Miracle-dz/UniFAR-FTUs huggingface dataset:
Fetal ECG subspace estimation based on cyclostationarity

In this paper, we propose a strategy to estimate a Fetal Electrocardiogram (FECG) subspace from a set of mixed ECG recordings from the thoracic and abdominal electrodes attached on a pregnant woman. The ECGs from an expectant woman contain FECG that can provide valuable information for fetal health monitoring, such as the fetal heart rate (FHR). After applying blind source separation (BSS) methods to mixed ECG, independent components are obtained. The main purpose of this paper is to classify an FECG group from all of these components which can be classified as FECG, MECG and noise according to the features of signals. Inspired by the concept of multidimensional independent component analysis (MICA) and to automate the classification task, we propose a procedure based on cyclostationarity of FECGs, in particular, an integrated Cyclic Coherence. The method is validated on real world DaISy dataset and the results are promising.
Real-time unequal error protection for distortion-optimal progressive image transmission

For optimal progressive transmission of an embedded image code over a noisy channel, we consider an unequal error protection strategy that minimizes the average of the expected distortion over a set of intermediate rates. In contrast to previous work, we find a near-optimal solution in real-time. For a binary symmetric channel, two state-of-the-art source coders (SPIHT and JPEG200), and a rate-compatible punctured turbo coder as a channel coder, we compare our solution to the strategy that optimizes the end-to-end performance.
Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome

Predicting clinical outcome following a specific treatment is a challenge that sees physicians and researchers alike sharing the dream of a crystal ball to read into the future. In Medicine, several tools have been developed for the prediction of outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR) [1,2] but over the past few years artificial neural networks (ANNs) have become an increas‐ ingly popular alternative to LR analysis for prognostic and diagnostic classification in clinical medicine [3]. The growing interest in ANNs has mainly been triggered by their ability to mimic the learning processes of the human brain. The network operates in a feed-forward mode from the input layer through the hidden layers to the output layer. Exactly what interactions are mod‐ eled in the hidden layers is still under study. Each layer within the network is made up of com‐ puting nodes with remarkable data processing abilities. Each node is connected to other nodes of a previous layer through adaptable inter-neuron connection strengths known as synaptic weights. ANNs are trained for specific applications through a learning process and knowledge is usually retained as a set of connection weights [4]. The backpropagation algorithm and its var‐ iants are learning algorithms that are widely used in neural networks. With backpropagation, the input data is repeatedly presented to the network. Each time, the output is compared to the desired output and an error is computed. The error is then fed back through the network and used to adjust the weights in such a way that with each iteration it gradually declines until the neural model produces the desired output.
A Gaussian Mixture Filter for Near-Far Object Tracking

The near-far object tracking (NFOT) problem concerns the tracking of an extended object consisting of a set of resolvable sources that, due to finite sensor resolution, appears as a single point when at long range from the sensor. A Gaussian mixture filter for this problem is presented for the case of a linear object comprising a known number of sources. The filter structure is a combination of the probabilistic data association (PDA) filter and the multiple simultaneous measurement filter. The filter takes into account the uncertainty concerning the object's structure and orientation, as well as the presence of false alarms. Simulations for an open-loop pursuit scenario are presented that compare the performance of the filter with another Gaussian mixture filter that does not consider sensor resolution
Converting underwater imaging into imaging in air

The application of imaging devices in underwater environments has become a common practice. Protecting the camera's constituent electric parts against water leads to refractive effects emanating from the water-glass-air transition of light rays. These non-linear distortions can not be modeled by the pinhole camera model. For our new approach we focus on flat interface systems. By handling refractive effects properly, we are able to convert the problem to imaging conditions in air. We show that based on the location of virtual object points in water, virtual parameters of a camera following the pinhole camera model can be computed per image ray. This enables us to image the same object as if it was situated in air. Our novel approach works for an arbitrary camera orientation to the refractive interface. We show experimentally that our adopted physical methods can be used for the computation of 3D object points by a stereo camera system with much higher precision than with a naive in-situ calibration.
What Can We Learn from Academic Spin-Off Failures? Insights from Five Case Studies

