pwc-archive/papers-with-abstracts huggingface dataset:
Cross-validated covariance estimators for high-dimensional minimum-variance portfolios
Authors: Sven Husmann, Antoniya Shivarova, Rick Steinert
Date: 2019-10-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the sample covariance estimator becomes ill-conditioned and leads to suboptimal portfolios out-of-sample. To address this issue, we review recently proposed efficient estimation methods for the covariance matrix and extend the literature by suggesting a multi-fold cross-validation technique for selecting the necessary tuning parameters within each method. Conducting an extensive empirical analysis with four datasets based on the S&P 500, we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio. In addition, we identify estimators that are strongly influenced by the choice of the tuning parameter and detect a clear relationship between the selection criterion within the cross-validation and the evaluated performance measure.
Costly Verification in Collective Decisions
Date: 2020-02-20 00:00:00

We study how a principal should optimally choose between implementing a new
policy and maintaining the status quo when information relevant for the
decision is privately held by agents. Agents are strategic in revealing their
information; the principal cannot use monetary transfers to elicit this
information, but can verify an agent's claim at a cost. We characterize the
mechanism that maximizes the expected utility of the principal. This mechanism
can be implemented as a cardinal voting rule, in which agents can either cast a
baseline vote, indicating only whether they are in favor of the new policy, or
they make specific claims about their type. The principal gives more weight to
specific claims and verifies a claim whenever it is decisive.
An N-Path Filter with Multiphase PWM Clocks for Harmonic Response Suppression
Date: 2019-10-30 00:00:00

A switched-capacitor N-path circuit can be employed for filtering an RF
signal, as well as a passive downconverter. A known limitation of an N-path
filter is that in addition to downconverting signals around the desired center
frequency, the circuit also downconverts signals located around harmonics of
the center frequency. An N-path filter that uses a PWM representation of a
sinusoidal LO to mitigate harmonic downconversion is proposed in this work.
Single-edge natural sampling pulse-width modulated (PWM) clocks are used to
drive the switches in the N-path filter. The potential for employing PWM for
providing gain control is also described.
Firm Entry and Exit with Unbounded Productivity Growth
Authors: John Stachurski
Date: 2019-10-29 00:00:00

In Hopenhayn's (1992) entry-exit model productivity is bounded, implying that the predicted firm size distribution cannot match the power law tail observable in the data. In this paper we remove the boundedness assumption and, in this more general setting, provide an exact characterization of existence of stationary equilibria, as well as a novel sufficient condition for existence based on treating production as a Lyapunov function. We also provide new representations of the rate of entry and aggregate supply. Finally, we prove that the firm size distribution has a power law tail under a very broad set of productivity growth specifications.
Duality and Stability in Complex Multiagent State-Dependent Network Dynamics
Date: 2020-07-19 00:00:00

Despite significant progress on stability analysis of conventional multiagent
networked systems with weakly coupled state-network dynamics, most of the
existing results have shortcomings in addressing multiagent systems with highly
coupled state-network dynamics. Motivated by numerous applications of such
dynamics, in our previous work [1], we initiated a new direction for stability
analysis of such systems that uses a sequential optimization framework.
Building upon that, in this paper, we extend our results by providing another
angle on multiagent network dynamics from a duality perspective, which allows
us to view the network structure as dual variables of a constrained nonlinear
program. Leveraging that idea, we show that the evolution of the coupled
state-network multiagent dynamics can be viewed as iterates of a primal-dual
algorithm for a static constrained optimization/saddle-point problem. This view
bridges the Lyapunov stability of state-dependent network dynamics and
frequently used optimization techniques such as block coordinated descent,
mirror descent, the Newton method, and the subgradient method. As a result, we
develop a systematic framework for analyzing the Lyapunov stability of
state-dependent network dynamics using techniques from nonlinear optimization.
Finally, we support our theoretical results through numerical simulations from
social science.
Evaluating a Semi-Autonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision
Date: 2019-10-30 00:00:00
Tasks: Brain Computer Interface, Motor Imagery

