Miracle-dz/UniFAR-FTUs huggingface dataset:
One-way quantum finite automata together with classical states

One-way quantum finite automata (1QFA) proposed by Moore and Crutchfield and by Kondacs and Watrous accept only subsets of regular languages with bounded error. In this paper, we develop a new computing model of 1QFA, namely, one-way quantum finite automata together with classical states (1QFAC for short). In this model, a component of classical states is added, and the choice of unitary evolution of quantum states at each step is closely related to the current classical state. 1QFAC can accept all regular languages with no error, and in particular, 1QFAC can accept some languages with essentially less number of states than deterministic finite automata (DFA). The main technical results are as follows. (1) We prove that the set of languages accepted by 1QFAC with bounded error consists precisely of all regular languages. (2) We show that, for any prime number m ≥ 2, there exists a regular language L0(m) whose minimal DFA needs O(m) states and that can not be accepted by the 1QFA proposed by Moore and Crutchfield and by Kondacs and Watrous, but there exists 1QFAC accepting L0(m) with only constant classical states and O(log(m)) quantum basis states. Analogous results for multi-letter automata are also established. (3) By a bilinearization technique we prove that any two 1QFAC A1 and A2 are equivalent if and only if they are (k1n1) 2 + (k2n2) 2 − 1-equivalent, and there exists a polynomial-time O([(k1n1) 2 + (k2n2) 2]4) algorithm for determining their equivalence, where k1 and k2 are the numbers of classical states of A1 and A2, as well as n1 and n2 are the numbers of quantum basis states of A1 andA2, respectively. (4) We show that the minimization problem of 1QFAC is in EXPSPACE. As a corollary of this result, we obtain that the minimization problem of MO-1QFA is also in EXPSPACE. Finally, we draw some conclusions and point out open ∗Corresponding author. E-mail address: issqdw@mail.sysu. edu.cn (D. Qiu). †E-mail address: {paulo.mateus,amilcar.sernadas}@math.ist. utl.pt.
Factors causing the relationship gap between top management and IS personnel

Purpose – The gap between top management and IS personnel is preventing organisations develop a competitive advantage based on IS. The purpose of this paper is thus to improve the understanding of the relationship between top management and IS personnel and to identify the key factors that are important in this relationship. Design/methodology/approach – Two separate questionnaires were used for IS department managers and top management to identify key factors in the relationship. In total, 221 CIOs and 93 CEOs agreed to participate in the research. To identify factors in the business-IS relationship an exploratory factor analysis was used. Further, factor scores were calculated and the independent samples t-test was used to compare these factor scores to reveal any significant differences in perceptions between CIOs and CEOs. Findings – The empirical investigation reveals the existence of nine factors that are important in the business-IS relationship. Seven factors are perceived differently by top manag.
Echo State Network for Occupancy Prediction and Pattern Mining in Intelligent Environments

Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat.
Online Trajectory Planning in Dynamic Environments for Surgical Task Automation

Automation of robotic surgery has the potential to improve the performance of surgeons and the quality of the life of patients. However, the automation of surgical tasks has challenging problems that must be resolved. One such problem is adaptive online trajectory planning based on the state of the surrounding dynamic environment. This study presents a framework for online trajectory planning in a dynamic environment for automatic assistance in robotic surgery. In the proposed system, a demonstration under various states of the environment is used for learning. The distribution of the demonstrated trajectory over the environmental conditions is modeled using a statistical model. The trajectory, under given environmental conditions, is computed as a conditional expectation using the learned model. Because of its low computational cost, the proposed scheme is able to generalize and plan a trajectory online in a dynamic environment. To design the motion of the system to track the planned trajectory in a stable and smooth manner, the concept of a sliding mode control was employed; its stability was proved theoretically. The proposed scheme was implemented on a robotic surgical system and the performance was verified through experiments and simulations. These experiments and simulations verified that the developed system successfully planned and updated the trajectories of the learned tasks in response to the changes in the dynamic environment.
The Social Ecological System