Today's academic literature on new technology-based firms is rather growth and success-oriented, despite the fact that many valuable lessons can be learned from failures. This study aims at contributing to our understanding of failure processes by documenting five case studies of spin-offs that originated from European universities. Within the framework of the resource-based view of the firm and social capital theory, the venture's resource base is used as a central element in explaining the failures through the presence or absence of certain resources or by inappropriate application. The analysis mainly illustrated the negative influence of the lack of a champion, poor market development, the absence of market(ing) experience in the venture team and an unbalanced shareholder structure. The relatively small financial base seems rarely to be the main issues in the failure process.
Folded RNA from an Intron of One Gene Might Inhibit Expression of a Counteracting Gene

Homeostatic maintenance of mRNA levels including prompt availability of mRNAs for translation in response to changing protein demands might be partly enabled by a system of combinatorial controls involving noncoding RNA blocking agents. This article proposes a specific version of that control mechanism, namely, a double-stranded RNA folding from transcription of an intron of one gene might and leading to an agent that inhibits mRNA of a counteracting gene. Thus transcription of the first gene would automatically repress translation of the second. On the basis of a bioinformatics search, we suggest a possible example, namely, that pro-apoptosis gene PAR4 might inhibit anti-apoptosis gene XIAP. Part of an intron from PAR4 folds to form a large, stable hairpin, and reverse complement of the hairpin stem (with approximately 280 nucleotides) matches a sub-sequence of an exon in XIAP. This would be part of an efficient system to drive initiated apoptosis to its conclusion. Figuratively speaking, it replaces two control knobs with one. Since repeats, some with many thousands of copies, occur throughout the genome with complex distributions, care must be taken before asserting that the presence of any repeat in any gene is significant. Our apoptosis example involves repeats, so experimental verification is needed and planned. However, if it is found that the noncoding RNA by-product of one gene folds into a hairpin that is processed into an agent that inhibits a counteracting gene, then the same type of control unit might be found extensively among counteracting families of genes of many types.
Model-Based Dereverberation Preserving Binaural Cues

The ability of the human auditory system for sound localization mainly depends on the binaural cues, especially interaural time and level differences (ITD and ILD). In the context of digital hearing aids and binaural audio transmission systems, these cues can be severely degraded by independent bilateral signal processing such as dereverberation or noise reduction. This contribution presents a novel two-stage binaural dereverberation algorithm which explicitly preserves the binaural cues. The first stage is based on a statistical model of the room impulse responses (RIR) and comprises a spectral subtraction rule which reduces late reverberation only. It includes a smoothing process of the spectral gains to reduce musical tones. In a second stage, the residual reverberation is attenuated by a dual-channel Wiener filter. This is derived from a coherence model of the reverberant sound field taking into account shadowing effects of the head. The overall binaural-input binaural-output structure efficiently reduces both early and late reverberation. In experiments as well as informal listening tests using measured binaural room impulse responses, the proposed algorithm significantly improves speech quality according to objective and subjective measures.
Evaluating Affirmative Action in American Law Schools: Does Attending a Better Law School Cause Black Students to Fail the Bar?

In the current issue of the Stanford Law Review, Richard H. Sander has published an already widely-acclaimed study of affirmative action at American law schools. The article proclaims boldly, and contrary to received wisdom, that affirmative action hurts black lawyers by causing them to fail the bar. I show that this is incorrect. Sander’s conclusions are based on internally inconsistent (and statistically invalid) estimates of the causal effect of attending a higher-tier school. Correcting his assumptions and testing his hypothesis directly shows that for similarly qualified blacks, attending a higher-tier law school has no detectable effecton bar passage rates.
Intellectual Capital and Financial Performance of Banks in Sialkot Pakistan

The main aim of this research is to determine the impact of intellectual capital on the financial performance of banking sector operating in Sialkot Pakistan. Integrated intellectual capital model (IICM) was used to achieve the objective of the study. Structured questionnaire survey forms were used to collect the data from the targeted respondents. Purposive sampling technique was used to choose the targeted sample. A total of 210 out of 250 completed and usable questionnaires survey were returned. Six research hypotheses were constructed to address the objectives of this study. Smart PLS 3.0 was used to test the proposed research hypotheses. The findings showed that the three hypotheses were supported while remaining were not. This study will provide a clear understanding about the concept and applications of intellectual capital in banking sector. This study will be a milestone for the potential researchers to conduct their studies in Pakistani context.
Non-negative matrix factorization for single-channel EEG artifact rejection

New applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work we target applications where the EEG is to be captured by a single electrode and a number of additional lightweight sensors are allowed. Thus, this paper introduces a new method for artifact removal for single-channel EEG recordings using nonnegative matrix factorisation (NMF) in a Gaussian source separation framework. We focus the study on ocular artifacts and show that by properly exploiting prior information on the latter, through the analysis of electrooculographic recordings, our artifact removal results on single-channel EEG are comparable to the results obtained with the classic multi-channel Independent Component Analysis technique.
A new form of DOS attack in a cloud and its avoidance mechanism

Data center networks are typically grossly under-provisioned. This is not a problem in a corporate data center, but it could be a problem in a shared infrastructure, such as a co-location facility or a cloud infrastructure. If an application is deployed in such an infrastructure, the application owners need to take into account the infrastructure limitations. They need to build in counter-measures to ensure that the application is secure and it meets its performance requirements. In this paper, we describe a new form of DOS attack, which exploits the network under-provisioning in a cloud infrastructure. We have verified that such an attack could be carried out in practice in one cloud infrastructure. We also describe a mechanism to detect and avoid this new form of attack.
Numerical Analysis of Wave Function Controlled by OLTP and Harmonic Oscillator in BEC Experiments

This paper will focus on numerical analysis of wave function under the action of optical lattice trapping potential (OLTP) applied along the direction of propagation together with a modified harmonic oscillator potential. This process is usually uesed in experiments that lead to form Bose-Einstein condensation BEC in ultra cold gases. A Crank-Nicolson scheme is employed for solve the Gross-Pitaviskii equation with specials care has been taken to nonlinearity term, time interval, and space steps. The results shows that the behavior of wave function is responsive to a verity of parameters such as the frequencies ratio of trapping oscillator, Interfering of optical laser beams, chemical potential and the energy.
Oxaliplatin-Induced Pulmonary Toxicity in Gastrointestinal Malignancies: Two Case Reports and Review of the Literature

Oxaliplatin is a common chemotherapy drug, used mainly for colon and gastric cancer. Most common side effects are peripheral sensory neuropathy, hematological toxicity, and allergic reactions. A less common side effect is pulmonary toxicity, characterized mainly by interstitial pneumonitis. The incidence of this side effect is unknown, but the toxicity can be fatal. Twenty-six cases of pulmonary toxicity have been described in the literature, seven in the setting of adjuvant treatment. We describe two fatal cases of pulmonary injury related to oxaliplatin and a review of the literature.
A 100GHz phase-locked loop in 0.13µm SiGe BiCMOS process

A fully integrated 100GHz phase-locked loop (PLL) is demonstrated in 0. 13µm SiGe BiCMOS process. The PLL employs a fundamental-frequency differential Colpitts voltage-controlled oscillator (VCO) with 8.3% tuning range, which achieves a phase noise of −124.5dBc/Hz at 10MHz offset, and a single-ended output power of 3dBm. The FoM of this VCO is the best among 90–100GHz VCOs. A Miller divider, operating from 50GHz up to 130GHz, is designed and the Gilbert-mixer phase detector is used to attenuate reference spurs. The total lock range of the PLL is from 92.7 to 100.2GHz, the phase noise is −102dBc/Hz at 1MHz offset, and reference spurs are not observable. The PLL dissipates 570mW and occupies 1.21mm2.
NEIGHBOUR DISCOVERY IN MULTI-RECEIVER COGNITIVE RADIO NETWORKS

A cognitive radio (CR) node is a radio device capable of operating (transmitting and receiving) over multiple channels. As a result, a network consisting of one or more CR nodes can adapt to varying channel availability in its geographical region by dynamically changing the channel (or channels) that nodes use for communication. We investigate the problem of neighbour discovery in a network consisting of one or more CR nodes when nodes have multiple receivers but only a single transmitter. Neighbour discovery, in turn, can be used to solve other important communication problems such as broadcasting and gossiping in an efficient manner. We present a TDMA-based deterministic distributed algorithm for neighbour discovery whose time-complexity is M ⌈ N r ⌉ +O(max(M,N) log r), where M is the maximum number of channels on which a node can operate, N denotes the size of the space used to assign identifiers to nodes, and r is the number of receivers at a node (with 1≤ r≤ min(M,N)).
A tobacco matrix attachment region reduces the loss of transgene expression in the progeny of transgenic tobacco plants

