Abstraction Based Output Feedback Control with State Based Specifications

We consider abstraction-based design of output-feedback controllers for non-linear dynamical systems against specifications over state-based predicates in linear-time temporal logic (LTL) Our algorithm isinspired by reactive synthesis under partial observation and utilizes boundedsynthesis . We introduce a new algorithm for the synthesis of abstractoutput-feedingback controllers w.r.t.…

Operator Augmentation for General Noisy Matrix Systems

In the computational sciences, one must often estimate model parameters from data subject to noise and uncertainty, leading to inaccurate results . In orderto improve the accuracy of models with noisy parameters, we consider theproblem of reducing error in a linear system with the operator corrupted by noise .…

Market Potential for CO _2 Removal and Sequestration from Renewable Natural Gas Production in California

Bioenergy with Carbon Capture and Sequestration (BECCS) is critical for climate change mitigation, but is commercially and technologicallyimmature and resource-intensive . In California, state and federal fuel and climate policies can drive first-markets for BECCS . Existing biomass residues produce biogas and RNG and enablelow-cost CCS through the upgrading process and CO$_2$ truck transport .…

Structure Preserving Discretization of 1D Nonlinear Port Hamiltonian Distributed Parameter Systems

A specificfinite dimensional port-Hamiltonian element is defined that enables a structurepreserving discretization of the infinite dimensional model that inherits theDirac structure, the underlying energy balance and matches the Hamiltonianfunction on any, possibly nonuniform mesh of the spatial geometry . This paper contributes with a new formal method of spatial discretizing of a class of nonlinear distributed parameter systems .…

Assessing Validity of Static Analysis Warnings using Ensemble Learning

Static Analysis (SA) tools are used to identify potential weaknesses in codeand fix them in advance . In legacy codebases, these rules-based static analysis tools generally report a lot of false warnings along with the actual ones . To address this problem, we propose a MachineLearning (ML) based learning process that uses source codes, historic commit data, and classifier-ensembles to prioritize the True warnings from the given list of warnings .…

Personalizing Performance Regression Models to Black Box Optimization Problems

Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques . Instead of aiming for a single model that works wellacross a whole set of possibly diverse problems, our personalized regressionapproach acknowledges that different models may suite different types of problems .…

The latent structure of national scientific development

Science is considered essential to innovation and economic prosperity . Understanding how nations build scientific capacity is crucial topromote economic growth and national development . Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientificproduction is increasingly specialized in global science over the past decades .…

Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis

Most classical (non-spiking) neural network models disregard internal neurondynamics and treat neurons as simple input integrators . This paper presents the Membrane Potential and Activation ThresholdHomeostasis (MPATH) neuron model . The MPATH neuron model combines several biologically inspiredmechanisms to efficiently simulate internal neuron dynamics with a singleparameter analogous to the membrane time constant in biological neurons .…

I m a Professor which isn t usually a dangerous job Internet Facilitated Harassment and its Impact on Researchers

The Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars . We perform aseries of systematic interviews with researchers who have experienced targeted, Internet-facilitatedharassment . We provide a framework for understanding the types of harassersthat target researchers, the harassment that ensues, and the impact on individuals and academic freedom .…

The Randomized Communication Complexity of Randomized Auctions

We study the communication complexity of incentive compatibleauction-protocols between a monopolist seller and a single buyer with acombinatorial valuation function over $n$ items . We design simple, incentivecompatible, and revenue-optimal auctions whose expected communicationcomplexity is much (in fact infinitely) more efficient than their deterministiccounterparts .…

MAQ CaF A Modular Air Quality Calibration and Forecasting method for cross sensitive pollutants

MAQ-CaF, a modular air qualitycalibration, and forecasting methodology, side-steps the challenges ofunreliability through its modular machine learning-based design which leveragesthe potential of IoT framework . CO, SO2, NO2,NO2, O3, PM1.0, PM2.5 andPM10 were calibrated and monitored with reasonable accuracy . Such an attempt is a step toward addressing climate change’s global challenge through appropriatemonitoring and air quality tracking across a wider geographical region viaaffordable monitoring.…

Transient and Asymptotic Properties of Robust Adaptive Controllers in the Presence of Non Coercive Lyapunov Functions

Adaptive control architectures often make use of Lyapunov functions to design adaptive laws . We are specifically interested in adaptive control methods, suchas the well-known L1 adaptive architecture, which employ a parameter observer . In such architectures, the observation error plays a critical role in determining analytical bounds on the tracking error as well as the performance and the robustness of such adaptive systems .…

Undiscounted Control Policy Generation for Continuous Valued Optimal Control by Approximate Dynamic Programming

We present a numerical method for generating the state-feedback control policy associated with general undiscounted, constant-setpoint,infinite-horizon, nonlinear optimal control problems with continuous statevariables . The method is based on approximate dynamic programming, and isclosely related to approximate policy iteration . Existing methods typicallyterminate based on the convergence of the control policy and the resulting system state to converge .…

