pwc-archive/papers-with-abstracts huggingface dataset:
Electricity Price Forecasting: The Dawn of Machine Learning
Authors: Arkadiusz Jędrzejewski, Jesus Lago, Grzegorz Marcjasz, Rafał Weron
Date: 2022-04-02 00:00:00
Tasks: BIG-bench Machine Learning, Electrical Engineering

Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading), through days (day-ahead auctions), to weeks, months or even years (exchange and over-the-counter traded futures and forward contracts). Over the last 25 years, various methods and computational tools have been applied to intraday and day-ahead EPF. Until the early 2010s, the field was dominated by relatively small linear regression models and (artificial) neural networks, typically with no more than two dozen inputs. As time passed, more data and more computational power became available. The models grew larger to the extent where expert knowledge was no longer enough to manage the complex structures. This, in turn, led to the introduction of machine learning (ML) techniques in this rapidly developing and fascinating area. Here, we provide an overview of the main trends and EPF models as of 2022.
Capacity Cost of Fulfilling the URLLC Performance in Industrial 5G New Radio Deployments
Authors: Ali A. Esswie
Date: 2022-04-02 00:00:00

The development of the 5G new radio specifications has been derived by the the deterministic low latency use cases such as the ultra-reliable and low-latency communications (URLLC). A URLLC application requires a stringent radio latency and reliability performance, e.g., one-way radio latency of 1 ms with 99.999% success probability. Furthermore, there is a concurrent progressive demand for broadband capacity cellular applications, e.g., enhanced mobile broadband (eMBB) use-cases. The coexistence among the URLLC and eMBB service classes over a single radio spectrum is a challenging task since achieving the tight URLLC radio targets typically results in a capacity loss. Hence, it is vital for telecom operators to understand the capacity cost of fulling the various URLLC requirements in order to sufficiently plan the corresponding pricing models. Hence, in this work, a comprehensive analysis of the system capacity loss is presented to achieve the various requirements of the different URLLC use-cases. An extensive set of realistic system level simulations is performed and introduced where valuable insights and system design recommendations on the URLLC-eMBB quality of service coexistence are presented.
Power Saving Techniques in 3GPP 5G New Radio: A Comprehensive Latency and Reliability Analysis
Authors: Ali A. Esswie
Date: 2022-04-02 00:00:00

Energy efficiency is critical for future sustainable cellular systems. Power saving optimization has been a key part of the fifth generation (5G) new radio specifications. For 5Gadvanced and future 6G, with the anticipation of a trillion internet of things (IoTs) devices with non-rechargeable or low-density batteries, device power efficiency is rather essential. There are numerous contributions from industry and academia which present the potential power saving gains of the various 5G power saving techniques; however, there is a lack of art on the performance cost paid to achieve such power saving gains. Therefore, this paper presents a comprehensive evaluation of the radio latency and reliability cost, which is lost due to a certain 5G new radio power saving feature. A thorough review of the state of-the-art 5G power saving techniques is introduced. Extensive system level simulations are performed to evaluate the latency and reliability cost of the considered power saving features. The paper offers valuable recommendations for supporting power efficient latency-critical traffic for beyond 5G-advanced systems.
Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
Authors: Kai Li, Ying Li, Lei Cheng, Qingjiang Shi, Zhi-Quan Luo
Date: 2022-04-02 00:00:00
Tasks: Scheduling

The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling. However, this task is challenging in the popular frequency division duplex massive MIMO systems with Type I codebook due to the limited channel information feedback. In this paper, we propose a novel formulation that leverages the structure of the codebook and feedback values for an accurate estimation of the downlink CCM. Then, we design a cutting plane algorithm to consecutively shrink the feasible set containing the downlink CCM, enabled by the careful design of pilot weighting matrices. Theoretical analysis shows that as the number of communication rounds increases, the proposed cutting plane algorithm can recover the ground-truth CCM. Numerical results are presented to demonstrate the superior performance of the proposed algorithm over the existing benchmark in CCM reconstruction.
Reliable Geofence Activation with Sparse and Sporadic Location Measurements: Extended Version
Authors: Kien Nguyen, John Krumm
Date: 2022-04-01 00:00:00
Tasks: Prediction

