Miracle-dz/UniFAR-FTUs huggingface dataset:
A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman ltering

We present a new approach for estimating approximative solutions of the inverse problem of EEG generation. Unlike with previous approaches, we develop our solution in a dynamical framework; to this end we propose a new extension of Kalman ltering to the case of spatiotemporal dynamics. The inclusion of the temporal aspect of the problem opens up new perspectives for improved accuracy of the estimates and for data-driven design of dynamical models. For the purpose of estimating the parameters of such models from given data we employ a likelihood maximisation approach. The performance of the algorithm is demonstrated by application to simulated EEG time series. It is shown that the availability of appropriate dynamical models is a crucial precondition for obtaining improved inverse solutions.
The Effects of Microfluidization and Homogenization on the Composition and Structure of Liposomal Aggregates from Whey Buttermilk and Commercial Buttermilk

ALL RIGHTS RESERVED iii COMMITTEE MEMBERSHIP TITLE: The effects of microfluidization and homogenization on the composition and structure of liposomal aggregates from whey buttermilk and commercial buttermilk AUTHOR: iv ABSTRACT The effects of microfluidization and homogenization on the structure of liposomal aggregates from whey buttermilk and commercial buttermilk Milk derived ingredients from the production of cheese and butter can be used as vehicles for nutrients. Buttermilk is a nutritious product of milk that comes from the churning of cream into butter. One of the advantages of buttermilk is that it is enriched in milk fat globule components, such as phospholipids and forms emulsions with fat when treated with high shear. The objective of this work was to explore the effects of shear on regular buttermilk and whey buttermilk in terms of liposomal aggregate size and chemical composition. The effects of microfluidization at 2000 psi and homogenization at 2000 psi/500 psi on the particle size distribution of liposomal aggregates between whey buttermilk (WBM) at pH 4.6 and 6.8 and commercial sweet buttermilk (SBM) at pH 4.60 were compared with whey protein isolate (WPI) at pH 4.6. At pH 6.80, WPI and SBM are too soluble in water to measure particle size but WBM is not as soluble. From this investigation, the mean particle diameter of the SBM aggregates at pH 4.6 decreased after the first pass through the microfluidizer and the same is true, after homogenization. SBM aggregates at pH 4.6 had a significantly larger mean particle diameter before treatments in both shear processes compared to WPI at pH 4.6 and WBM at pH 4.6 and WBM at pH 6.8 (p < 0.0001). WPI at pH 4.6 and WBM at both pH showed no significant differences in their mean particle size in both homogenized and microfluidized treatments. WPI and SBM samples resulted in significant particle diameter differences from before to after homogenizing at pH 4.6. SBM at pH 4.6 had significantly larger v average particle diameter than WBM at pH 4.6 (p < 0.0002), WPI at pH 4.6 (p < 0.0002) and WBM at pH 6.8 (p < 0.0045) before microfluidization at pass 0. WBM and WPI across all treatments showed very similar tendencies in small particle size attributes and some similarities in protein composition. In addition, the small aggregate size of WBM is suggested to be influenced by the presence of phospholipids and thus, creating significantly smaller mean particles compared …
Technology in the Classroom: An Empirical Study with an International Perspective

Researchers posit that the use of information and communication technology (ICT) will revolutionize traditional educational methods in university contexts and change the process of how educators do their job. However, research offers conflicting views regarding the benefits of ICT in the classroom. To better understand the various advantages and disadvantages of using ICT in university teaching, findings of a major cross-sectional empirical study of the perceptions and practices of international business faculty located in fourteen countries are reported. The study identifies obstacles that prevent faculty from
Do Broad Scope Managerial Accounting Systems Moderate the Effects of Budget Emphasis, Budget Participation and Perceived Environmental Uncertainty on the Propensity to Create Budgetary Slack?

This study empirically investigates the potential moderating effect of broad scope managerial accounting system use on several variables previously shown to contribute to the formation of budgetary slack. It is hypothesized that the additional information provided via a broad scope managerial accounting system would reduce uncertainty for managers and eliminate a portion of the need to build excess slack into the budget. Survey data collected from Fortune 1000 managers indicates that the use of broad scope managerial accounting systems does not moderate the impact of budget emphasis, budget participation and perceived environmental uncertainty on the propensity to create budget slack.
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks

Deep neural networks have been shown to be vulnerable to adversarial examples, perturbed inputs that are designed specifically to produce intentional errors in the learning algorithms. However, existing attacks are either computationally expensive or require extensive knowledge of the target model and its dataset to succeed. Hence, these methods are not practical in a deployed adversarial setting. In this paper we introduce an exploratory approach for generating adversarial examples using procedural noise. We show that it is possible to construct practical black-box attacks with low computational cost against robust neural network architectures such as Inception v3 and Inception ResNet v2 on the ImageNet dataset. We show that these attacks successfully cause misclassification with a low number of queries, significantly outperforming state-of-the-art black box attacks. Our attack demonstrates the fragility of these neural networks to Perlin noise, a type of procedural noise used for generating realistic textures. Perlin noise attacks achieve at least 90% top 1 error across all classifiers. More worryingly, we show that most Perlin noise perturbations are “universal” in that they generalize, as adversarial examples, across large portions of the dataset, with up to 73% of images misclassified using a single perturbation. These findings suggest a systemic fragility of DNNs that needs to be explored further. We also show the limitations of adversarial training, a technique used to enhance the robustness against adversarial examples. Thus, the attacker just needs to change the perspective to generate the adversarial examples to craft successful attacks and, for the defender, it is difficult to foresee a priori all possible types of adversarial perturbations.
Procedural Noise Adversarial Examples for Black-Box A acks on Deep Neural Networks

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples – perturbed inputs specically designed to produce intentional errors in the learning algorithms at test time. Various white-box aacks have proven the fragility of DNNs against adversarial examples. However, existing black-box aacks are either computationally expensive or require extensive knowledge of the target model and its dataset to succeed. us, such methods are not practical in a concrete seing. In this paper we introduce a more structured approach for generating adversarial examples in computer vision problems based on the use of procedural noise paerns. Our approach unveils a systemic vulnerability of DNNs to such paerns in this application domain. We further demonstrate that it is possible to construct practical black-box attacks with low computational cost and a reduced number of queries against robust architectures such as Inception v3 and ResNet v2 on the ImageNet dataset. We propose to use Bayesian optimization to eciently learn the parameters that control the structure of the adversarial noise paerns. Using such an approach our black-box aack outperforms existing white and black-box aacks against ImageNet. We also evidence the limitations of adversarial training: the aacker just needs to change their perspective to generate the adversarial examples to bypass the defence and cra successful attacks, whereas, for the defender, it is dicult to foresee all possible types of adversarial perturbations. Finally, and more worryingly, we show that our procedural noise perturbations are “universal” and “transferable” in that they generalize across dierent inputs and models respectively. is nding may give insight on the nature of transferable and universal adversarial perturbations.
Robotic Infrared Vision: Enabling operation in fog and low-light environments

—The operation of robots in fog or smoke is a challenging task for both autonomous and remote-controlled operations. To increase the situational awareness and observational abilities, a legged robot was equipped with a thermal camera, which was integrated into the controlling unit to enable operation in environments which would not have been accessible without. The interface between the robot and thermal camera control will be addressed in this context, as well as the physical and mechanical setup. Additionally to the communication between Spot and the thermal camera, an option was developed to access the data stream from an external computer within the network. The software developed for this purpose will be introduced as well.The performance of the system will be discussed on the basis of a ﬁeld test in fog. Demonstrating that the implemented system enhanced the situational awareness of the operator and allowed for operation in a scenario, which would have been impossible for remote robot operation without the additional capability of thermal imaging. In particular, the system enabled the operator to detect a person in an environment with high fog density. The robot was further able to approach the person in an obstacle ﬁlled environment and therefore gather more detailed information about the scene.
eScholarship@UMassChan eScholarship@UMassChan

