pwc-archive/papers-with-abstracts huggingface dataset:
Collaboration in Coworking Spaces: Impact on Firm Innovativeness and Business Models
Authors: M. Moore
Date: 2021-11-18 00:00:00
Tasks: Management

Purpose: The purpose of this paper is to contribute to the debate as to whether collaboration in coworking spaces contributes to firm innovativeness and impacts the business models of organizations in a positive manner. Methodology: This paper includes primary data from 75 organizations in 17 coworking spaces and uses quantitative research methods. The methodology includes multiple statistical methods, such as principal component analysis, correlation analysis as well as linear and binary regression analysis. Results: The results show a positive interrelation between collaboration and innovation, indicating that coworkers are able to improve their innovative capabilities by making use of strategic partnerships in coworking spaces. Further, this study shows that business models are significantly affected by the level of collaboration in coworking spaces, which suggests that coworking is a promoting force for business model development or business model innovation. Contributions: The paper contributes to management literature and represents the first empirical investigations which focuses on the effects of collaboration on a firm-level in coworking spaces. Practical implications: The results indicate that organizations in coworking spaces should embrace a collaborative mindset and should actively seek out collaborative alliances and partnerships, as doing such is shown to increase their innovativeness and/or develop their business model. Future Research: Future research should focus on the antecedents of collaboration or could investigate the effects of collaboration in coworking spaces on a community level.
The Curious Case of the 2021 Minneapolis Ward 2 City Council Election
Authors: David McCune, Lori McCune
Date: 2021-11-18 00:00:00

In this article we explain why the November 2021 election for the Ward 2 city council seat in Minneapolis, MN, may be the mathematically most interesting ranked choice election in US history.
Opinion Dynamics with Conflicting Interests
Authors: Patrick Mellacher
Date: 2021-11-17 00:00:00
Tasks: Decision Making

I develop a rather simple agent-based model to capture a co-evolution of opinion formation, political decision making and economic outcomes. I use this model to study how societies form opinions if their members have opposing interests. Agents are connected in a social network and exchange opinions, but differ with regard to their interests and ability to gain information about them. I show that inequality in information and economic resources can have a drastic impact on aggregated opinion. In particular, my model illustrates how a tiny, but well-informed minority can influence group decisions to their favor. This effect is amplified if these agents are able to command more economic resources to advertise their views and if they can target their advertisements efficiently, as made possible by the rise of information technology. My results contribute to the understanding of pressing questions such as climate change denial and highlight the dangers that economic and information inequality can pose for democracies.
Growth, Inequality and Declining Business Dynamism in a Unified Schumpeter Mark I + II Model
Authors: Patrick Mellacher
Date: 2021-11-17 00:00:00
Tasks: counterfactual

I develop a simple Schumpeterian agent-based model where the entry and exit of firms, their productivity and markup, the birth of new industries and the social structure of the population are endogenous and use it to study the causes of rising inequality and "declining business dynamism" since the 1980s. My hybrid model combines features of i) the so-called Schumpeter Mark I (centering around the entrepreneur), ii) the Mark II model (emphasizing the innovative capacities of firms), and iii) Cournot competition, with firms using OLS learning to estimate the market environment and the behavior of their competitors. A scenario which is quantitatively calibrated to US data on growth and inequality replicates a large number of stylized facts regarding the industry life-cycle, growth, inequality and all ten stylized facts on "declining business dynamism" proposed by Akcigit and Ates (AEJ:Macro, 2021). Counterfactual simulations show that antitrust policy is highly effective at combatting inequality and increasing business dynamism and growth, but is subject to a conflict of interest between workers and firm owners, as GDP and wages grow at the expense of profits. Technological factors, on the other hand, are much less effective in combatting declining business dynamism in my model.
Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation
Authors: Hao Xu, Steven A. Niederer, Steven E. Williams, David E. Newby, Michelle C. Williams, Alistair A. Young
Date: 2021-11-18 00:00:00
Tasks: Anatomy, Segmentation

Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcella-tion, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com.
End-to-End Learned Image Compression with Quantized Weights and Activations
Authors: Heming Sun, Lu Yu, Jiro Katto
Date: 2021-11-17 00:00:00
Tasks: Image Compression, MS-SSIM, Quantization, SSIM