In this paper, we evaluate a semi-autonomous brain-computer interface (BCI)
for manipulation tasks. In such system, the user controls a robotic arm through
motor imagery commands. In traditional process-control BCI systems, the user
has to provide those commands continuously in order manipulate the effector of
the robot step-by-step, which results in a tiresome process for simple tasks
such as pick and replace an item from a surface. Here, we take a
semi-autonomous approach based on a conformal geometric algebra model that
solves the inverse kinematics of the robot on the fly, then the user only has
to decide on the start of the movement and the final position of the effector
(goal-selection approach). Under these conditions, we implemented
pick-and-place tasks with a disk as an item and two target areas placed on the
table at arbitrary positions. An artificial vision (AV) algorithm was used to
obtain the positions of the items expressed in the robot frame through images
captured with a webcam. Then, the AV algorithm is integrated to the inverse
kinematics model to perform the manipulation tasks. As proof-of-concept,
different users were trained to control the pick-and-place tasks through the
process-control and semi-autonomous goal-selection approaches, so that the
performance of both schemes could be compared. Our results show the superiority
in performance of the semi-autonomous approach, as well as evidence of less
mental fatigue with it.
Extraction and integration of genetic networks from short-profile omic datasets
Authors: Jacopo Iacovacci, Alina Peluso, Timothy Ebbels, Markus Ralser, Robert Charles Glen
Date: 2019-10-31 00:00:00

Mass-spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile at the genome scale intracellular concentrations of e.g. amino acids or elements. Short profiles of molecular or sub-molecular features are intrinsically non-Gaussian and may reveal patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here we introduce two profile similarity measures that enforce information from the empirical covariance matrix of the data, the Mahalanobis cosine and the hybrid-Mahalanobis cosine. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from omics data by analysing experimental datasets derived from the ionome and the metabolome of the model organism S. cerevisiae, and several large curated databases of genetic annotations. The proposed covariance-based similarity measures can in general recover known and predicted associations between genes better than the commonly used Pearson's correlation and the standard cosine similarity. The choice of which of the two measures to recommend depends upon whether the focus is on extracting genetic associations at a global or local genetic network scale.
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System
Authors: M. Sirlanci, M. E. Levine, C. C. Low Wang, D. J. Albers, A. M. Stuart
Date: 2019-10-31 00:00:00
Tasks: Nutrition, parameter estimation

In this paper, we build a new, simple, and interpretable mathematical model to estimate and forecast physiology related to the human glucose-insulin system, constrained by available data. By constructing a simple yet flexible model class with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU); different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body and two other terms representing the effect of nutrition and externally delivered insulin. The stochastic fluctuations encapsulate model error necessitated by the simple model form and enable flexible incorporation of data. The model parameters must be learned in a patient-specific fashion, leading to personalized models. We present experimental results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems that are robust to model imperfections and noisy data. Finally, the model's predictive capability is compared with two different models built explicitly for T2DM and ICU contexts.
A 71-76 and 81-86GHz, Scaled 16-Element Transceiver Phased Array with Shared Image Selection Weaver Architecture, 25% EIRP to PDC, and Low EVM Variation
Date: 2019-10-31 00:00:00

A 16-element, compact phased array transceiver is demonstrated at E-band
(71-76 and 81-86GHz). A Weaver image selection architecture reduces the LO
tuning range to 3GHz while covering the 10GHz band. The bidirectional and
efficient implementation of Weaver architecture in scalable array is proposed
using a bidirectional shared image selection IF mixer. The beam is steered
across +/-30{\deg} with an average 30dBm EIRP across the upper and lower bands
using narrowband LO phase shifter. A 1.5 GHz modulation bandwidth for 16 QAM
and 64 QAM waveform is generated with EVM better than -19 dB/ -24 dB,
respectively. The 2x2 transceiver die is implemented in SiGe BiCMOS and is
assembled with 16-elements while operating at a 25% EIRP/PDC efficiency.
A Concise Survey of G4U
Date: 2019-10-31 00:00:00
Tasks: Survey

The general four-component model-based decomposition with unitary
transformation of coherency matrix (G4U) is a state-of-the-art four-component
decomposition, which has received extensive attentions recently. A literature
survey is carried out to indicate the overall influence, improvement,
development, evaluation, and application of G4U. Totally, 137 literatures are
found mentioning G4U in Google Scholar\c{opyright} until October 7, 2019, which
can be attributed into 4 categories in terms of the degree of concentration and
17 subcategories according to the focus of attention. Among these literatures,
61 of them simply mention G4U mainly because it is a new four-component
model-based decomposition, a typical model-based decomposition, or even a
target decomposition. There are also 9 literatures which improve G4U and
develop G4U-like decompositions with unitary transformation of coherency
matrix. 20 literatures generally use G4U as a typical target decomposition for
comparison or a pseudo-color visualization technique to show the performance of
some developed approaches. There are also 47 literatures dedicated to
critically evaluate and deeply apply G4U in the remote sensing of forestry,
agriculture, wetland, snow, glaciated terrain, earth surface, manmade target,
environment, and damages caused by earthquake, tsunami, and landside, which
indicates the value and significance of G4U in the true sense.
How Turing parasites expand the computational landscape of digital life
Authors: Seoane LF, Solé R
Date: 2019-10-31 00:00:00