This paper sheds light on the ecological transition phenomenon by suggesting a sustainable development model. This model demonstrates the different variables that impact the climate which in return suggests actions that ensures a renewal in our way of consuming, producing, working and living together. The paper then explains the factors that prevent the ecological transition in Morocco because the protection of the environment is conditioned by the implementation of preventive and corrective actions in order to remedy the harmful effects of overusing the existing natural resources. The first part is dedicated to explaining the components of economical model as well as the tools used in our research, the second part is dedicated to decorticating the model and its underlying mechanisms, and the last part explains the fuzzy logic behind it.
An ultra-low-energy/frame multi-standard JPEG co-processor in 65nm CMOS with sub/near-threshold power supply

Many digital ICs can benefit from sub/near threshold operations that provide ultra-low-energy/operation for long battery lifetime. In addition, sub/near threshold operation largely mitigates the transient current hence lowering the ground bounce noise. This also helps to improve the performance of sensitive analog circuits on the chip, such as delay-lock loops (DLL), which is crucial for the functioning of large digital circuits. However, aggressive voltage scaling causes throughput and reliability degradation. This paper presents SubJPEG, a state of the art multi-standard 65nm CMOS JPEG encoding coprocessor that enables ultra-wide VDD scaling. With a 0.45V power supply, it delivers 15fps 640×480 VGA application with only 1.3pJ/operation energy consumption per DCT and quantization computation. This co-processor is very suitable for applications such as digital cameras, portable wireless and medical imaging. To the best of our knowledge, this is the largest sub-threshold processor so far.
Focusing on Liquid Refinement Typing

We present a foundation systematizing, in a way that works for any evaluation order, the variety of mechanisms for SMT constraint generation found in index refinement and liquid type systems. Using call-by-push-value, we design a polarized subtyping relation allowing us to prove that our logically focused typing algorithm is sound, complete, and decidable, even in cases seemingly likely to produce constraints with existential variables. We prove type soundness with respect to an elementary domain-theoretic denotational semantics. Soundness implies, relatively simply, our system’s totality and logical consistency.
Improved Privacy-Preserving PCA Using Space-optimized Homomorphic Matrix Multiplication

Principal Component Analysis (PCA) is a pivotal technique widely utilized in the realms of machine learning and data analysis. It aims to reduce the dimensionality of a dataset while minimizing the loss of information. In recent years, there have been endeavors to utilize homomorphic encryption in privacy-preserving PCA algorithms for the secure cloud computing scenario. These approaches commonly employ a PCA routine known as PowerMethod, which takes the covariance matrix as input and generates an approximate eigenvector corresponding to the primary component of the dataset. However, their performance is constrained by the absence of an eﬃcient homomorphic covariance matrix computation circuit and an accurate homomorphic vector normalization strategy in the PowerMethod algorithm. In this study, we propose a novel approach to privacy-preserving PCA that addresses these limitations, resulting in superior eﬃciency, accuracy, and scalability compared to previous approaches. We attain such eﬃciency and precision through the following contributions: (i) We implement space and speed optimization techniques for a homomorphic matrix multiplication method, speciﬁcally tailored for parallel computing scenarios. (ii) Leveraging the beneﬁts of this optimized matrix multiplication, we devise an eﬃcient homomorphic circuit for computing the covariance matrix homomorphically. (iii) Utilizing the covariance matrix, we develop a novel and eﬃcient homomorphic circuit for the PowerMethod that incorporates a universal homomorphic vector normalization strategy to enhance both its accuracy and practicality. Our privacy-preserving PCA scheme, implemented using our innovative homomorphic Power-Method circuit, surpasses state-of-the-art approaches with an average speedup of 1.9 times on datasets with size 256 × 256, along with an average improvement in R2 accuracy of 0.16. Notably, our scheme demonstrates an even more remarkable estimated speedup of 25 times when applied to larger datasets with size 60000 × 256, showcasing scalability that has not been reported by previous approaches.
Stochastic space‐time regional rainfall modeling adapted to historical rain gauge data