End-to-end Learned image compression (LIC) has reached the traditional hand-crafted methods such as BPG (HEVC intra) in terms of the coding gain. However, the large network size prohibits the usage of LIC on resource-limited embedded systems. This paper reduces the network complexity by quantizing both weights and activations. 1) For the weight quantization, we study different kinds of grouping and quantization scheme at first. A channel-wise non-linear quantization scheme is determined based on the coding gain analysis. After that, we propose a fine tuning scheme to clip the weights within a certain range so that the quantization error can be reduced. 2) For the activation quantization, we first propose multiple non-linear quantization codebooks with different maximum dynamic ranges. By selecting an optimal one through a multiplexer, the quantization range can be saturated to the greatest extent. In addition, we also exploit the mean-removed quantization for the analysis transform outputs in order to reduce the bit-width cost for the specific channel with the large non-zero mean. By quantizing each weight and activation element from 32-bit floating point to 8-bit fixed point, the memory cost for both weight and activation can be reduced by 75% with negligible coding performance loss. As a result, our quantized LIC can still outperform BPG in terms of MS-SSIM. To our best knowledge, this is the first work to give a complete analysis on the coding gain and the memory cost for a quantized LIC network, which validates the feasibility of the hardware implementation.
Visual design intuition: Predicting dynamic properties of beams from raw cross-section images
Authors: Philippe M. Wyder, Hod Lipson
Date: 2021-11-14 00:00:00

In this work we aim to mimic the human ability to acquire the intuition to estimate the performance of a design from visual inspection and experience alone. We study the ability of convolutional neural networks to predict static and dynamic properties of cantilever beams directly from their raw cross-section images. Using pixels as the only input, the resulting models learn to predict beam properties such as volume maximum deflection and eigenfrequencies with 4.54% and 1.43% Mean Average Percentage Error (MAPE) respectively, compared to the Finite Element Analysis (FEA) approach. Training these models doesn't require prior knowledge of theory or relevant geometric properties, but rather relies solely on simulated or empirical data, thereby making predictions based on "experience" as opposed to theoretical knowledge. Since this approach is over 1000 times faster than FEA, it can be adopted to create surrogate models that could speed up the preliminary optimization studies where numerous consecutive evaluations of similar geometries are required. We suggest that this modeling approach would aid in addressing challenging optimization problems involving complex structures and physical phenomena for which theoretical models are unavailable.
A New RIS Architecture with a Single Power Amplifier: Energy Efficiency and Error Performance Analysis
Authors: Recep A. Tasci, Fatih Kilinc, Ertugrul Basar, George C. Alexandropoulos
Date: 2021-11-18 00:00:00

Reconfigurable intelligent surface (RIS)-assisted communication have recently attracted the attention of the wireless communication community as a potential candidate for the next $6$-th generation (6G) of wireless networks. Various studies have been carried out on the RIS technology, which is capable of enabling the control of the signal propagation environment by network operators. However, when an RIS is used in its inherently passive structure, it appears to be only a supportive technology for communications, while suffering from a multiplicative path loss. Therefore, researchers have lately begun to focus on RIS hardware designs with minimal active elements to further boost the benefits of this technology. In this paper, we present a simple RIS hardware architecture including a single and variable gain amplifier for reflection amplification to confront the multiplicative path loss. The end-to-end signal model for communication systems assisted with the proposed amplifying RIS design is presented, together with an analysis focusing on the capacity maximization and theoretical bit error probability performance, which is corroborated by computer simulations. In addition, the major advantages of the proposed amplifying RIS design compared to its passive counterpart are discussed. It is shown that the proposed RIS-based wireless system significantly eliminates the double fading problem appearing in conventional passive RIS-assisted systems and improves the communication energy efficiency.
Theoretical Linear Convergence of Deep Unfolding Network for Block-Sparse Signal Recovery
Authors: Rong Fu, Vincent Monardo, Tianyao Huang, Yimin Liu
Date: 2021-11-18 00:00:00

In this paper, we consider the recovery of the high-dimensional block-sparse signal from a compressed set of measurements, where the non-zero coefficients of the recovered signal occur in a small number of blocks. Adopting the idea of deep unfolding, we explore the block-sparse structure and put forward a block-sparse reconstruction network named Ada-BlockLISTA, which performs gradient descent on every single block followed by a block-wise shrinkage. Furthermore, we prove the linear convergence rate of our proposed network, which also theoretically guarantees exact recovery for a potentially higher sparsity level based on underlyingblock structure. Numerical results indicate that Ada-BlockLISTA yields better signal recovery performance compared with existing algorithms, which ignore the additional block structure in the signal model.
A Supervised Learning Framework for Joint Angle-of-Arrival and Source Number Estimation
Authors: Noud Kanters, Andrés Alayón Glazunov
Date: 2021-11-18 00:00:00

