Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanoscale molecular-switch crossbar circuits

Molecular electronics offer an alternative pathway to construct nanoscale circuits in which the critical dimension is naturally associated with molecular sizes. We describe the fabrication and testing of nanoscale molecular-electronic circuits that comprise a molecular monolayer of [2]rotaxanes sandwiched between metal nanowires to form an 8 × 8 crossbar within a 1 µm 2 area. The resistance at each cross point of the crossbar can be switched reversibly. By using each cross point as an active memory cell, crossbar circuits were operated as rewritable, nonvolatile memory with a density of 6. 4G bits cm −2 . B ys etting the resistances at specific cross points, two 4 × 4s ubarrays of the crossbar were configured to be a nanoscale demultiplexer and multiplexer that were used to read memory bits in a third subarray.
Spatial-spectral compressive sensing of hyperspectral image

Compressive sensing (CS) is a new emerging approach in recent years, and is applied in acquisition of signals having a sparse or compressible representation in some basis. The CS literature has mostly focused on the problems involving 1-D signals and 2-D images. However, for hyperspectral image, compressive acquisition of this signal is complicated for its 3-D structures. In this paper, we consider the correlation of spatial and spectral of hyperspectral image and propose spatial-spectral compressive sensing. The results show that the proposed method leads to an increase in CS reconstruction performance under the same compression ratio and reconstruction algorithm. In particular, our method is more advantageous in realizing airborne or spaceborne hyperspectral remote sensing for its lower memory storage.
Trading Network Performance for Cash in the Bitcoin Blockchain

Public blockchains have emerged as a plausible cloud-like substrate for applications that require resilient communication. However, sending messages over existing public blockchains can be cumbersome and costly as miners require payment to establish consensus on the sequence of messages. In this paper we analyze the network performance of the Bitcoin public ledger when used as a massaging substrate. We present several real-world observations on its characteristics, transaction visibility, and fees paid to miners; and we propose two models for fee-cost estimation. We find that applications to some extent can improve messaging latency by paying transaction fees. We also suggest that spendings should be kept below 300 Satoshi per byte.
System Architecture Directions for Tangible Cloud Computing

The field of wireless sensor networks has produced a range of supporting hardware and software technologies that facilitate the creation of sensor network applications. Despite these advances, the implementation of wireless sensor network applications remains a complex task that requires domain experts and a significant investment of time and money. This level of investment is often infeasible for single applications, especially for those applications with a short life-cycle. This paper suggests a new direction in wireless sensor network research. We argue that next generation sensor network platforms should strive towards a shared infrastructure, multi-application paradigm, with a clean separation of concerns between infrastructure providers and application developers. These are principles that are well established in the field of Cloud Computing. This paper introduces our vision for future sensor networks, which we refer to as the 'Tangible Cloud'. To support this vision, we introduce a reference architecture and pricing model for sensor network resources.
Predictors of Early Neurocognitive Deficits in Low-Risk Patients Undergoing On-Pump Coronary Artery Bypass Surgery

Background— Postoperative cognitive deficits (POCDs) are a source of morbidity and occur frequently even in low-risk patients undergoing cardiac surgery. Predictors of neurocognitive deficits can identify potentially modifiable risk factors as well as high-risk patients in whom alternate revascularization strategies may be considered. Methods and Results— 448 patients undergoing coronary surgery (coronary artery bypass graft [CABG]) underwent standardized preoperative and postoperative neurocognitive testing as part of 2 randomized trials evaluating the effects of mild hypothermia during coronary surgery. Prospectively collected data were used to identify univariate predictors of POCDs and multivariable logistic regression models were constructed. Models were bootstrapped 1000 times. POCDs occurred in 59% of patients. Significant univariate predictors included intraoperative normothermia, impaired left ventricular (LV) function, higher educational level, elevated serum creatinine and reduced creatinine clearance, prolonged intubation time, intensive care unit (ICU) stay, and hospital stay. Advanced age, presence of carotid disease, and cardiopulmonary bypass time were not associated with increased POCDs in this cohort. Multivariable modeling identified intraoperative normothermia (odds ratio [95% confidence interval] −1.15 [1.01, 1.31]), poor LV function (1.53 [1.02, 2.30]), and elevated preoperative creatinine (1.01 [1.00 to 1.03] for every 1 mmol/L increase), prolonged (>24 hours) ICU stay (1.88 [1.27 to 2.79]), and higher educational level (1.52 [1.01 to 2.28]) as independent predictors of POCD occurrence. Conclusions— Mild hypothermia, in the intraoperative and perioperative period, may be a protective strategy for the prevention of POCDs. Patients with elevated pre-operative creatinine and poor LV function carry a higher risk of POCDs and may benefit from revascularization strategies other than conventional on-pump CABG.
Spatio-spectral sufficient statistic for mental imagery EEG signals