Geofences are a fundamental tool of location-based services. A geofence is usually activated by detecting a location measurement inside the geofence region. However, location measurements such as GPS often appear sporadically on smartphones, partly due to weak signal, or privacy preservation, because users may restrict location sensing, or energy conservation, because sensing locations can consume a significant amount of energy. These unpredictable, and sometimes long, gaps between measurements mean that entry into a geofence can go completely undetected. In this paper we argue that short term location prediction can help alleviate this problem by computing the probability of entering a geofence in the future. Complicating this prediction approach is the fact that another location measurement could appear at any time, making the prediction redundant and wasteful. Therefore, we develop a framework that accounts for uncertain location predictions and the possibility of new measurements to trigger geofence activations. Our framework optimizes over the benefits and costs of correct and incorrect geofence activations, leading to an algorithm that reacts intelligently to the uncertainties of future movements and measurements.
Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information
Authors: Jonathan Ansari, Eva Lütkebohmert, Ariel Neufeld, Julian Sester
Date: 2022-04-03 00:00:00

We show how inter-asset dependence information derived from market prices of options can lead to improved model-free price bounds for multi-asset derivatives. Depending on the type of the traded option, we either extract correlation information or we derive restrictions on the set of admissible copulas that capture the inter-asset dependencies. To compute the resultant price bounds for some multi-asset options of interest, we apply a modified martingale optimal transport approach. Several examples based on simulated and real market data illustrate the improvement of the obtained price bounds and thus provide evidence for the relevance and tractability of our approach.
Microtubule organization and cell geometry
Authors: Panayiotis Foteinopoulos, Bela M. Mulder
Date: 2022-04-04 00:00:00

We present a systematic study of the influence of cell geometry on the orientational distribution of microtubules (MTs) nucleated from a single microtubule organizing center (MTOC). For simplicity we consider an elliptical cell geometry, a setting appropriate to a generic non-spherical animal cell. Within this context we introduce four models of increasing complexity, in each case introducing additional mechanisms that govern the interaction of the MTs with the cell boundary. In order, we consider the cases: MTs that can bind to the boundary with a fixed mean residence time (M0), force-producing MTs that can slide on the boundary towards the cell poles (MS), MTs that interact with a generic polarity factor that is transported and deposited at the boundary, and which in turn stabilizes the MTs at the boundary (MP), and a final model in which both sliding and stabilization by polarity factors is taken into account (MSP). In the baseline model (M0), the exponential length distribution of MTs causes most of the interactions at the cell boundary to occur along the shorter transverse direction in the cell, leading to transverse biaxial order. MT sliding (MS) is able to reorient the main axis of this biaxial order along the longitudinal axis. The polarization mechanism introduced in MP and MSP overrules the geometric bias towards bipolar order observed in M0 and MS, and allows the establishment of unipolar order either along the short- (MP) or the long cell axis (MSP). The behavior of the latter two models can be qualitatively reproduced by a very simple toy model with discrete MT orientations.
Emergence of Autocatalytic Sets in a Simple Model of Technological Evolution
Authors: Wim Hordijk, Stuart Kauffman
Date: 2022-04-03 00:00:00

Two alternative views of an economy are combined and studied. The first view is that of technological evolution as a process of combinatorial innovation. Recently a simple mathematical model (TAP) was introduced to study such a combinatorial process. The second view is that of a network of production functions forming an autocatalytic set. Autocatalytic (RAF) sets have been studied extensively in the context of chemical reaction networks. Here, we combine the two models (TAP and RAF) and show that they are compatible. In particular, it is shown that production function networks resulting from the combinatorial TAP model have a high probability of containing autocatalytic (RAF) sets. We also study the size distribution and robustness of such "economic autocatalytic sets", and compare our results with those from the chemical context. These initial results strongly support earlier claims that the economy can indeed be seen as an autocatalytic set.
Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity
Authors: Bastian Pietras, Valentin Schmutz, Tilo Schwalger
Date: 2022-04-04 00:00:00