Efficient Relation aware Scoring Function Search for Knowledge Graph Embedding

The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensuring the excellent performance of KG embedding . The relation-aware search requires a much larger search space than the previous one . We propose to encode the space as a supernet and propose an efficient alternative minimization algorithm to search through the supernet in a one-shot manner .…

Building Bilingual and Code Switched Voice Conversion with Limited Training Data Using Embedding Consistency Loss

A parallel non-autoregressive network to achieve bilingual andcode-switched voice conversion for multiple speakers . We achieve cross-lingual VC betweenMandarin speech with multiple speakers and English speech with multiplespeakers by applying bilingual bottleneck features . We use an adversarial speaker classifier with a gradient reversallayer to reduce the source speaker’s information from the output of encoder .…

Effectively Sampling Higher Order Mutants Using Causal Effect

Higher Order Mutation (HOM) has been proposed to avoid equivalent mutants and improve the scalability of mutation testing . But generating useful HOMs remains an expensive search problem on its own . We propose a new approach to generateStrongly Subsuming Higher Order Mutants using a recently introducedCausal Program Dependence Analysis (CPDA) The SSHOM generation approach chooses pairs of program elements usingheuristics based on CPDA analysis, performs First Order Mutations to the chosenpairs, and generates an HOM by combining two FOMs .…

Competing Epidemics on Graphs Global Convergence and Coexistence

The dynamics of the spread of contagions such as viruses, infectious diseases have effectively been captured by the well known . We demonstrate how the existing works are unsuccessful in characterizing a large subset of the model parameter space, including all parameters for which the competitiveness of the bi-virus system is significant enough to attain coexistence of the epidemics .…

Topological Simplifications of Hypergraphs

We study hypergraph visualization via its topological simplification . In simplifying a hypergraph, we allowvertices to be combined if they belong to almost the same set of hyperedges . Our proposed approaches are general, mathematically justifiable, and they putvertex simplification and hyperedge simplification in a unifying framework.…

The Density Fingerprint of a Periodic Point Set

Fingerprinth is a fast algorithm based on Brillouin zones and related inclusion-exclusionformulae . We prove invariance under isometries,continuity, and completeness in the generic case . The proof of continuity integratesmethods from discrete geometry and lattice theory . We have implemented the algorithm and describe its application tocrystal structure prediction .…

Hybrid Encoder Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks

Hybrid encoder makes efficient and precise native ads recommendation through two consecutive steps: retrieval and ranking . In the retrieval step, user andad are encoded with a siamese component, which enables relevant candidates to be retrieved via ANN search . The hybrid encoder’s effectiveness is experimentally verified: with very littleadditional cost, it outperforms Siamese encoder significantly and achieves comparable recommendation quality as the cross encoder .…

Landmark Aware and Part based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images

Convolutional Neural Network (CNN) has had limited success in predicting expressions from faces having extreme poses,illumination, and occlusion conditions . In this work, we propose a Part-based Ensemble Transfer Learning network, which models how humans recognize facial expressions by correlating the spatialorientation pattern of the facial features with a specific expression .…

Opinion Dynamics under Social Pressure

We introduce a new opinion dynamics model where a group of agents holds twokinds of opinions: inherent and declared . Each agent’s inherent opinion is fixed and unobservable by the other agents . At each time step, agents broadcast their declared opinions on a social network, governed by the agents’ inherent opinions and social pressure .…

Bayesian inversion for unified ductile phase field fracture

Numerical aspects of ductile failuredictate a sub-optimal calibration of plasticity- and fracture-related parameters for a large number of material properties . The prediction of crack initiation and propagation in ductile failure processes are challenging tasks for the design and fabrication of metallic structures on a large scale .…

Orthogonal iterations on Structured Pencils

We present a class of fast subspace tracking algorithms based on orthogonaliterations for structured matrices/pencils . These new subspace trackers reach a complexity of only $O(nk^2)$ operations per time update, where $n$ and $k$ are the size of thematrix and of the small rank perturbation, respectively .…

A Short Survey of Pre trained Language Models for Conversational AI A NewAge in NLP

Building a dialogue system that can communicate naturally with humans is a challenging problem of agent-based computing . The rapid growthin this area is usually hindered by the long-standing problem of data scarcity . The recently introduced pre-trained language models have the potential to address the issueof data scarcity and bring considerable advantages by generating contextualizedword embeddings .…

Integrated Framework of Vehicle Dynamics Instabilities Energy Models and Sparse Flow Smoothing Controllers

This work presents an integrated framework of: vehicle dynamics models, energy models, and sparse Lagrangian controls via automated vehicles . This framework serves as a keybuilding block in developing control strategies for human-in-the-loop trafficflow smoothing on real highways . We outline the fundamental merits of integrating vehicle dynamics and energy modeling into asingle framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results .…

Evolutionary game model of risk propensity in group decision making

We introduce an evolutionary game on hypergraphs in which decisions between arisky alternative and a safe one are taken in social groups of different sizes . The model naturally reproduces choice shifts, namely the differences betweenthe preference of individual decision makers and the consensual choice of agroup, that have been observed in choice dilemmas .…