Classification of mental tasks from electroencephalogram (EEG) signals has important applications in brain-computer interfacing (BCI). However, classification of the highly redundant and high-dimensional EEG signal, with high spatial and spectral correlations, is quite challenging. Therefore, the discriminant information, especially that of the first and second data moments, need to be extracted in the form of uncorrelated features. This work addresses this need by approximating a linear minimal-dimension sufficient statistic of the EEG matrix data in both spatial and spectral domains. As a result of the two-dimensional spatio-temporal approach and the generalized sufficiency approximation, a significant improvement on the classification accuracy is achieved.
Structured matrix-based methods for polynomial ∈-gcd: analysis and comparisons

The relationship between univariate polynomial ∈-gcd and factorization of resultant matrices is investigated and several stable and effective algorithms for the computation of an ∈-gcd are proposed. The main result is the design of a practically stable algorithm whose arithmetic cost is quadratic in the degrees of the input polynomials. The algorithm relies on the displacement structure properties of Sylvester and Bézout matrices. Its effectiveness is confirmed by numerical experiments.
A Vehicle Detection Algorithm Based on Deep Belief Network

Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.
A method for collision detection between needle and tissues in the needle insertion simulation

This paper presents a method to detect the collision between the needle model and voxel-based human tissue model for simulation of needle insertion. Four planes are used in front of the needle to protect penetration into bone in advance. Each plane is defined by using four points to consider thickness of the needle. Hounsfield unit (HU) values which represent the type of human tissue are collected at each point to identify colliding voxels. The size of voxel and needle, and the maximum velocity of the needle is identified and used to determine the relative position, and number of planes and points. The proposed method improves the performance of collision detection while maintaining the update rate at 1 kHz.
Providing freshness guarantees for outsourced databases

Database outsourcing becomes increasingly attractive as advances in network technologies eliminate the perceived performance difference between in-house databases and out-sourced databases, and price advantages of third-party data-base service providers continue to increase due to economy of scale. However, the potentially explosive growth of database outsourcing is hampered by security concerns, namely data privacy and query integrity of outsourced databases. While privacy issues of outsourced databases have been extensively studied, query integrity for outsourced databases has just started to draw attention from the database community. Currently, there still does not exist a solution that can provide complete integrity. In particular, previous studies have not examined the mechanisms for providing freshness guarantees, that is, the assurance that queries are executed against the most up-to-date data, instead of just some version of the data in the past. Providing a practical solution for freshness guarantees is challenging because continuously monitoring data's up-to-dateness is expensive. In this paper, we perform a thorough study on how to add freshness guarantees over proposed schemes (including authenticated data structure-based and probabilistic-based approaches) to provide integrity assurance. We implement our solutions and perform extensive experiments to quantify the cost. Our experiment results show that we can provide reasonable tight freshness guarantees without sacrificing much performance.
Detecting a large number of objects in real-time using apache storm

Object detection is an important function for intelligent multimedia processing, but its computational complexity prevented its pervasive uses in consumer electronics. To process large-scale datasets in real-time, more resources and reliable infrastructures are required for spreading the data and running the applications across multiple machines in parallel. In order to detect a large number of objects in real-time, a task-parallel processing framework based on Storm is proposed.
Reification, polymorphism and reuse: three principles for designing visual interfaces

This paper presents three design principles to support the development of large-scale applications and take advantage of recent research in new interaction techniques: Reification turns concepts into first class objects, polymorphism permits commands to be applied to objects of different types, and reuse makes both user input and system output accessible for later use. We show that the power of these principles lies in their combination. Reification creates new objects that can be acted upon by a small set of polymorphic commands, creating more opportunities for reuse. The result is a simpler yet more powerful interface. To validate these principles, we describe their application in the redesign of a complex interface for editing and simulating Coloured Petri Nets. The cpn2000 interface integrates floating palettes, toolglasses and marking menus in a consistent manner with a new metaphor for managing the workspace. It challenges traditional ideas about user interfaces, getting rid of pull-down menus, scrollbars, and even selection, while providing the same or greater functionality. Preliminary tests with users show that they find the new system both easier to use and more efficient.
Vitamin D Dietary Intake Questionnaire Validation Conducted among Young Polish Women

