learning, Econometrics, Fairness

A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status-quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called \textit{fairness} of machine learning algorithms, especially towards disadvantaged groups. In this chapter, we review the issue of fairness in machine learning through the lenses of structural econometrics models in which the unknown index is the solution of a functional equation and issues of endogeneity are explicitly accounted for. We model fairness as a linear operator whose null space contains the set of strictly {\it fair} indexes. A {\it fair} solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space. We also acknowledge that policymakers may incur a cost when moving away from the status quo. Achieving \textit{approximate fairness} is obtained by introducing a fairness penalty in the learning procedure and balancing more or less heavily the influence between the status quo and a full fair solution.
A recommender system for automatic picking of subsurface formation tops
Authors: Jesse R. Pisel, Joshua A. Dierker, Sanya Srivastava, Samira B. Ravilisetty, Michael J. Pyrcz
Date: 2022-02-17 00:00:00
Tasks: Recommendation Systems

Geoscience domain experts traditionally correlate formation tops in the subsurface using geophysical well logs (known as well-log correlation) by-hand. Based on individual well log interpretation and well-to-well comparisons, these correlations are done in the context of depositional models within a stratigraphic framework. Recently, many researchers have focused on automatic well-log correlation using a variety of warping algorithms that measure well similarity, and both unsupervised and supervised machine learning methods that assign categorical labels based on known tops in many other wells. These methods require a standardized suite of digital well logs (i.e. gamma ray logs for every well) along with the depth to the top of the formations, which might not be available in many cases. Herein, we propose a method that does not use geophysical well logs for correlation, but rather uses already picked tops in multiple wells to recommend the depth to the remaining unpicked tops in the wells. This recommender system calculates the depth to all formation tops in all the wells for two different datasets in two different basins. The Teapot Dome dataset is composed of lithostratigraphic formation tops, and the Mannville Group dataset is composed of sequence-stratigraphic (representing multiple lithologic groups within a stratigraphic unit) formation tops. For the demonstration, mean absolute error and root mean squared error of four-fold cross-validation compares the recommender system predictions to the ground truth human interpretations. The recommender system is competitive and often outperforms state of the art spline interpolation methods. Lastly, increasing the size of the training dataset decreases the prediction error, and that variance in error decreases with increasing formation tops picked in each formation and well for the lithostratigraphic top picks.
High-performance automatic categorization and attribution of inventory catalogs
Authors: Anton Kolonin
Date: 2022-02-09 00:00:00
Tasks: BIG-bench Machine Learning, Text Categorization, Vocal Bursts Intensity Prediction

Techniques of machine learning for automatic text categorization are applied and adapted for the problem of inventory catalog data attribution, with different approaches explored and optimal solution addressing the tradeoff between accuracy and performance is selected.
Understanding and Shifting Preferences for Battery Electric Vehicles
Authors: Nikos Arechiga, Francine Chen, Rumen Iliev, Emily Sumner, Scott Carter, Alex Filipowicz, Nayeli Bravo, Monica Van, Kate Glazko, Kalani Murakami, Laurent Denoue, Candice Hogan, Katharine Sieck, Charlene Wu, Kent Lyons
Date: 2022-02-09 00:00:00
Tasks: Reinforcement Learning (RL)

Identifying personalized interventions for an individual is an important task. Recent work has shown that interventions that do not consider the demographic background of individual consumers can, in fact, produce the reverse effect, strengthening opposition to electric vehicles. In this work, we focus on methods for personalizing interventions based on an individual's demographics to shift the preferences of consumers to be more positive towards Battery Electric Vehicles (BEVs). One of the constraints in building models to suggest interventions for shifting preferences is that each intervention can influence the effectiveness of later interventions. This, in turn, requires many subjects to evaluate effectiveness of each possible intervention. To address this, we propose to identify personalized factors influencing BEV adoption, such as barriers and motivators. We present a method for predicting these factors and show that the performance is better than always predicting the most frequent factors. We then present a Reinforcement Learning (RL) model that learns the most effective interventions, and compare the number of subjects required for each approach.
Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation
Authors: Qijie Shen, Hong Wen, Wanjie Tao, Jing Zhang, Fuyu Lv, Zulong Chen, Zhao Li
Date: 2022-02-05 00:00:00
Tasks: Click-Through Rate Prediction

