Miracle-dz/UniFAR-FTUs huggingface dataset:
Endogenous Monetary-Fiscal Regime Change in the United States

We estimate U.S. monetary and fiscal policy regime interactions in a regime switching model where regimes are represented by endogenous latent policy factors. Policy regimes interact strongly: shocks that switch one policy from active to passive tend to induce the other policy to switch from passive to active, consistent with existence of a unique equilibrium. In some periods, though, both policies are active and government debt grows rapidly. We also observe relatively strong interactions between monetary and fiscal policy regimes after the recent financial crisis. Latent policy regime factors exhibit patterns of correlation with macroeconomic time series, suggesting that policy regime change is endogenous. JEL Classification: C13, C32, C38, E52, E58, E63
Detailed simulation of electrical demands due to nationwide adoption of heat pumps, taking account of renewable generation and mitigation

This study quantifies the increase in the peak power demand, net of non-dispatchable generation, that may be required by widespread adoption of heat pumps. Electrification of heating could reduce emissions but also cause a challenging increase in peak power demand. This study expands on previous studies by quantifying the increase in greater detail; considering a wider range of scenarios, the characteristics of heat pumps and the interaction between wind generation and demand side management (DSM). A model was developed with dynamic simulations of individual heat pumps and dwellings. The increase in peak net-demand is highly sensitive to assumptions regarding the heat pumps, their installation, building fabric and the characteristics of the grid. If 80% of dwellings in the UK use heat pumps, peak net-demand could increase by around 100% (54 GW) but this increase could be mitigated to 30% (16 GW) by favourable conditions. DSM could reduce this increase to 20%, or 15% if used with extensive thermal storage. If 60% of dwellings use heat pumps, the increase in peak net-demand could be as low as 5.5 GW. High-performance heat pumps, appropriate installation and better insulated dwellings could make the increase in peak net-demand due to the electrification of heating more manageable.
Finite Completeness of Categories of Petri Nets

The problem of finite completeness of categories of Petri nets is studied. Since Petri nets have finite products, the problem reduces to the issue of the existence of equalizers. We show that the categories of Petri nets with general and Winskel morphisms do not admit equalizers, and hence are not finitely complete. The main positive result of the paper states that reachable Petri nets with multiplicative morphisms form a finitely complete category. As an application of this result, some well-known categories are shown to be finitely complete. For instance, since all morphisms between reachable safe Petri nets are multiplicative, it follows that the category of reachable safe Petri nets is finitely complete.
Visual and Audiovisual Speech Visual discrimination of vowel duration

Department of Behavioural Sciences and Learning, Linko¨ping University, SwedenLinnaeus Centre HEAD, Swedish Institute for Disability Research, Linko¨ping University, SwedenLidestam, B. (2009). Visual discrimination of vowel duration. Scandinavian Journal of Psychology, 50, 427–435.Discrimination of vowel duration was explored with regard to discrimination threshold, error bias, and effects of modality and consonant context.A total of 122 normal-hearing participants were presented with disyllabic-like items such as /lal-lal/ or /mam-mam/ in which the lengths of the vowelswere systematically varied and were asked to judge whether the ﬁrst or second vowel was longer. Presentation was either visual, auditory, or audiovi-sual. Vowel duration differences varied in 24 steps: 12 with a longer ﬁrst /a/ and 12 with a longer last /a/ (range: ±33–400 ms). Results: 50% JNDswere smaller than the lowest tested step size (33 ms); 75% JNDs were in the 33–66 ms range for all conditions but V /lal/, with a 75% JND at66–100 ms. Errors were greatest for visual presentation and for /lal-lal/ tokens. There was an error bias towards reporting the ﬁrst vowel as longer, andthis was strongest for /mam-mam/ and when both vowels were short, possibly reﬂecting a sublinguistic processing strategy.Key words: Vowel duration, discrimination, perception, accuracy, bias.Bjo¨rn Lidestam, Department of Behavioural Sciences and Learning, Linko¨ping University, SE-581 83 Linko¨ping, Sweden. Tel: +46 13 282155;e-mail: bjorn.lidestam@liu.se
Dismantling the Taboo against Vaccines in Pregnancy