Machine learning is a promising technique for angle-of-arrival (AOA) estimation of waves impinging a sensor array. However, the majority of the methods proposed so far only consider a known, fixed number of impinging waves, i.e., a fixed source number. This paper proposes a machine-learning-based estimator designed for the case when the source number is variable and hence unknown a priori. The proposed estimator comprises a framework of single-label classifiers. Each classifier predicts if waves are present within certain randomly selected segments of the array's field of view (FOV), resulting from discretising the FOV with a certain (FOV) resolution. The classifiers' predictions are combined into a probabilistic angle spectrum, whereupon the source number and the AOAs are estimated jointly by applying a probability threshold whose optimal level is learned from data. The estimator's performance is assessed using a new performance metric: the joint AOA estimation success rate. Numerical simulations show that for low SNR (-10 dB), a low FOV resolution (2$^\circ$) yields a higher success rate than a high resolution (1$^\circ$), whereas the opposite applies for mid (0 dB) and high (10 dB) SNRs. In nearly all simulations, except one at low SNR and a high FOV resolution, the proposed estimator outperforms the MUSIC algorithm if the maximum allowed AOA estimation error is approximately equal to (or larger than) the FOV resolution.
Out-of-Zone Signal Leakage Sensing in Radio Dynamic Zones
Authors: Sung Joon Maeng, İsmail Güvenç, Mihail L. Sichitiu, Ozgur Ozdemir
Date: 2021-11-18 00:00:00

Radio dynamic zones (RDZs) are geographically bounded areas where novel advanced wireless technologies can be developed, tested, and improved, without the concern of interfering to other incumbent radio technologies nearby the RDZ. In order to operate an RDZ, use of a real-time spectrum monitoring system carries critical importance. Such a monitoring system should detect out-of-zone (OoZ) signal leakage outside of the RDZ, and if the interference to nearby receivers is intolerable, the monitoring system should be capable of mitigating such interference. This can e.g. be achieved by stopping operations inside the RDZ or switching to other bands for RDZ operation. In this paper, we introduce a spectrum monitoring concept for OoZ signal leakage detection at RDZs, where sensor nodes (SNs) are installed at the boundary of an RDZ and monitor the power leakage from multiple transmitters within the RDZ. We propose a prediction algorithm that estimates the received interference at OoZ geographical locations outside of the RDZ, using the measurements obtained at sparsely located SNs at the RDZ boundary. Using computer simulations, we evaluate the performance of the proposed algorithm and study its sensitivity to SN deployment density.
Casting graph isomorphism as a point set registration problem using a simplex embedding and sampling
Authors: Yigit Oktar
Date: 2021-11-15 00:00:00

Graph isomorphism is an important problem as its worst-case time complexity is not yet fully understood. In this study, we try to draw parallels between a related optimization problem called point set registration. A graph can be represented as a point set in enough dimensions using a simplex embedding and sampling. Given two graphs, the isomorphism of them corresponds to the existence of a perfect registration between the point set forms of the graphs. In the case of non-isomorphism, the point set form optimization result can be used as a distance measure between two graphs having the same number of vertices and edges. The related idea of equivalence classes suggests that graph canonization may be an important tool in tackling graph isomorphism problem and an orthogonal transformation invariant feature extraction based on this high dimensional point set representation may be fruitful. The concepts presented can also be extended to automorphism, and subgraph isomorphism problems and can also be applied on hypergraphs with certain modifications.
Gradient flows on graphons: existence, convergence, continuity equations
Authors: Sewoong Oh, Soumik Pal, Raghav Somani, Raghavendra Tripathi
Date: 2021-11-18 00:00:00

Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems. They typically arise as the continuum limit of exchangeable particle systems evolving by some mean-field interaction involving a gradient-type potential. However, in many problems, such as in multi-layer neural networks, the so-called particles are edge weights on large graphs whose nodes are exchangeable. Such large graphs are known to converge to continuum limits called graphons as their size grow to infinity. We show that the Euclidean gradient flow of a suitable function of the edge-weights converges to a novel continuum limit given by a curve on the space of graphons that can be appropriately described as a gradient flow or, more technically, a curve of maximal slope. Several natural functions on graphons, such as homomorphism functions and the scalar entropy, are covered by our set-up, and the examples have been worked out in detail.
On the emergence of single versus multi-state allostery
Authors: Eric Rouviere, Rama Ranganathan, Olivier Rivoire
Date: 2021-11-17 00:00:00