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity pattern is hippocampal replay, which is critical for memory consolidation. The switchings between replay events and a low-activity state in neural recordings suggests metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to spike noise and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesosocpic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie ("chemical Langevin equation"), which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down states dynamics) by means of phase-plane analysis. This stochastic neural mass model is the basic component of our mesoscopic model for replay. We show that our model faithfully captures the stochastic nature of individual replayed trajectories. Moreover, compared to the deterministic Romani-Tsodyks model of place cell dynamics, it exhibits a higher level of variability in terms of content, direction and timing of replay events, compatible with biological evidence and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.
Curiosity as filling, compressing, and reconfiguring knowledge networks
Authors: Shubhankar P. Patankar, Dale Zhou, Christopher W. Lynn, Jason Z. Kim, Mathieu Ouellet, Harang Ju, Perry Zurn, David M. Lydon-Staley, Dani S. Bassett
Date: 2022-04-03 00:00:00

Due to the significant role that curiosity plays in our lives, several theoretical constructs, such as the information gap theory and compression progress theory, have sought to explain how we engage in its practice. According to the former, curiosity is the drive to acquire information that is missing from our understanding of the world. According to the latter, curiosity is the drive to construct an increasingly parsimonious mental model of the world. To complement the densification processes inherent to these theories, we propose the conformational change theory, wherein we posit that curiosity results in mental models with marked conceptual flexibility. We formalize curiosity as the process of building a growing knowledge network to quantitatively investigate information gap theory, compression progress theory, and the conformational change theory of curiosity. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We leverage data acquired from the online encyclopedia Wikipedia to determine the degree to which each theory explains the growth of knowledge networks built by individuals and by collectives. Our findings lend support to a pluralistic view of curiosity, wherein intrinsically motivated information acquisition fills knowledge gaps and simultaneously leads to increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each theoretical account may be explanatory, thereby clarifying their complementary and distinct explanations of curiosity. Our findings offer a novel network theoretical perspective on intrinsically motivated information acquisition that may harmonize with or compel an expansion of the traditional taxonomy of curiosity.
Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation
Authors: Amin Dehghani, Hamid Soltanian-Zadeh, Gholam-Ali Hossein-Zadeh
Date: 2022-04-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.
A decade of molecular cell atlases
Authors: Lior Pachter
Date: 2022-04-03 00:00:00

The recent opinion article "A decade of molecular cell atlases" by Stephen Quake narrates the incredible single-cell genomics technology advances that have taken place over the last decade, and how they have translated to increasingly resolved cell atlases. However the sequence of events described is inaccurate and contains several omissions and errors. The errors are corrected in this note.
Modeling the impact of birth control policies on China's population and age: effects of delayed births and minimum birth age constraints
Authors: Yue Wang, Renaud Dessalles, Tom Chou
Date: 2020-09-01 00:00:00
Tasks: Management

We consider age-structured models with an imposed refractory period between births. These models can be used to formulate alternative population control strategies to China's one-child policy. By allowing any number of births, but with an imposed delay between births, we show how the total population can be decreased and how a relatively older age distribution can be generated. This delay represents a more "continuous" form of population management for which the strict one-child policy is a limiting case. Such a policy approach could be more easily accepted by society. Our analyses provide an initial framework for studying demographics and how social constraints influence population structure.
Plant root growth against a mechanical obstacle: The early growth response of a maize root facing an axial resistance agrees with the Lockhart model
Authors: Manon Quiros, Marie-Béatrice Bogeat-Triboulot, Etienne Couturier, Evelyne Kolb
Date: 2022-04-04 00:00:00

