pwc-archive/papers-with-abstracts huggingface dataset:
On Broken Symmetry in Cognition
Authors: Xin Li
Date: 2023-03-07 00:00:00
Tasks: Specificity

Cognition is not passive data accumulation but the active resolution of uncertainty through symmetry breaking. This paper argues that both cognitive evolution and development unfold via sequential symmetry-breaking transitions that disrupt innate regularities across space, time, self, and representation. First, spatial symmetry is broken through bilateral body plans and neural codes like grid and place cells, which privilege egocentric orientation and localized encoding. Second, reinforcement learning introduces temporal asymmetry by favoring future rewards, establishing a directional flow of inference. Third, goal-directed simulation breaks spatiotemporal symmetry between internal self-models and the external world, enabling embodied inference and solving the combinatorial search problem. Fourth, social cognition via mentalizing and imitation breaks the symmetry between minds, allowing agents to infer others' beliefs. Finally, language imposes a linear, recursive structure onto unordered thought, breaking expressive symmetry through syntax and grammar. These asymmetries are unified by the Context-Content Uncertainty Principle (CCUP), which frames cognition as a cyclical entropy-minimizing process. At the core lies the principle of structure-before-specificity: ambiguous input is first mapped onto stable latent structures before being bound to specific instances. This promotes generalization, reduces sample complexity, and prevents overfitting. Inverting inference, from content back to context, further breaks the curse of dimensionality by constraining inference to goal-consistent manifolds. Thus, symmetry breaking is not incidental but the foundational mechanism by which cognition organizes, stabilizes, and scales intelligent behavior in an uncertain and dynamic world.
Feature Importance: A Closer Look at Shapley Values and LOCO
Authors: Isabella Verdinelli, Larry Wasserman
Date: 2023-03-10 00:00:00
Tasks: Feature Correlation, Feature Importance

There is much interest lately in explainability in statistics and machine learning. One aspect of explainability is to quantify the importance of various features (or covariates). Two popular methods for defining variable importance are LOCO (Leave Out COvariates) and Shapley Values. We take a look at the properties of these methods and their advantages and disadvantages. We are particularly interested in the effect of correlation between features which can obscure interpretability. Contrary to some claims, Shapley values do not eliminate feature correlation. We critique the game theoretic axioms for Shapley values and suggest some new axioms. We propose new, more statistically oriented axioms for feature importance and some measures that satisfy these axioms. However, correcting for correlation is a Faustian bargain: removing the effect of correlation creates other forms of bias. Ultimately, we recommend a slightly modified version of LOCO. We briefly consider how to modify Shapley values to better address feature correlation.
A pseudo-likelihood approach to community detection in weighted networks
Authors: Andressa Cerqueira, Elizaveta Levina
Date: 2023-03-10 00:00:00
Tasks: Community Detection, Computational Efficiency, Stochastic Block Model

Community structure is common in many real networks, with nodes clustered in groups sharing the same connections patterns. While many community detection methods have been developed for networks with binary edges, few of them are applicable to networks with weighted edges, which are common in practice. We propose a pseudo-likelihood community estimation algorithm derived under the weighted stochastic block model for networks with normally distributed edge weights, extending the pseudo-likelihood algorithm for binary networks, which offers some of the best combinations of accuracy and computational efficiency. We prove that the estimates obtained by the proposed method are consistent under the assumption of homogeneous networks, a weighted analogue of the planted partition model, and show that they work well in practice for both homogeneous and heterogeneous networks. We illustrate the method on simulated networks and on a fMRI dataset, where edge weights represent connectivity between brain regions and are expected to be close to normal in distribution by construction.
Rosenthal-type inequalities for linear statistics of Markov chains
Authors: Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Marina Sheshukova
Date: 2023-03-10 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we establish novel deviation bounds for additive functionals of geometrically ergodic Markov chains similar to Rosenthal and Bernstein inequalities for sums of independent random variables. We pay special attention to the dependence of our bounds on the mixing time of the corresponding chain. More precisely, we establish explicit bounds that are linked to the constants from the martingale version of the Rosenthal inequality, as well as the constants that characterize the mixing properties of the underlying Markov kernel. Finally, our proof technique is, up to our knowledge, new and based on a recurrent application of the Poisson decomposition.
Design of solar collector networks for industrial applications
Authors: Martín Picón-Núñez, Guillermo Martínez-Rodríguez, Amanda L. Fuentes-Silva
Date: 2014-05-10 00:00:00
Proceeding: Applied Thermal Engineering 2014 5