Why are living systems complex? Why does the biosphere contain living beings with complexity features beyond those of the simplest replicators? What kind of evolutionary pressures result in more complex life forms? These are key questions that pervade the problem of how complexity arises in evolution. One particular way of tackling this is grounded in an algorithmic description of life: living organisms can be seen as systems that extract and process information from their surroundings in order to reduce uncertainty. Here we take this computational approach using a simple bit string model of coevolving agents and their parasites. While agents try to predict their worlds, parasites do the same with their hosts. The result of this process is that, in order to escape their parasites, the host agents expand their computational complexity despite the cost of maintaining it. This, in turn, is followed by increasingly complex parasitic counterparts. Such arms races display several qualitative phases, from monotonous to punctuated evolution or even ecological collapse. Our minimal model illustrates the relevance of parasites in providing an active mechanism for expanding living complexity beyond simple replicators, suggesting that parasitic agents are likely to be a major evolutionary driver for biological complexity.
Prediction of 5-hydroxytryptamine Transporter Inhibitor based on Machine Learning
Date: 2019-10-31 00:00:00
Tasks: BIG-bench Machine Learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve
the condition. Topological fingerprints, ECFP4, and molecular descriptors were
used. Some SERT and small molecules combined prediction models were established
by using 5 machine learning methods. We selected the higher accuracy models(RF,
SVM, LR) in five-fold cross-validation of training set to establish an
integrated model (VOL_CLF). The training set is from Chembl database and
oversampled by SMOTE algorithm to eliminate data imbalance. The unbalanced data
from same sources (Chembl) was used as Test set 1; the unbalanced data with
different sources(Drugbank) was used as Test set 2 . The prediction accuracy of
SERT inhibitors in Test set 1 was 90.7%~93.3%(VOL_CLF method was the highest);
the inhibitory recall rate was 84.6%-90.1%(RF method was the highest); the
non-inhibitor prediction accuracy rate was 76.1%~80.2%(RF method is the
highest); the non-inhibitor predictive recall rate is 81.2%~87.5% (SVM and
VOL_CLF methods were the highest) The RF model in Test Set 2 performed better
than the other models. The SERT inhibitor predicted accuracy rate, recall rate,
non-inhibitor predicted accuracy rate, recall rate were 42.9%, 85.7%, 95.7%,
73.3%.This study demonstrates that machine learning methods effectively predict
inhibitors of serotonin transporters and accelerate drug screening.
Credit risk with asymmetric information and a switching default threshold
Authors: Imke Redeker, Ralf Wunderlich
Date: 2019-10-31 00:00:00
Tasks: Management

We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of a constant default threshold. The default threshold at which the firm is liquidated is modeled as a random variable whose value is chosen by the management of the firm and dynamically adjusted to account for changes in the economy or the appointment of a new firm management. Investors on the market have no access to the value of the threshold and only anticipate the distribution of the threshold. We distinguish different information levels on the firm's assets and derive explicit formulas for the conditional default probability given these information levels. Numerical results indicate that the information level has a considerable impact on the estimation of the default probability and the associated credit yield spread.
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling
Date: 2020-05-13 00:00:00
Tasks: Distributed Optimization

Many multi-agent control algorithms and dynamic agent-based models arising in
natural and social sciences are based on the principle of iterative averaging.
Each agent is associated to a value of interest, which may represent, for
instance, the opinion of an individual in a social group, the velocity vector
of a mobile robot in a flock, or the measurement of a sensor within a sensor
network. This value is updated, at each iteration, to a weighted average of
itself and of the values of the adjacent agents. It is well known that, under
natural assumptions on the network's graph connectivity, this local averaging
procedure eventually leads to global consensus, or synchronization of the
values at all nodes. Applications of iterative averaging include, but are not
limited to, algorithms for distributed optimization, for solution of linear and
nonlinear equations, for multi-robot coordination and for opinion formation in
social groups. Although these algorithms have similar structures, the
mathematical techniques used for their analysis are diverse, and conditions for
their convergence and differ from case to case. In this paper, we review many
of these algorithms and we show that their properties can be analyzed in a
unified way by using a novel tool based on recurrent averaging inequalities
(RAIs). We develop a theory of RAIs and apply it to the analysis of several
important multi-agent algorithms recently proposed in the literature.
Structured exploration in the finite horizon linear quadratic dual control problem
Authors: Andrea Iannelli, Mohammad Khosravi, Roy S. Smith
Date: 2019-10-31 00:00:00
Tasks: Efficient Exploration