Several physical mechanisms have been proposed to explain allostery in proteins. They differ by the number of internal states that they assume a protein to occupy, leaving open the question of what controls the emergence of these distinct physical forms of allostery. Here, we analyze a simplified model of protein allostery under a range of physical and evolutionary constraints. We find that two archetypal mechanisms can emerge through evolution: a single-state mechanism where ligand binding induces a displacement along a soft normal mode or a multi-state mechanism where ligand binding induces a switch across an energy barrier to a different stable state. Importantly, whenever the two mechanisms are possible, the multi-state mechanism confers a stronger allosteric effect and thus a selective advantage. This work defines the essential constraints on single or multi-state allostery, and sets the stage for a physical theory of its evolutionary origins.
SARS-CoV-2's closest relative, RaTG13, was generated from a bat transcriptome not a fecal swab: implications for the origin of COVID-19
Authors: Steven E Massey
Date: 2021-11-18 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Virology

RaTG13 is the closest related coronavirus genome phylogenetically to SARS-CoV-2, consequently understanding its provenance is of key importance to understanding the origin of the COVID-19 pandemic. The RaTG13 NGS dataset is attributed to a fecal swab from the intermediate horseshoe bat Rhinolophus affinis. However, sequence analysis reveals that this is unlikely. Metagenomic analysis using Metaxa2 shows that only 10.3 % of small subunit (SSU) rRNA sequences in the dataset are bacterial, inconsistent with a fecal sample, which are typically dominated by bacterial sequences. In addition, the bacterial taxa present in the sample are inconsistent with fecal material. Assembly of mitochondrial SSU rRNA sequences in the dataset produces a contig 98.7 % identical to R.affinis mitochondrial SSU rRNA, indicating that the sample was generated from this or a closely related species. 87.5 % of the NGS reads map to the Rhinolophus ferrumequinum genome, the closest bat genome to R.affinis available. In the annotated genome assembly, 62.2 % of mapped reads map to protein coding genes. These results clearly demonstrate that the dataset represents a Rhinolophus sp. transcriptome, and not a fecal swab sample. Overall, the data show that the RaTG13 dataset was generated by the Wuhan Institute of Virology (WIV) from a transcriptome derived from Rhinolophus sp. tissue or cell line, indicating that RaTG13 was in live culture. This raises the question of whether the WIV was culturing additional unreported coronaviruses closely related to SARS-CoV-2 prior to the pandemic. The implications for the origin of the COVID-19 pandemic are discussed.
Charting and navigating the space of solutions for recurrent neural networks
Authors: Elia Turner, Kabir Dabholkar, Omri Barak
Date: 2021-11-17 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: BIG-bench Machine Learning

Recurrent Neural Networks (RNNs) were recently successfully used to model the way neural activity drives task-related behavior in animals, operating under the implicit assumption that the obtained solutions are universal. Observations in both neuroscience and machine learning challenge this assumption. Animals can approach a given task with a variety of strategies, and training machine learning algorithms introduces the phenomenon of underspecification. These observations imply that every task is associated with a space of solutions. To date, the structure of this space is not understood, limiting the approach of comparing RNNs with neural data. Here, we characterize the space of solutions associated with various tasks. We first study a simple two-neuron network on a task that leads to multiple solutions. We trace the nature of the final solution back to the network's initial connectivity and identify discrete dynamical regimes that underlie this diversity. We then examine three neuroscience-inspired tasks: Delayed and interval discrimination, and Time reproduction. For each task, we find a rich set of solutions. Variability can be found directly in the neural activity of the networks, and additionally by testing the trained networks' ability to extrapolate, as a perturbation to a system often reveals hidden structure. Furthermore, we relate extrapolation patterns to specific dynamical objects and effective algorithms found by the networks. We introduce a tool to derive the reduced dynamics of networks by generating a compact directed graph describing the essence of the dynamics with regards to behavioral inputs and outputs. Using this representation, we can partition the solutions to each task into a handful of types and partially predict them from neural features. Our results shed light on the concept of the space of solutions and its uses in Machine learning and in Neuroscience.