Plant root growth is dramatically reduced in compacted soils, affecting the growth of the whole plant. Through a model experiment coupling force and kinematics measurements, we probed the force-growth relationship of a primary root contacting a stiff resisting obstacle, that mimics the strongest soil impedance variation encountered by a growing root. The growth of maize roots just emerging from a corseting agarose gel and contacting a force sensor (acting as an obstacle) was monitored by time-lapse imaging simultaneously to the force. The evolution of the velocity field along the root was obtained from kinematics analysis of the root texture with a PIV derived-technique. A triangular fit was introduced to retrieve the elemental elongation rate or strain rate. A parameter-free model based on the Lockhart law quantitatively predicts how the force at the obstacle modifies several features of the growth distribution (length of the growth zone, maximal elemental elongation rate, velocity) during the first 10 minutes. These results suggest a strong similarity of the early growth responses elicited either by a directional stress (contact) or by an isotropic perturbation (hyperosmotic bath).
On a Stochastic Model of Diversification
Authors: Maria Logvaneva, Mikhail Tselishchev
Date: 2022-04-04 00:00:00
Tasks: model

We propose a definition of diversification as a binary relationship between financial portfolios. According to it, a convex linear combination of several risk positions with some weights is considered to be less risky than the probabilistic mixture of the same risk positions with the same weights. It turns out to be that the proposed partial ordering coincides with the well-known second order stochastic dominance, but allows to take a look at it from another perspective.
Estimating Functions of Probability Distributions from a Finite Set of Samples, Part 1: Bayes Estimators and the Shannon Entropy
Authors: David H. Wolpert, David R. Wolf
Date: 1994-03-08 00:00:00

We present estimators for entropy and other functions of a discrete probability distribution when the data is a finite sample drawn from that probability distribution. In particular, for the case when the probability distribution is a joint distribution, we present finite sample estimators for the mutual information, covariance, and chi-squared functions of that probability distribution.
Operation management of green ports and shipping networks: overview and research opportunities
Authors: Lu ZHEN, Dan ZHUGE, Liwen MURONG, Ran Yan, Shuaian W ANG
Date: 2019-03-12 00:00:00
Proceeding: Frontiers of Engineering Management 2019 3
Tasks: Management

Global ports and maritime shipping networks
are important carriers for global supply chain networks, but
they are also the main sources of energy consumption and
pollution. To limit ship emissions in ports and offshore
areas, the International Maritime Organization, as well as
some countries, has issued a series of policies. This study
highlights the importance and necessity of investigating
emergent research problems in the operation management
of green ports and maritime shipping networks. Consider-
able literature related to this topic is reviewed and
discussed. Moreover, a comprehensive research frame-
work on green port and shipping operation management is
proposed for future research opportunities. The framework
mainly comprises four research areas related to emission
control and grading policies. This review may provide new
ideas to the academia and industry practitioners for
improving the performance and efficiency of the operation
management of green ports and maritime shipping net-
works.
Automating Reinforcement Learning with Example-based Resets
Authors: Jigang Kim, J. Hyeon Park, Daesol Cho, H. Jin Kim
Date: 2022-04-05 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning algorithms assume an episodic setting, in which the agent resets to a fixed initial state distribution at the end of each episode, to successfully train the agents from repeated trials. Such reset mechanism, while trivial for simulated tasks, can be challenging to provide for real-world robotics tasks. Resets in robotic systems often require extensive human supervision and task-specific workarounds, which contradicts the goal of autonomous robot learning. In this paper, we propose an extension to conventional reinforcement learning towards greater autonomy by introducing an additional agent that learns to reset in a self-supervised manner. The reset agent preemptively triggers a reset to prevent manual resets and implicitly imposes a curriculum for the forward agent. We apply our method to learn from scratch on a suite of simulated and real-world continuous control tasks and demonstrate that the reset agent successfully learns to reduce manual resets whilst also allowing the forward policy to improve gradually over time.