Stochastic rainfall models are important tools both for practical issues and in studies of weather‐ and climate‐sensitive systems. We propose an event‐based model, continuous in space (two‐dimensional) and time, that describes regional‐scale, ground‐observed storms by a Boolean random field of rain patches. The model creates complex space‐time structures with a mathematically tractable framework. The estimation method relates temporal observations at fixed sites to the movement of the model storm rain field, thereby making historical rain gauge data suitable for model fitting. The model is estimated using hourly historical data at eight rain gauges in Alabama and tested for its capabilities in capturing statistical characteristics of the historical data, including rainfall intensity, rainfall intensity extremes, temporal correlation, effects of temporal aggregation, spatial coverage, and spatial correlation.
Monitoring 2.5 μm particulate matter within urbanized regions using satellite-derived aerosol optical thickness, a study in Hong Kong

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R 2 = 0.78 and R 2 = 0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM2.5 concentrations. When a whole year (2007) of AOT images was compared with PM2.5 concentrations recorded at five ground stations, correlations of R 2 = 0.31, R 2 = 0.10 and R 2 = 0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM2.5 concentration were also observed for individual stations (R 2 = 0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities.
Intelligent Assistive System Using Real-Time Action Recognition for Stroke Survivors

Cog Watch is an EU project developing technologies for cognitive rehabilitation of stroke patients. The Cog Watch prototype is an automatic system to re-train patients with Apraxia or Action Disorganization Syndrome (AADS) to complete activities of daily living (ADLs). This paper describes the approach to automatic planning based on a Markov Decision Process, and real-time action recognition (AR) based on instrumented objects using Hidden Markov Models. The experimental results demonstrate the ability of a psychologically plausible planning system integrated in a Task Model (TM) to improve task performance via user simulation, and the viability of the approach to AR.
DeepDev-PERF: a deep learning-based approach for improving software performance

Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched by performance experts. Recent advancements in deep learning approaches and the wide-spread availability of open-source data creates a great opportunity to automate the identification and patching of performance problems. In this paper, we present DeepDev-PERF, a transformer-based approach to suggest performance improvements for C# applications. We pretrain DeepDev-PERF on English and Source code corpora, followed by finetuning for the task of generating performance improvement patches for C# applications. Our evaluation shows that our model can generate the same performance improvement suggestion as the developer fix in 53
Radiometric surface temperature calibration effects on satellite based evapotranspiration estimation

Agriculture on the Texas High Plains (THP) uses approximately 89% of groundwater withdrawals from the Ogallala Aquifer, leading to steady decline in water table levels. Therefore, efficient water management is essential for sustaining agricultural production in the THP. Accurate evapotranspiration (ET) maps provide critical information on actual spatio‐temporal crop water use. METRIC (Mapping Evapotranspiration at High Resolution using Internalized Calibration) is a remote sensing based energy balance method that uses radiometric surface temperature (T s) for mapping ET. However, T s calibration effects on satellite based ET estimation are less known. Further, METRIC has never been applied for the advective conditions of the semi‐arid THP. In this study, METRIC was applied and predicted ET was compared with measured values from five monolithic weighing lysimeters at the USDA‐ARS Conservation and Production Research Laboratory in Bushland, Texas, USA. Three different levels of calibration were applied on a Landsat 5 Thematic Mapper's thermal image acquired on 23 July 2006 to derive T s. Application of METRIC on a MODTRAN calibrated image improved the accuracy of distributed ET prediction. In addition, ET estimates were further improved when a THP‐specific model was used for estimating leaf area index. Results indicated that METRIC performed well with ET mean bias error±root mean square error of 0.4±0.7 mm d−1.
A budget-balanced and strategy-proof auction for multi-passenger ridesharing