This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty. To this end, a synthesis problem is defined where the feedback law has to simultaneously gain knowledge of the system and robustly optimize the cost. By framing the problem in a finite horizon setting, the trade-offs arising when the tasks include both identification and control are formally captured in the optimization problem. Results show that efficient exploration strategies are achieved when the structure of the problem is exploited.
Option-based Equity Risk Premiums
Authors: Alan L. Lewis
Date: 2019-10-31 00:00:00

We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting $N$-component Gaussian mixture models to option quotes, where $N$ is a small integer (here 4 or 5). These densities are transformed to their real-world equivalents by exponential tilting with a single parameter: the Coefficient of Relative Risk Aversion $\kappa$. From history, I estimate $\kappa = 3 \pm 0.5$. From the inferred real-world densities, the equity risk premium is readily calculated. Three term structures serve as examples.
A Lyapunov-like Characterization of Predefined-Time Stability
Date: 2019-10-31 00:00:00

This technical note studies Lyapunov-like conditions to ensure a class of
dynamical systems to exhibit predefined-time stability. The origin of a
dynamical system is predefined-time stable if it is fixed-time stable and an
upper bound of the settling-time function can be arbitrarily chosen a priori
through a suitable selection of the system parameters. We show that the studied
Lyapunov-like conditions allow to demonstrate equivalence between previous
Lyapunov theorems for predefined-time stability for autonomous systems.
Moreover, the obtained Lyapunov-like theorem is extended for analyzing the
property of predefined-time ultimate boundedness with predefined bound, which
is useful when analyzing uncertain dynamical systems. Therefore, the proposed
results constitute a general framework for analyzing predefined-time stability,
and they also unify a broad class of systems which present the predefined-time
stability property. On the other hand, the proposed framework is used to design
robust controllers for affine control systems, which induce predefined-time
stability (predefined-time ultimate boundedness of the solutions) w.r.t. to
some desired manifold. A simulation example is presented to show the behavior
of a developed controller, especially regarding the settling time estimation.
Generative network complex (GNC) for drug discovery
Date: 2019-10-31 00:00:00
Tasks: Decoder, Drug Discovery

It remains a challenging task to generate a vast variety of novel compounds
with desirable pharmacological properties. In this work, a generative network
complex (GNC) is proposed as a new platform for designing novel compounds,
predicting their physical and chemical properties, and selecting potential drug
candidates that fulfill various druggable criteria such as binding affinity,
solubility, partition coefficient, etc. We combine a SMILES string generator,
which consists of an encoder, a drug-property controlled or regulated latent
space, and a decoder, with verification deep neural networks, a target-specific
three-dimensional (3D) pose generator, and mathematical deep learning networks
to generate new compounds, predict their drug properties, construct 3D poses
associated with target proteins, and reevaluate druggability, respectively. New
compounds were generated in the latent space by either randomized output,
controlled output, or optimized output. In our demonstration, 2.08 million and
2.8 million novel compounds are generated respectively for Cathepsin S and BACE
targets. These new compounds are very different from the seeds and cover a
larger chemical space. For potentially active compounds, their 3D poses are
generated using a state-of-the-art method. The resulting 3D complexes are
further evaluated for druggability by a championing deep learning algorithm
based on algebraic topology, differential geometry, and algebraic graph
theories. Performed on supercomputers, the whole process took less than one
week. Therefore, our GNC is an efficient new paradigm for discovering new drug
candidates.
Exploring cities of Central and Eastern Europe within transnational company networks: the core-periphery effect
Date: 2019-10-31 00:00:00

After the Fall of the Berlin Wall, Central Eastern European cities (CEEc)
integrated the globalized world, characterized by a core-periphery structure
and hierarchical interactions between cities. This article gives evidence of
the core-periphery effect on CEEc in 2013 in terms of differentiation of their
urban functions after 1989. We investigate the position of all CEEc in
transnational company networks in 2013. We examine the orientations of
ownership links between firms, the spatial patterns of these networks and the
specialization of firms in CEEc involved. The major contribution of this paper
consists in giving proof of a core-periphery structure within Central Eastern
Europe itself, but also of the diffusion of innovations theory as not only
large cities, but also medium-sized and small ones are part of the
multinational networks of firms. These findings provide significant insights
for the targeting of specific regional policies of the European Union.