The design and selection of banks of solar energy collectors for thermal applications requires that a thermal and a hydraulic objective be met. On the thermal side, the working fluid must provide the heat load to the process at the specified temperature and on the hydraulic side the fluid must flow through the system experiencing a pressure drop that is within the specified limits. In the case of solar collectors, the working fluid used to transfer heat to the process either in an open circuit or in a closed circuit is water. A solar collector can be viewed as a particular type of heat exchanger and the set of solar collectors needed for a particular application, as a network of heat exchangers. In this work, the overall arrangement of solar collectors which form the total collector surface area is referred to as the network of solar collectors (NSC). An NSC is used in large scale heating applications in buildings or in production processes. Such a network of collectors can exhibit arrangements that go from series, parallel or any combination of these. Contrary to domestic applications where water flows through the exchanger in natural convection, in large scale applications the flow of water is forced through the use of a pumping system. This paper introduces the tools for the design and selection of the most appropriate network arrangement for a given application as a function of the specified pressure drop for fluid flow, the required temperature and heat duty. A thermo-hydraulic model for solar collector networks is presented and its solution is graphically displayed with the length of the exchanger plotted against the number of arrays in parallel. The thermal and the hydraulic models are solved separately so that two solution spaces are represented in the same plot. The thermal space represents the thermal length required to meet the specified heat load as a function of the number of parallel arrays. The hydraulic space, on the other hand, represents the hydraulic length that meets the specified pressure drop as a function of the number of parallel arrays. The point where the two spaces meet determines the network structure that fulfils the required heat duty with an acceptable pressure drop.
Edge computing on TPU for brain implant signal analysis
Authors: János Rokai, István Ulbert, Gergely Márton
Date: 2023-02-28 00:00:00
Proceeding: Neural Networks 2023 2
Tasks: Contrastive Learning, Deep Learning, Edge-computing, Spike Sorting, Time Series Analysis

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited.

We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models.

We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain–computer interfaces.
eBPF-based Working Set Size Estimation in Memory Management
Authors: Zhilu Lian, Yangzi Li, Zhixiang Chen, Shiwen Shan, Baoxin Han, Yuxin Su
Date: 2023-01-17 00:00:00
Tasks: Management

Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems. Previous work proposed several methods to estimate WSS, including self-balloning, Zballoning and so on. However, these methods which are based on virtual machine usually cause a large overhead. Thus, using those methods to estimate WSS is impractical. In this paper, we propose a novel framework to efficiently estimate WSS with eBPF (extended Berkeley Packet Filter), a cutting-edge technology which monitors and filters data by being attached to the kernel. With an eBPF program pinned into the kernel, we get the times of page fault and other information of memory allocation. Moreover, we collect WSS via vanilla tool to train a predictive model to complete estimation work with LightGBM, a useful tool which performs well on generating decision trees over continuous value. The experimental results illustrate that our framework can estimate WSS precisely with 98.5\% reduction in overhead compared to traditional methods.
Characterizing bearing equivalence in directed graphs
Authors: Zhiyong Sun, Shiyu Zhao, Daniel Zelazo
Date: 2023-03-09 00:00:00

In this paper, we study bearing equivalence in directed graphs. We first give a strengthened definition of bearing equivalence based on the \textit{kernel equivalence} relationship between bearing rigidity matrix and bearing Laplacian matrix. We then present several conditions to characterize bearing equivalence for both directed acyclic and cyclic graphs. These conditions involve the spectrum and null space of the associated bearing Laplacian matrix for a directed bearing formation. For directed acyclic graphs, all eigenvalues of the associated bearing Laplacian are real and nonnegative, while for directed graphs containing cycles, the bearing Laplacian can have eigenvalues with negative real parts. Several examples of bearing equivalent and bearing non-equivalent formations are given to illustrate these conditions.
Scatter-based common spatial patterns -- a unified spatial filtering framework
Authors: Jinlong Dong, Milana Komosar, Johannes Vorwerk, Daniel Baumgarten, Jens Haueisen
Date: 2023-03-07 00:00:00
Tasks: Computational Efficiency, EEG, Electroencephalogram (EEG), Motor Imagery

The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification.Therefore, we propose a novel spatial filtering framework called scaCSP based on the scatter matrices of spatial covariances of EEG signals, which works generally in both binary and multi-class problems whereas CSP can be cast into our framework as a special case when only the range space of the between-class scatter matrix is used in binary cases.We further propose subspace enhanced scaCSP algorithms which easily permit incorporating more discriminative information contained in other range spaces and null spaces of the between-class and within-class scatter matrices in two scenarios: a nullspace components reduction scenario and an additional spatial filter learning scenario.The proposed algorithms are evaluated on two data sets including 4 MI tasks. The classification performance is compared against state-of-the-art competing algorithms: CSP, Tikhonov regularized CSP (TRCSP), stationary CSP (sCSP) and stationary TRCSP (sTRCSP) in the binary problems whilst multi-class extensions of CSP based on pair-wise and one-versus-rest techniques in the multi-class problems. The results show that the proposed framework outperforms all the competing algorithms in terms of average classification accuracy and computational efficiency in both binary and multi-class problems.The proposed scsCSP works as a unified framework for general multi-class problems and is promising for improving the performance of MI-BCIs.
Kernel-based identification using Lebesgue-sampled data
Authors: Rodrigo A. González, Koen Tiels, Tom Oomen
Date: 2023-03-10 00:00:00