DNA replication is regulated by the location and timing of replication initiation. Therefore, much effort has been invested in identifying and analyzing the sites of human replication initiation. However, the heterogeneous nature of eukaryotic replication kinetics and the low efficiency of individual initiation site utilization in metazoans has made mapping the location and timing of replication initiation in human cells difficult. A potential solution to the problem of human replication mapping is single-molecule analysis. However, current approaches do not provide the throughput required for genome-wide experiments. To address this challenge, we have developed Optical Replication Mapping (ORM), a high-throughput single-molecule approach to map newly replicated DNA, and used it to map early initiation events in human cells. The single-molecule nature of our data, and a total of more than 2000-fold coverage of the human genome on 27 million fibers averaging ~300 kb in length, allow us to identify initiation sites and their firing probability with high confidence. In particular, for the first time, we are able to measure genome-wide the absolute efficiency of human replication initiation. We find that the distribution of human replication initiation is consistent with inefficient, stochastic initiation of heterogeneously distributed potential initiation complexes enriched in accessible chromatin. In particular, we find sites of human replication initiation are not confined to well-defined replication origins but are instead distributed across broad initiation zones consisting of many initiation sites. Furthermore, we find no correlation of initiation events between neighboring initiation zones. Although most early initiation events occur in early-replicating regions of the genome, a significant number occur in late - replicating regions. The fact that initiation sites in typically late-replicating regions have some probability of firing in early S phase suggests that the major difference between initiation events in early and late replicating regions is their intrinsic probability of firing, as opposed to a qualitative difference in their firing-time distributions. Moreover, modeling of replication kinetics demonstrates that measuring the efficiency of initiation-zone firing in early S phase suffices to predict the average firing time of such initiation zones throughout S phase, further suggesting that the differences between the firing times of early and late initiation zones are quantitative, rather than qualitative. These observations are consistent with stochastic models of initiation-timing regulation and suggest that stochastic regulation of replication kinetics
Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments

This paper presents a framework for spatially correlated sparse multiple input multiple output (MIMO) channel path delay estimation in scattering environments. In MIMO outdoor communication scenarios, channel impulse responses of different transmit-receive antenna pairs are often supposed to be sparse due to few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit-receive antenna pair. In fact, in a more realistic situation, and due to the presence of scatterers in the environment, it would be more practical to deal with the received signals as clusters of multi-rays around mean time delays. In this paper we deal with such situation and we propose a subspace based method for estimating the mean path delays of the channel.
The inflationary trispectrum

We calculate the trispectrum of the primordial curvature perturbation generated by an epoch of slow-roll inflation in the early universe, and demonstrate that the non-Gaussian signature imprinted at horizon crossing is unobservably small, of order τNL ≲ r/50, where r < 1 is the tensor-to-scalar ratio. Therefore any primordial non-Gaussianity observed in future microwave background experiments is likely to have been synthesized by gravitational effects on superhorizon scales. We discuss the application of Maldacena’s consistency condition to the trispectrum.
Data driven methods for effective micromobility parking

Proliferation of shared urban mobility devices (SUMDs), particularly dockless e-scooters, has created opportunities for users with efficient, short trips, but raised management challenges for cities and regulators in terms of safety, infrastructure, and parking. There is a need in some high-demand areas for dedicated parking locations for dockless e-scooters and other devices. We propose the use of data generated by SUMD trips for establishing locations of parking facilities and assessing their required capacity and anticipated utilization. The problem objective is: find locations for a given number of parking facilities that maximize the number of trips that could reasonably be ended and parked at these facilities. Posed another way, what is the minimum number and best locations of parking facilities needed to cover a desired portion of trips at these facilities? In order to determine parking locations, areas of high-density trip destination points are found using unsupervised machine learning algorithms. The dwell time of each device is used to estimate the number of devices parked in a location over time and the necessary capacity of the parking facility. The methodology is tested on a dataset of approximately 100,000 e-scooter trips at Vanderbilt University in Nashville, Tennessee, USA. We find DBSCAN to be the most effective algorithm at determining high-performing parking locations. A selection of 19 parking locations, is enough to capture roughly 25% of all trips in the dataset. The vast majority of parking facilities found require a mean capacity of 6 scooters when sized for the 98th percentile observed demand.
Image coding using wavelet transforms and entropy-constrained trellis-coded quantization

The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multi-resolution approximations. Multi-resolution decomposition schemes have proven to be very effective for high-quality, low bit-rate image coding. In this work, we investigate the use of entropy-constrained trellis-coded quantization (ECTCQ) for encoding the wavelet coefficients of both monochrome and color images. ECTCQ is known as an effective scheme for quantizing memoryless sources with low to moderate complexity, The ECTCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. Performance comparisons are made using the classical quadrature mirror filter bank of Johnston and nine-tap spline filters that were built from biorthogonal wavelet bases. We conclude that the encoded images obtained from the system employing nine-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise ratios are obtained for encoding monochrome and color versions of the 512x512 "Lenna" image. Comparisons with other results from the literature reveal that the proposed wavelet coder is quite competitive.
The Art of Prolog