Summary The RB7 matrix attachment region (MAR), when flanking a uidA (GUS) reporter gene, has been previously shown to increase uidA gene expression by 60-fold in stably transformed tobacco suspension cell lines. We have now used the same co-transformation procedure to determine the effect of flanking MARs on uidA gene expression in tobacco plants. The neomycin phosphotransferase selection gene and uidA reporter gene on separate plasmids were co-transformed into seedlings by microprojectile bombardment. In primary transgenic plants, the average uidA expression in plants with MARs was twofold greater than in control plants without MARs, but there was no effect on variation of expression. GUS activity was not proportional to the number of integrated uidA transgenes over the entire range of copy numbers. However, in the lower part of the copy number range, MAR lines show a tendency for expression to increase with copy number. Transgene expression in backcross progenies of the MARcontaining lines averaged threefold higher than in control progenies. MARs also reduced the loss of transgene expression in the BC 1 generation. Sixty-three per cent of the 21 MAR-containing primary transformants, but only 20% of the 14 control primary transformants, produced backcross progenies in which no loss of transgene expression was observed. These observations are discussed in the context of homology-dependent gene silencing.
Image annotation with tagprop on the MIRFLICKR set

Image annotation is an important computer vision problem where the goal is to determine the relevance of annotation terms for images. Image annotation has two main applications: (i) proposing a list of relevant terms to users that want to assign indexing terms to images, and (ii) supporting keyword based search for images without indexing terms, using the relevance estimates to rank images. In this paper we present TagProp, a weighted nearest neighbour model that predicts the term relevance of images by taking a weighted sum of the annotations of the visually most similar images in an annotated training set. TagProp can use a collection of distance measures capturing different aspects of image content, such as local shape descriptors, and global colour histograms. It automatically finds the optimal combination of distances to define the visual neighbours of images that are most useful for annotation prediction. TagProp compensates for the varying frequencies of annotation terms using a term-specific sigmoid to scale the weighted nearest neighbour tag predictions. We evaluate different variants of TagProp with experiments on the MIR Flickr set, and compare with an approach that learns a separate SVM classifier for each annotation term. We also consider using Flickr tags to train our models, both as additional features and as training labels. We find the SVMs to work better when learning from the manual annotations, but TagProp to work better when learning from the Flickr tags. We also find that using the Flickr tags as a feature can significantly improve the performance of SVMs learned from manual annotations.
End-User Service Computing: Spreadsheets as a Service Composition Tool

In this paper, we show how spreadsheets, an end-user development paradigm proven to be highly productive and simple to learn and use, can be used for complex service compositions. We identify the requirements for spreadsheet-based service composition, and present our framework that implements these requirements. Our framework enables spreadsheets to send requests and retrieve results from various local and remote services. We show how our tools support different composition patterns, and how the style of declarative dependencies of spreadsheets can facilitate service composition. We also discuss novel issues identified by using the framework in several projects and education.
Tracky Notes: Trackable Sticky Notes for Indexing a Video of Meetings

Video records are sometimes used during meetings nowadays in order to compensate for the incompleteness of hand-written minutes. To use video effectively to record the minutes of a meeting, indices are considered necessary in addition to standard functions of movie players. Some systems can form indices by sensing human actions in the world, but such indices are not directly relevant to the contents of the discussions that occur in meetings. In this study, we propose an automated meeting index system based on people's discussions during daily meetings in the world. In our system, indices based on the contents of discussion can be automatically formulated in meetings if only paper, writing implements, and a camera are arranged in a certain manner. To this end, we develop trackable sticky notes, called "Tracky Notes," as indices. Tracky Notes can be tracked with visible markers through a camera, and is robust against occlusion. We then propose a meeting viewer where Tracky Notes are used as indices. Finally, we report the result of a user study conducted in a university classroom. The study revealed the appropriate region of visible markers and the robustness against occlusion of Tracky Notes. We also measured the effectiveness of our system comparing with standard functions of movie players.
Rank-1 Tensor Approximation Methods and Application to Deflation

Because of the attractiveness of the canonical polyadic (CP) tensor decomposition in various applications, several algorithms have been designed to compute it, but efficient ones are still lacking. Iterative deflation algorithms based on successive rank-1 approximations can be used to perform this task, since the latter are rather easy to compute. We first present an algebraic rank-1 approximation method that performs better than the standard higher-order singular value decomposition (HOSVD) for three-way tensors. Second, we propose a new iterative rank-1 approximation algorithm that improves any other rank-1 approximation method. Third, we describe a geometric framework allowing to study the convergence of deflation CP decomposition (DCPD) algorithms based on successive rank-1 approximations. A set of computer experiments then validates theoretical results and demonstrates the efficiency of DCPD algorithms compared to other ones.