Vaccinating pregnant women in order to protect them, the fetus, and the child has become universal in no way at all. Prejudice in health professionals add to fears of women and their families. Both these feelings are not supported by even the smallest scientific data. Harmlessness for the mother and the child has been observed for seasonal, pandemic, or quadrivalent influenza, mono, combined polysaccharide or conjugated meningococcal or pneumococcal, tetanus toxoid, acellular pertussis, human papillomavirus, cholera, hepatitis A, Japanese encephalitis, rabies, anthrax, smallpox, yellow fever, mumps, measles and rubella combined, typhoid fever, inactivated or attenuated polio vaccines, and Bacillus Calmétte Guerin vaccines. Instead, the beneficial effects of influenza vaccine for the mother and the child as well as of pertussis vaccine for the child have been demonstrated. Obstetrician-gynecologists, general practitioners, and midwives must incorporate vaccination into their standard clinical care. Strong communication strategies effective at reducing parental vaccine hesitancy and approval of regulatory agencies for use of vaccines during pregnancy are needed. It must be clear that the lack of pre-licensure studies in pregnant women and, consequently, the lack of a statement about the use of the vaccine in pregnant women does not preclude its use in pregnancy.
Semi-Supervised Clustering Approach for P 300 based BCI Speller Systems

The paper presents a k-means based semi-supervised clustering approach for recognizing and classifying P300 signals for BCI Speller System. P300 signals are proved to be the most suitable Event Related Potential (ERP) signal, used to develop the BCI systems. Due to non-stationary nature of ERP signals, the wavelet transform is the best analysis tool for extracting informative features from P300 signals. The focus of the research is on semisupervised clustering as supervised clustering approach need large amount of labeled data for training, which is a tedious task. Hence works for small-labeled datasets to train classifiers. On the other hand, unsupervised clustering works when no prior information is available i.e. totally unlabeled data. Thus leads to low level of performance. The in-between solution is to use semi-supervised clustering, which uses a few labeled with large unlabeled data causes less trouble and time. The authors have selected and defined adhoc features and assumed the Clusters for small datasets. This motivates us to propose a novel approach that discovers the features embedded in P300 (EEG) signals, using an k-means based semisupervised cluster classification using ensemble SVM.
Reuse of CAN-Based Legacy Applications in Time-Triggered Architectures

Upcoming car series will be deployed with time-triggered communication protocols due to benefits with respect to bandwidth, predictability, dependability, and system integration. In present day automotive networks, controller area network (CAN) is the most widely used communication protocol. Today, up to five CAN buses and several private CAN networks result from the bandwidth limits of CAN in conjunction with constraints concerning bus utilization aimed at controlling transmission latencies. In this context, the upcoming introduction of time-triggered networks into series production offers the potential to reduce the number of CAN networks by exploiting the high bandwidth of the time-triggered network instead of CAN buses. Due to the elimination of CAN buses, the resulting reduction of wiring and connectors promises a significant reduction in hardware cost and reliability improvements. In order to support the reuse of existing CAN-based application software, this paper presents a solution for the emulation of a CAN communication service on top of an underlying time-triggered network. By providing to CAN-based applications the same interface as in a conventional CAN system, redevelopment efforts for CAN-based legacy software are minimized. For this purpose, a CAN emulation middleware operates between a operating system and the CAN-based applications. In a first step, the middleware establishes event channels on top of the communication network in order to support on-demand transmission requests at a priori unknown points in time. The middleware then emulates the carrier sense multiple access collision avoidance (CSMA/CA) media access protocol of a physical CAN network for passing messages received via event channels to the application in the correct temporal order. Finally, the application programming interface (API) of the widely used HIS/VectorCAN driver provides a handle-based programming interface with support for message filtering and callbacks. A validation setup with a time-triggered protocol (TTP) cluster demonstrates that the CAN emulation can handle CAN-based legacy software and a real-world communication matrix provided by the automotive industry
An X chromosome-wide association analysis identifies variants in GPR174 as a risk factor for Graves’ disease