ly, this program guesses nondeterministically the correct permutation via permutation(Xs,Ys), and ordered checks that the permutation is actually ordered. Operationally, the behavior is as follows. A query involving sort is reduced to a query involving permutation and ordered. A failure-driven loop ensues. A permutation of the list is generated by permutation and tested by ordered. If the permuted list is not ordered, the execution backtracks to the permutation goal, which generates another permutation to be tested. Eventually an ordered permutation is generated and the computation terminates. Permutation sort is a highly inefficient sorting algorithm, requiring time super-exponential in the size of the list to be sorted. Pushing the tester into the generator, however, leads to a reasonable algorithm. The generator for permutation sort, permutation, selects an arbitrary element and recursively permutes the rest of the list. The tester, ordered, verifies that the first two elements of the permutation are in order, then recursively checks the rest. If we view the combined recursive permutation and ordered goals as a recursive sorting process, we have the basis for insertion sort, Program 3.21. To sort a list, sort the tail of the list and insert the head of the list into its correct place in the order. The arbitrary selection of an element has been replaced by choosing the first element. Another example of the advantage of intertwining generating and testing can be seen with programs solving the N queens problem. The N queens problem requires the placement of N pieces on an Nby-N rectangular board so that no two pieces are on the same line: horizontal, vertical, or diagonal. The original formulation called for 8 queens to be placed on a chessboard, and the criterion of not being on the same line corresponds to two queens not attacking each other under the rules of chess. Hence the problem's name. 253 Nondeterministic Programming
RoboPatriots: George Mason University 2010 RoboCup Team

The RoboPatriots are a team of three humanoid robots sponsored by the Computer Science Department at George Mason University. Each robot is based on the Kondo KHR-3HV and a customized Surveyor SVS camera (see Figure 1(a)). Our goals for RoboCup 2010 are to develop a durable hardware and software platform for future research activities into vision based localization, path planning in dynamic environments, and multi-robot coordination. In addition, we continued our research into evolving human-like motions [1].
Supporting Event-based Unified Data Access / query over Integrated Data-views for Decision Making in Geographic Information Systems

Geographic information is critical for building disaster planning, crisis management and early-warning systems. Decision making in Geographic Information Systems (GIS) increasingly relies on analyses of spatial data in map-based formats. Maps are complex structures composed of layers created from distributed heterogeneous data belonging to the separate organizations. This chapter presents a distributed service architecture for managing the production of knowledge from distributed collections of observations and simulation data through integrated data-views. Integrated views are defined by a federation service (“federator”) located on top of the standard service components. Common GIS standards enable the construction of this system. However, compliance requirements for interoperability, such as XML-encoded data and domain specific data characteristics, have costs and performance overhead. We investigate issues of combining standard compliance with performance. Although our framework is designed for GIS, we extend the principles and requirements to general science domains and discuss how these may be applied. * Corresponding author.
Stimulated saliva has a distinct composition that influences release of volatiles from wine

Aroma perception plays an important role in wine preference and evaluation and varies between groups of wine consumers. Saliva influences the release of aroma in the oral cavity. The composition of human saliva varies depending on stimulation; however, the compositional differences of stimulated and unstimulated saliva and their influences on aroma release have not been evaluated. In this study, we recruited healthy adults, of which 15 were Australian and 15 Chinese. Three types of saliva were collected from each participant: before, during, and after salivary stimulation. The collected salivary samples were characterised by flow rate, total protein concentration, esterase activity and microbiome composition by full-length 16S rRNA gene sequencing. The saliva samples were mixed with wine to investigate the differences in released volatiles by headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS). Differences in salivary composition and specific wine volatiles were found between Australian and Chinese participants, and amongst the three stimulation stages. Differential species were identified and significant correlations between the relative abundance of 3 bacterial species and 10 wine volatiles were observed. Our results confirm the influence of host factors and stimulation on salivary composition. Understanding the interactions of salivary components, especially salivary bacteria, on the release of aroma during wine tasting allows nuanced appreciation of the variability of flavour perception in wine consumers.
Linking Neural Manifolds to Principles of Circuit Structure

While large-scale recording techniques indicate that the activity of heterogeneous neuronal populations lies on low-dimensional “neural manifolds”, it has remained challenging to reconcile this picture with the classical view of precisely tuned neurons interacting with each other in some ordered circuit structure. Using a modelling approach, we provide a conceptual, yet mathematically precise, link between these two contrasting views. We first show that there is no unique relationship between the circuit structure and the emergent low-dimensional dynamics that characterise the population activity. We then propose a method for retrieving the circuit structure from recordings of the population activity and test it on artificial data. Our approach provides not only a unifying framework for circuit and field models on one side, and low-rank networks on the other side, but also opens the perspective to identify principles of circuit structure from large-scale recordings.