In many classical e-commerce platforms, personalized recommendation has been proven to be of great business value, which can improve user satisfaction and increase the revenue of platforms. In this paper, we present a new recommendation problem, Trigger-Induced Recommendation (TIR), where users' instant interest can be explicitly induced with a trigger item and follow-up related target items are recommended accordingly. TIR has become ubiquitous and popular in e-commerce platforms. In this paper, we figure out that although existing recommendation models are effective in traditional recommendation scenarios by mining users' interests based on their massive historical behaviors, they are struggling in discovering users' instant interests in the TIR scenario due to the discrepancy between these scenarios, resulting in inferior performance. To tackle the problem, we propose a novel recommendation method named Deep Interest Highlight Network (DIHN) for Click-Through Rate (CTR) prediction in TIR scenarios. It has three main components including 1) User Intent Network (UIN), which responds to generate a precise probability score to predict user's intent on the trigger item; 2) Fusion Embedding Module (FEM), which adaptively fuses trigger item and target item embeddings based on the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM), which can effectively highlight users' instant interest from their behaviors based on the result of FEM. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of DIHN over state-of-the-art methods.
Toward a traceable, explainable, and fairJD/Resume recommendation system
Authors: Amine Barrak, Bram Adams, Amal Zouaq
Date: 2022-02-02 00:00:00

In the last few decades, companies are interested to adopt an online automated recruitment process in an international recruitment environment. The problem is that the recruitment of employees through the manual procedure is a time and money consuming process. As a result, processing a significant number of applications through conventional methods can lead to the recruitment of clumsy individuals. Different JD/Resume matching model architectures have been proposed and reveal a high accuracy level in selecting relevant candidatesfor the required job positions. However, the development of an automatic recruitment system is still one of the main challenges. The reason is that the development of a fully automated recruitment system is a difficult task and poses different challenges. For example, providing a detailed matching explanation for the targeted stakeholders is needed to ensure a transparent recommendation. There are several knowledge bases that represent skills and competencies (e.g, ESCO, O*NET) that are used to identify the candidate and the required job skills for a matching purpose. Besides, modernpre-trained language models are fine-tuned for this context such as identifying lines where a specific feature was introduced. Typically, pre-trained language models use transfer-based machine learning models to be fine-tuned for a specific field. In this proposal, our aim is to explore how modern language models (based on transformers) can be combined with knowledge bases and ontologies to enhance the JD/Resume matching process. Our system aims at using knowledge bases and features to support the explainability of the JD/Resume matching. Finally, given that multiple software components, datasets, ontology, andmachine learning models will be explored, we aim at proposing a fair, ex-plainable, and traceable architecture for a Resume/JD matching purpose.
Controllability of Networked Sampled-data Systems
Authors: Zixuan Yang, Xiaofan Wang, Lin Wang
Date: 2022-02-18 00:00:00

The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively. The effects of sampling on the controllability of networked systems are analyzed, with some necessary and/or sufficient controllability conditions derived. Different from the sampling control of single systems, the pathological sampling of node systems could be eliminated by an appropriate design of network structure and inner couplings. While for singular topology matrices, the pathological sampling of single nodes will cause the entire system to lose controllability. Moreover, any periodic sampling will not affect the controllability of networked systems with specific node dynamics. All the results indicate that whether a networked system is under pathological sampling or not is jointly determined by mutually coupled factors.
Ray-transfer functions for camera simulation of 3D scenes with hidden lens design
Authors: Thomas Goossens, Zheng Lyu, Jamyuen Ko, Gordon Wan, Joyce Farrell, Brian Wandell
Date: 2022-02-17 00:00:00
Tasks: Position

Combining image sensor simulation tools (e.g., ISETCam) with physically based ray tracing (e.g., PBRT) offers possibilities for designing and evaluating novel imaging systems as well as for synthesizing physically accurate, labeled images for machine learning. One practical limitation has been simulating the optics precisely: Lens manufacturers generally prefer to keep lens design confidential. We present a pragmatic solution to this problem using a black box lens model in Zemax; such models provide necessary optical information while preserving the lens designer's intellectual property. First, we describe and provide software to construct a polynomial ray transfer function that characterizes how rays entering the lens at any position and angle subsequently exit the lens. We implement the ray-transfer calculation as a camera model in PBRT and confirm that the PBRT ray-transfer calculations match the Zemax lens calculations for edge spread functions and relative illumination.
Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models
Authors: Sarala Padi, Seyed Omid Sadjadi, Dinesh Manocha, Ram D. Sriram
Date: 2022-02-16 00:00:00
Tasks: Data Augmentation, Emotional Intelligence, Emotion Recognition, Multimodal Emotion Recognition, Speaker Recognition, Transfer Learning