Background Graves’ disease is a female preponderant autoimmune illness and the contribution of the X chromosome to its risk has long been appreciated. However, no X-linked susceptibility loci have been indentified from recent genome-wide association studies (GWAS). Methods We re-examined the X chromosome data from our recent GWAS for Graves’ disease by including males that were previously excluded from the X chromosome analyses. The data were analysed using logistic regression analysis including sex as a covariate, and an additive method assuming X chromosome inactivation, implemented in snpMatrix. Results A cluster of single nucleotide polymorphism (SNPs) at Xq21.1 was found showing association with genome-wide significance, among which rs3827440 was a non-synonymous SNP of GPR174 (Plogistic regression= 9.52×10−8; PsnpMatrix=4.60×10−9; OR=1.76, 95% CI 1.45 to 2.13). The association was reproduced in an independent sample collection set including 4564 Graves’ disease cases and 3968 sex matched controls (combined Plogistic regression=5.53×10−21; combined PsnpMatrix=4.26×10−22; OR=1.69, 95% CI 1.53 to 1.86). Notably, GPR174 was widely expressed in immune related tissues and rs3827440 genotypes were associated with distinct mRNA levels (p=0.002). GPR174 did not show sex biased gene expression in our expression analysis. Resequencing study suggested the contribution of some rare variants in the GPR174 gene region to disease risk with a collapsing p value of 1.16×10−3. Conclusions The finding of an X-linked risk locus for Graves’ disease expands our understanding of the role of the X chromosome in disease susceptibility.
A multipath network-on-chip topology

Network-on-Chip (NoC) is a new on chip communication design system that replaced bus-based communication in System-on-Chip (SoC). It provides efficient and scalable communication among the IPs (Intellectual Properties) in a single chip. Mainly, it was introduced to fulfill the growing demand of electronics system which needs to integrate a large number of computational resources into a single chip with an aim to get high performances, And to get efficient performance, the interconnection network plays an important role in chip design. The network architecture or topology should be highly scalable so that it can incorporate a large number of IPs without degrading the performance. In this paper, we proposed a network topology that provides multiple path diversity and high degree of scalability. The architecture of the proposed topology is described along with a packet based adaptive routing algorithm. We simulated the network using an open source simulator named OMNeT++. The topology is tested for average latency, number of events that to be carried in the simulation using a packet based routing algorithm and has been compared with existing Fat tree topology.
A Novel Process Meta-model for Developing Automatic Speech Recognition Systems

Nowadays, Automatic Speech Recognition (ASR) figures as the main character in the Human-Computer Interaction (HCI) scenario, not only for its great usability but also for its recent advances. Several commercial applications have been launched with built-in speech interfaces. Yet, building a speech recognition system is not a trivial task. Although the speech recognition disciplines are very well documented, they occur in an unrelated way. It is difficult to understand the logic process for building such a system. We propose a development process meta-model for ASR systems in order to guide anyone to build their own ASR system. The process is validated with two distinct case studies: a context-sensitive ASR for navigating mobile robots and a semantic-driven ASR for controlling a home automation system.
Intermediate feature space approach for anomaly detection in aircraft engine data

Change detection is an important task for remote monitoring, fault diagnostics and system prognostics. When a fault occurs, it will often times cause changes in measurable quantities of the system. Early detection of changes in system measurements that indicate abnormal conditions helps the diagnostics of the fault so that appropriate maintenance action can be taken before the fault progresses, causing secondary damage to the system and system downtime. This paper presents two approaches for fusing the output of multiple change detection algorithms using random forests. What is novel and interesting about the work presented here is that the partitioning of the data into different change scenarios before training the classifier fusion approach results in a significant improvement over even a straightforward fusion approach.
On Semibent Boolean Functions

We show that any Boolean function, in even dimension, equal to the sum of a Boolean function g which is constant on each element of a spread and of a Boolean function h whose restrictions to these elements are all linear, is semibent if and only if g and h are both bent. We deduce a large number of infinite classes of semibent functions in explicit bivariate (respectively, univariate) polynomial form.
Multi-label Hierarchical Text Classification using the ACM Taxonomy

Many of the works of text classification involve t he attribution of each text a single class label from a predefined se t of classes, usually small and flat organized (flat classification). However, ther e are more complex classification problems in which we can assign to e ach text more than one class (multi-label classification), that can be organized in a hierarchical structure (hierarchical classification) to support thematic s earches by browsing topics of interests. In this paper, a problem of multi-label hierarchi cal text classification is presented. The experiment involves the creation of a multi-label hierarchical text collection, its pre-processing, followed by th e application of different classifiers to the collection, and finally, the eva luation of the classifiers performance.
Forecasting Geo-sensor Data with Participatory Sensing Based on Dropout Neural Network