Sampling in control applications is increasingly done non-equidistantly in time. This includes applications in motion control, networked control, resource-aware control, and event-based control. Some of these applications, like the ones where displacement is tracked using incremental encoders, are driven by signals that are only measured when their values cross fixed thresholds in the amplitude domain. This paper introduces a non-parametric estimator of the impulse response and transfer function of continuous-time systems based on such amplitude-equidistant sampling strategy, known as Lebesgue sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the bounded output uncertainty between the event timestamps, which ultimately leads to more accurate models and more efficient output sampling compared to the equidistantly-sampled kernel-based approach. The efficacy of our proposed method is demonstrated through a mass-spring damper example with encoder measurements and extensive Monte Carlo simulation studies on system benchmarks.
Data-Efficient Control Barrier Function Refinement
Authors: Bolun Dai, Heming Huang, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2023-03-10 00:00:00
Tasks: valid

Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a unicycle and a two-link arm.
Optimal Sizing of Isolated Renewable Power Systems with Ammonia Synthesis: Model and Solution Approach
Authors: Zhipeng Yu, Jin Lin, Feng Liu, Jiarong Li, Yuxuan Zhao, Yonghua Song
Date: 2023-03-10 00:00:00

Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80\% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multi-year uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multi-year uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3610 RMB/t) in long-run simulations.
Nonlinear constructive observer design for direct homography estimation
Authors: Tarek Bouazza, Pieter van Goor, Robert Mahony, Tarek Hamel
Date: 2023-03-10 00:00:00
Tasks: Homography Estimation

Feature-based homography estimation approaches rely on extensive image processing for feature extraction and matching, and do not adequately account for the information provided by the image. Therefore, developing efficient direct techniques to extract the homography from images is essential. This paper presents a novel nonlinear direct homography observer that exploits the Lie group structure of $\mathbf{SL}(3)$ and its action on the space of image maps. Theoretical analysis demonstrates local asymptotic convergence of the observer. The observer design is also extended for partial measurements of velocity under the assumption that the unknown component is constant or slowly time-varying. Finally, simulation results demonstrate the performance of the proposed solutions on real images.
Sufficient Control of Complex Networks
Authors: Xiang Li, Guoqi Li, Leitao Gao, Beibei Li, Gaoxi Xiao
Date: 2023-03-10 00:00:00

In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters. To the best of our knowledge, this is the first time that such a problem is investigated. We prove that the sufficient controllability problem can be converted into a minimum cost flow problem, for which an algorithm can be easily devised with polynomial complexity. Further, we study the problem of minimum-cost sufficient control, which is to drive a sufficiently large subset of the network nodes to any predefined state with the minimum cost using a given number of controllers. It is proved that the problem is NP-hard. We propose an ``extended $L_{\mathrm{0}}$-norm-constraint-based Projected Gradient Method" (eLPGM) algorithm which may achieve suboptimal solutions for the problems at small or medium sizes. To tackle the large-scale problems, we propose to convert the control problem into a graph algorithm problem, and devise an efficient low-complexity ``Evenly Divided Control Paths" (EDCP) algorithm to tackle the graph problem. Simulation results on both synthetic and real-life networks are provided, demonstrating the satisfactory performance of the proposed methods.
Distributed Design of Controllable and Robust Networks using Zero Forcing and Graph Grammars
Authors: Priyanshkumar I. Patel, Johir Suresh, Waseem Abbas
Date: 2023-03-09 00:00:00

This paper studies the problem of designing networks that are strong structurally controllable, and robust simultaneously. For given network specifications, including the number of nodes $N$, the number of leaders $N_L$, and diameter $D$, where $2 \le D \le N/N_L$, we propose graph constructions generating strong structurally controllable networks. We also compute the number of edges in graphs, which are maximal for improved robustness measured by the algebraic connectivity and Kirchhoff index. For the controllability analysis, we utilize the notion of zero forcing sets in graphs. Additionally, we present graph grammars, which are sets of rules that agents apply in a distributed manner to construct the graphs mentioned above. We also numerically evaluate our methods. This work exploits the trade-off between network controllability and robustness and generates networks satisfying multiple design criteria.
Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems
Authors: Yiming Meng, Jun Liu
Date: 2023-03-08 00:00:00

The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems. The complexity of the abstraction is usually the dominating factor that determines the efficiency of the algorithm. For the control synthesis of discrete-time nonlinear stochastic systems modelled by nonlinear stochastic difference equations, recent literature has demonstrated the soundness of abstractions in preserving robust probabilistic satisfaction of {\omega}-regular lineartime properties. However, unnecessary transitions exist within the abstractions, which are difficult to quantify, and the completeness of abstraction-based control synthesis in the stochastic setting remains an open theoretical question. In this paper, we address this fundamental question from the topological view of metrizable space of probability measures, and propose constructive finite-state abstractions for control synthesis of probabilistic linear temporal specifications. Such abstractions are both sound and approximately complete. That is, given a concrete discrete-time stochastic system and an arbitrarily small L1-perturbation of this system, there exists a family of finite-state controlled Markov chains that both abstracts the concrete system and is abstracted by the slightly perturbed system. In other words, given an arbitrarily small prescribed precision, an abstraction always exists to decide whether a control strategy exists for the concrete system to satisfy the probabilistic specification.