Automatic emotion recognition plays a key role in computer-human interaction as it has the potential to enrich the next-generation artificial intelligence with emotional intelligence. It finds applications in customer and/or representative behavior analysis in call centers, gaming, personal assistants, and social robots, to mention a few. Therefore, there has been an increasing demand to develop robust automatic methods to analyze and recognize the various emotions. In this paper, we propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. More specifically, we i) adapt a residual network (ResNet) based model trained on a large-scale speaker recognition task using transfer learning along with a spectrogram augmentation approach to recognize emotions from speech, and ii) use a fine-tuned bidirectional encoder representations from transformers (BERT) based model to represent and recognize emotions from the text. The proposed system then combines the ResNet and BERT-based model scores using a late fusion strategy to further improve the emotion recognition performance. The proposed multimodal solution addresses the data scarcity limitation in emotion recognition using transfer learning, data augmentation, and fine-tuning, thereby improving the generalization performance of the emotion recognition models. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture (IEMOCAP) dataset. Experimental results indicate that both audio and text-based models improve the emotion recognition performance and that the proposed multimodal solution achieves state-of-the-art results on the IEMOCAP benchmark.
Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total Network Power?
Authors: Özlem Tuğfe Demir, Meysam Masoudi, Emil Björnson, Cicek Cavdar
Date: 2022-02-18 00:00:00

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).
Task-oriented Scheduling for Networked Control Systems: An Age of Information-Aware Implementation on Software-defined Radios
Authors: Onur Ayan, Polina Kutsevol, Hasan Yağız Özkan, Wolfgang Kellerer
Date: 2022-02-18 00:00:00
Tasks: Autonomous Driving, Scheduling

Networked control systems (NCSs) are feedback control loops that are closed over a communication network. Emerging applications, such as telerobotics, drones and autonomous driving are the most prominent examples of such systems. Regular and timely information sharing between the components of NCSs is essential to fulfill the desired control tasks, as stale information can lead to performance degradation or even physical damage. In this work, we consider multiple heterogeneous NCSs that transmit their system state over a shared physical wireless channel towards a gateway node. We conduct a comprehensive experimental study on selected MAC protocols using software-defined radios with state-of-the-art (SotA) solutions that have been designed to increase information freshness and control performance. As a significant improvement over the SotA, we propose a novel contention-free algorithm that is able to outperform the existing solutions by combining their strengths in one protocol. In addition, we propose a new metric called normalized mean squared error that maps the age of information to a dimensionless quantity that captures the expected value of a control system's next transmission. We demonstrate its adoption and effectiveness for wireless resource scheduling in a case study involving multiple inverted pendulums. From our experimental study and results, we observe that value-aware prioritization of the sub-systems contributes to minimizing the negative effects of information staleness on control performance. In particular, as the number of devices increases, the benefit of control-awareness to the quality of control stands out when compared to protocols that focus solely on maximizing information freshness.
Quantisation-aware Precoding for MU-MIMO with Limited-capacity Fronthaul
Authors: Yasaman Khorsandmanesh, Emil Björnson, Joakim Jaldèn
Date: 2022-02-17 00:00:00
Tasks: Quantization

Base stations in 5G and beyond use advanced antenna systems (AASs), where multiple passive antenna elements and radio units are integrated into a single box. A critical bottleneck of such a system is the digital fronthaul between the AAS and baseband unit (BBU), which has limited capacity. In this paper, we study an AAS used for precoded downlink transmission over a multi-user multiple-input multiple-output (MU-MIMO) channel. First, we present the baseline quantization-unaware precoding scheme created when a precoder is computed at the BBU and then quantized to be sent over the fronthaul. We propose a new precoding design that is aware of the fronthaul quantization. We formulate an optimization problem to minimize the mean squared error at the receiver side. We rewrite the problem to utilize mixed-integer programming to solve it. The numerical results manifest that our proposed precoding greatly outperforms quantization-unaware precoding in terms of sum rate.
Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?
Authors: Ali Nouruzi, Atefeh Rezaei, Ata Khalili, Nader Mokari, Mohammad Reza Javan, Eduard A. Jorswieck, Halim Yanikomeroglu
Date: 2022-02-18 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management

In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs.
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control
Authors: Killian Dally, Erik-Jan van Kampen
Date: 2022-02-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including a coordinated 40 degree bank climbing turn with a normalized Mean Absolute Error of 2.64%. The controller is robust to six failure cases, including the rudder jammed at -15 deg, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.
Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
Authors: Daniel Bogdoll, Moritz Nekolla, Tim Joseph, J. Marius Zöllner
Date: 2022-02-07 00:00:00
Tasks: Autonomous Driving

Driving on roads is restricted by various traffic rules, aiming to ensure safety for all traffic participants. However, human road users usually do not adhere to these rules strictly, resulting in varying degrees of rule conformity. Such deviations from given rules are key components of today's road traffic. In autonomous driving, robotic agents can disturb traffic flow, when rule deviations are not taken into account. In this paper, we present an approach to derive the distribution of degrees of rule conformity from human driving data. We demonstrate our method with the Waymo Open Motion dataset and Safety Distance and Speed Limit rules.