Nowadays, geosensor data, such as air quality and traffic flow, have become more and more essential in people's daily life. However, installing geosensors or hiring volunteers at every location and every time is so expensive. Some organizations may have only few facilities or limited budget to sense these data. Moreover, people usually tend to know the forecast instead of ongoing observations, but the number of sensors (or volunteers) will be a hurdle to make precise prediction. In this paper, we propose a novel concept to forecast geosensor data with participatory sensing. Given a limited number of sensors or volunteers, participatory sensing assumes each of them can observe and collect data at different locations and at different time. By aggregating these sparse data observations in the past time, we propose a neural network based approach to forecast the future geosensor data in any location of an urban area. The extensive experiments have been conducted with large-scale datasets of the air quality in three cities and the traffic of bike sharing systems in two cities. Experimental results show that our predictive model can precisely forecast the air quality and the bike rentle traffic as geosensor data.
From memristors to compositional machine learning: exploring neuromemristive algorithmic abstractions with the knowm API

Due to the recent congruence of big data and compute acceleration, the field of machine learning (ML) has made significant advancements, driven primarily by deep neural networks trained with the stochastic gradient descent algorithm (DNN). Error rates on challenging learning benchmarks have fallen precipitously. The algorithmic success of DNNs combined with the significant architectural mismatch between modern processors and neural architectures has resulted in a race to build DNN processors. The mismatch is demonstrated by analyzing the dynamic power consumption due to capacitive loss associated with synaptic access: [EQUATION]
Equivalence of nested queries with mixed semantics

We consider the problem of deciding query equivalence for a conjunctive language in which queries output complex objects composed from a mixture of nested, unordered collection types. Using an encoding of nested objects as flat relations, we translate the problem to deciding the equivalence between encodings output by relational conjunctive queries. This encoding equivalence cleanly unifies and generalizes previous results for deciding equivalence of conjunctive queries evaluated under various processing semantics. As part of our characterization of encoding equivalence, we define a normal form for encoding queries and contend that this normal form offers new insight into the fundamental principles governing the behaviour of nested aggregation.
A NOVEL APPROACH TO FACE RECOGNITION BASED ON THERMAL IMAGING

A novel approach to face recognition system based on thermal image is presented. The thermal images are captured using Thermal Mid Wave Infrared (MWIR) camera system at various temperature to take into consideration of subtle variations that may occur over time. The vasculature information from the image is extracted and used for matching process. Four images are used for registration process. The image registration is done with the Rigid Body Image Registration tool. Before registering Gabor filter is applied to the images and then filtered images are fused and registered. Face segmentation is done by localized region based active counters. The anisotropic diffusion filter is applied to an image and fused with the segmented image. The signature is then generated from the segmented image. Then templates are generated and stored for matching purpose. To perform authenticate the signatures are generated first and matched with the templates stored in the system
A Constraint Programming Approach for Instruction Assignment

A fundamental problem in compiler optimization, which has increased in importance due to the spread of multi-core architectures, is to find parallelism in sequential programs. Current processors can only be fully taken advantage of if workload is distributed over the available processors. In this paper we look at distributing instructions in a block of code over multi-cluster processors, the instruction assignment problem. The optimal assignment of instructions in blocks of code on multiple processors is known to be NP-complete. In this paper we present a constraint programming approach for scheduling instructions on multi-cluster systems that feature fast inter-processor communication. We employ a problemdecomposition technique to solve the problem in a hierarchicalmanner where an instance of the master problem solves multiple sub-problems to derive a solution. We found that our approach was able to achieve an improvement of 6-20 percent, on average, over the state-of-the-art techniques on superblocks from SPEC 2000 benchmarks.
Numerical Integration by Cubature Formulae in Bayesian Neural Networks

We compare two methods for approximating the high dimensional integrals encountered in Bayesian neural networks. The frequently used hybrid Monte Carlo method is contrasted with integration by cubature formulae. We combine the cubature integration method with a nonlinear transformation to make the approximation of the integral easier. An experimental comparison of both integration methods for our problem domain is given.
Posterior Probability Matching and Human Perceptual Decision Making

Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models’ performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools for testing for such a strategy.