Ridesharing and ridesourcing services have become widespread, and properly pricing rides is a crucial problem for these services. We propose and analyze a budget-balanced and strategy-proof mechanism, the Weighted Minimum Surplus (WMS) auction, for the dynamic ridesharing problem with multiple passengers per ride. We also propose and analyze a budget-balanced version of the well-known VCG mechanism, the $\mathrm{VCG}_s$. Under the assumption of downward closed alternatives, we obtain a lower bound for the surplus welfare and surplus profit of the WMS. Finally, we present an exact algorithm based on integer linear programming to solve these auctions. Encouraging experimental results of profit and welfare were obtained for both the WMS auction and the $\mathrm{VCG}_s$.
Non-probabilistic Supervised Learning for Non-linear Convex Variational Problems

In this article we propose, based on a non-probabilistic supervised learning approach, a new general mathematical framework to solve non-linear convex variational problems on reﬂexive Banach spaces. The variational problems covered by this methodology include the standard ones. Concerning the training sets considered in this work, called radial dictionaries, the deﬁnition includes, among others, tensors in Tucker format with bounded rank and Neural Networks with ﬁxed architecture and bounded parameters. The training set will be used to construct, from an iterative algorithm deﬁned by a multivalued map, a sequence called progressive learning by dictionary optimization. We prove the convergence of this sequence to the solution of the variational problem. Furthermore, we obtain the same rate of convergence which is obtained in the Method of Steepest Descend implemented in a reﬂexive Banach space, O ( m − 1 ) .
End-to-End Multi-Task Deep Learning and Model Based Control Algorithm for Autonomous Driving

End-to-end driving with a deep learning neural network (DNN) has become a rapidly growing paradigm of autonomous driving in industry and academia. Yet safety measures and interpretability still pose challenges to this paradigm. We propose an end-to-end driving algorithm that integrates multi-task DNN, path prediction, and control models in a pipeline of data ﬂow from sensory devices through these models to driving decisions. It provides quantitative measures to evaluate the holistic, dynamic, and real-time performance of end-to-end driving systems, and thus allows to quantify their safety and interpretability. The DNN is a modiﬁed UNet, a well known encoder-decoder neural network of semantic segmentation. It consists of one segmentation, one regression, and two classiﬁcation tasks for lane segmentation, path prediction, and vehicle controls. We present three variants of the modiﬁed UNet architecture having di ﬀ erent complexities, compare them on di ﬀ erent tasks in four static measures for both single and multi-task (MT) architectures, and then identify the best one by two additional dynamic measures in real-time simulation. We also propose a learning-and model-based longitudinal controller using model predictive control method. With the Stanley lateral controller, our results show that MTUNet outperforms an earlier modiﬁed UNet in terms of curvature and lateral o ﬀ set estimation on curvy roads at normal speed, which has been tested in a real car driving on real roads.
A 0.35µm CMOS comparator circuit for high-speed ADC applications

A high-speed differential clocked comparator circuit is presented. The comparator consists of a preamplifier and a latch stage followed by a dynamic latch that operates as an output sampler. The output sampler circuit consists of a full transmission gate (TG) and two inverters. The use of this sampling stage results in a reduction in the power consumption of this high-speed comparator. Simulations show that charge injection of the TG adds constructively to the sampled signal value, therefore amplifying the sampled signal with a modest gain of 1.15. Combined with the high gain of the inverters, the sampled signals are amplified toward the rail voltages. This comparator is designed and fabricated in a 0.35 /spl mu/m standard digital CMOS technology. Measurement results show a sampling frequency of 1 GHz with 16 mV resolution for a 1 V input signal range and 2 mW power consumption from a 3.3 V supply. The architecture can be scaled down to smaller feature sizes and lower supply voltages.
A large deviations perspective on ordinal optimization

We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature in an approximate mathematical framework under the assumption that the underlying random variables have a Gaussian distribution. We use the large deviations theory to develop a mathematically rigorous framework for determining the optimal allocation of computing resources even when the underlying variables have general, nonGaussian distributions. Further, in a simple setting we show that when there exists an indifference zone, quick stopping rules may be developed that exploit the exponential decay rates of the probability of false selection. In practice, the distributions of the underlying variables are estimated from generated samples leading to performance degradation due to estimation errors. On a positive note, we show that the corresponding estimates of optimal allocations converge to their true values as the number of samples used for estimation increases to infinity.