Due to inadequate intake of Vitamin D, identification of individuals characterised by the highest risk of deficiencies is one of the more crucial tasks for public health. The aim of the presented study was to assess the validity and reproducibility of the designed Vitamin D dietary intake questionnaire based on food frequency assessment—VIDEO-FFQ (VItamin D Estimation Only—Food Frequency Questionnaire) in a group of Polish women aged 20–30 years. Seventy-five participants kept a three-day dietary record and filled out the VIDEO-FFQ twice (immediately after the three-day dietary record and after six weeks). The assessment of validity and reproducibility was conducted by verifying standard errors of estimation, median differences, and percentages of individuals classified into tertiles, correlations and Bland-Altman plots. The Vitamin D intake for the majority of the surveyed women was inadequate as over 85% of them were characterised by values of intake lower than 5.0 μg per day. The results allowed concluding that a high accuracy of the VIDEO-FFQ was achieved. The required Bland-Altman index values lower than 5.0% were obtained, confirming satisfactory validity and reproducibility. The VIDEO-FFQ may be deemed a convenient practical tool for the estimation of Vitamin D intake in young women.
Productivity Adjustments and Learning-by-Doing as Human Capital

This paper measures plant-level productivity gains associated with learning curves across the entire manufacturing sector. We measure these gains at plant startups and also after major employment changes. We find: 1. ) The gains are strongly associated with a variety of human capital measures implying that learning-by-doing is largely a firm-specific human capital investment. 2. ) This implicit investment is large; many plants invest as much in learning-by-doing as they invest in physical capital and much more than they invest in formal job training. 3. ) This investment differs persistently over industries and is higher with greater R&D. 4. ) Consistent with a learning-by-doing interpretation, the human capital investment is much larger following employment decreases than increases. We conclude that learning-by-doing is a major factor in wage determination, technical progress and asymmetric employment adjustment costs.
Recent Advances in 3D Integration at IMEC

IMEC is focusing its 3D-integration technology developments in 3 distinct directions: 3D-System-in-aPackage (3D-SiP), 3D-Wafer-Level-Packaging (3D-WLP) and 3D-Stacked-IC (3D-SiC). First, the background of these separate approaches will be given. Next the materials and technologies involved, the typical characteristics and the ongoing developments will be discussed. Finally, the roadmap for the 3D-integration in IMEC will be presented.
DNA Repair Biomarkers XPF and Phospho-MAPKAP Kinase 2 Correlate with Clinical Outcome in Advanced Head and Neck Cancer

Background Induction chemotherapy is a common therapeutic option for patients with locoregionally-advanced head and neck cancer (HNC), but it remains unclear which patients will benefit. In this study, we searched for biomarkers predicting the response of patients with locoregionally-advanced HNC to induction chemotherapy by evaluating the expression pattern of DNA repair proteins. Methods Expression of a panel of DNA-repair proteins in formalin-fixed paraffin embedded specimens from a cohort of 37 HNC patients undergoing platinum-based induction chemotherapy prior to definitive chemoradiation were analyzed using quantitative immunohistochemistry. Results We found that XPF (an ERCC1 binding partner) and phospho-MAPKAP Kinase 2 (pMK2) are novel biomarkers for HNSCC patients undergoing platinum-based induction chemotherapy. Low XPF expression in HNSCC patients is associated with better response to induction chemoradiotherapy, while high XPF expression correlates with a worse response (p = 0.02). Furthermore, low pMK2 expression was found to correlate significantly with overall survival after induction plus chemoradiation therapy (p = 0.01), suggesting that pMK2 may relate to chemoradiation therapy. Conclusions We identified XPF and pMK2 as novel DNA-repair biomarkers for locoregionally-advanced HNC patients undergoing platinum-based induction chemotherapy prior to definitive chemoradiation. Our study provides insights for the use of DNA repair biomarkers in personalized diagnostics strategies. Further validation in a larger cohort is indicated.
Online Fusion of Incremental Learning for Wireless Sensor Networks

Ever-more ubiquitous embedded systems provide us with large amounts of data. Performing analysis close to the data source allows for data reduction while giving information when unexpected behavior (i.e. Anomalies in the system under observation) occurs. This work presents a novel approach to online anomaly detection, based on an ensemble of classifiers that can be executed on distributed embedded systems. We consider both single and multi-dimensional input classifiers that are based on prediction errors. Predictions of single-dimensional time series input come from either a linear function model or general statistics over a data window. Multi-dimensional input stems from current and historical sensor values as well as predictions. We combine the classifier outputs in the ensemble using a heuristic method and Fisher's combined probability test. The proposed framework is tested thoroughly using synthetic and real-world data. The results are compared to known methods for anomaly detection on limited-resource systems. While individual classifiers perform comparably to known methods, our results show that using an ensemble of classifiers increases the overall detection of anomalies considerably.
Hydrologic modeling of runoff from a livestock manure windrow composting site with a fly ash pad surface and vegetative filter strip buffers

Windrow composting of livestock manure materials provides a strategy for converting organic wastes into a recyclable soil fertility product that is less hazardous to the environment. Although outdoor windrow composting can produce runoff that is detrimental to surface water quality, vegetative filter strip (VFS) buffers were reported to significantly reduce runoff and contaminants from a windrow composting research site. To estimate the efficacy of VFS buffers and other best management practices on runoff from future windrow composting facilities, a computer hydrologic model may provide a valuable tool for predicting runoff losses from these proposed sites. This research evaluated a windrow composting/vegetative filter strip buffer (WCVFS) hydrologic model for estimating runoff volume losses from a livestock manure-based windrow composting site with a fly ash composting pad surface and VFS buffers. Runoff and physical attribute data from six rainfall events during 2002 to 2004 at a central Iowa windrow composting research site were used in the WCVFS model evaluation. Three rainfall events were designated as “wet” composting period events (2002 and 2003 seasons), and three were designated as “dry” composting period events (2004 season). Runoff data were comprised of average observed runoff volumes from three compost windrow area:VFS buffer area ratio treatments that included 1:1, 1:0.5 (large and small VFS buffer areas, respectively), and a 1:0 (no buffer) control. The WCVFS model performance was good to very good for the 2003 wet composting period model validation rainfall event with no significant differences among 1:1, 1:0.5, and 1:0 ratio treatments for simulated versus observed runoff volumes. In contrast, WCVFS model performance was unsatisfactory for the 2004 dry composting period validation event with significantly higher simulated runoff volume from the 1:0.5 ratio treatment versus observed runoff volumes. There were no significant differences for the 1:1 and 1:0 treatments. The WCVFS model effectively estimated 1:1, 1:0.5, and 1:0 treatment runoff volumes from the earlier wet composting period and 1:1 and 1:0 treatment runoff volumes from the later dry composting period rainfall events. However, the soils data-derived VFS buffer runoff and infiltration functions in the WCVFS model flow routing component may not have sufficiently accounted for some short-term hydrologic changes in VFS buffer soil and fly ash pad surfaces. This could have resulted in overestimation of dry composting period simulated runoff volume from the smaller 1:0.5 ratio VFS buffer area treatment. Consequently, the use of other alternatives to soils data-derived VFS buffer runoff and infiltration functions should be evaluated in future WCVFS model simulation trials to potentially improve runoff volume prediction accuracy.
Adaptive and dynamic load balancing methodologies for distributed environment

Distributed network computing environments have become a cost effective and popular choice to achieve high performance and to solve large scale computation problems. Unlike past supercomputers, a cluster or grid or peer-to-peer (P2P) system can be used as multipurpose computing platform to run diverse high performance parallel applications. Cluster computing environment consist of Personal Computers (PCs) that are interconnected using high speed networks and are located at same location where as grid computing involves coupled and coordinated use of geographically distributed resources for purposes such as large scale computation and distributed data analysis. Peer-To-Peer is composed of participants that make a portion of their resources (such as processing power, disk storage, and network bandwidth) available directly to their peers without intermediary network hosts or servers. Load Balancing (LB) in a distributed system is a process of sharing computational resources by transparently distributing system workload. A workstation user may not use the machine all the time, but may require more than it while working actively. Some resources may be heavily loaded, while other remains idle. Performance enhancement is one of the most important issues in distributed system. The performance of the system can often be improved to an acceptable level simply by distributing the load among the resources.
Automatic classification of shoeprints for use in forensic science based on the Fourier transform

This study developed a system of automatic classification of shoeprint images into groups belonging to the same sole pattern. When presented with an image of a new shoeprint the system displays a ranked sequence of shoeprint images from the database. The shoeprint images are ranked from best match to worst match in terms of the pattern of the shoeprint. For this study a database of 503 shoeprint images belonging to 139 pattern groups was established with each group containing 2 or more examples. The pattern grouping was performed by a panel of human experts. This designed system is a fully automatic method and functions with minimum user intervention. Tests of the system have shown that the first shoeprint image displayed is a correct match 54% of the time and that a correct match appears within the first 5% of displayed shoeprints 75% of the time. The system has translational and rotational invariance so that the spatial positioning of the new shoeprint images does not have to correspond with the spatial positioning of the shoeprint images of the database.