nils-herrmann/clean_scirepeval huggingface dataset:
Bone marrow lesions detected by specific combination of MRI sequences are associated with severity of osteochondral degeneration
Venue: Arthritis Research Therapy

BackgroundBone marrow lesions (BMLs) are useful diagnostic and prognostic markers in knee osteoarthritis (OA) but what they represent at the tissue level remains unclear. The aim of this study was to provide comprehensive tissue characterization of BMLs detected using two specific MRI sequences.MethodsTibial plateaus were obtained from 60 patients (29 females, 31 males) undergoing knee arthroplasty for OA. To identify BMLs, MRI was performed ex vivo using T1 and PDFS weighted sequences. Multi modal tissue level analyses of the osteochondral unit (OCU) were performed, including cartilage volume measurement, OARSI grading, micro CT analysis of bone microstructure, routine histopathological assessment and quantitation of bone turnover indices.ResultsBMLs were detected in 74 of tibial plateaus, the remainder comprising a No BML group. Of all BMLs, 59 were designated BML 1 (detected only by PDFS) and 41 were designated BML 2 (detected by both PDFS T1) The presence of a BML was related to degeneration of the OCU, particularly within BML 2. When compared to No BML, BML 2 showed reduced cartilage volume (p 0.008) higher OARSI scores (p 0.004) thicker subchondral plate (p 0.002) increased trabecular bone volume and plate like structure (p 0.0004) increased osteoid volume (p 0.002) and thickness (p 0.003) more bone marrow oedema (p 0.03) fibrosis (p 0.002) necrosis (p 0.01) and fibrovascular cysts (p 0.04) For most measures, BML 1 was intermediate between No BML and BML 2.ConclusionsBMLs detected by specific MRI sequences identify different degrees of degeneration in the OCU. This suggests that MRI characteristics of BMLs may enable identification of different BML phenotypes and help target novel approaches to treatment and prevention of OA.
Time series k means: A new k means type smooth subspace clustering for time series data
Venue: Inf. Sci.

Existing clustering algorithms are weak in extracting smooth subspaces for clustering time series data. In this paper, we propose a new k means type smooth subspace clustering algorithm named Time Series k means (TSkmeans) for clustering time series data. The proposed TSkmeans algorithm can effectively exploit inherent subspace information of a time series data set to enhance clustering performance. More specifically, the smooth subspaces are represented by weighted time stamps which indicate the relative discriminative power of these time stamps for clustering objects. The main contributions of our work include the design of a new objective function to guide the clustering of time series data and the development of novel updating rules for iterative cluster searching with respect to smooth subspaces. Based on a synthetic data set and five real life data sets, our experimental results confirm that the proposed TSkmeans algorithm outperforms other state of the art time series clustering algorithms in terms of common performance metrics such as Accuracy, Fscore, RandIndex, and Normal Mutual Information.
A new clustering method using wavelet based probability density functions for identifying patterns in time series data
Venue: 2016 IEEE EMBS International Student Conference (ISC)

Clustering is a prominent method to identify similar patterns in large groups of data and can be beneficial in the bioinformatics studies due to this property. Classical methods such as k means and maximum likelihood consider a mixture of Gaussian probability density function (PDF) of data and find clusters based on maximizing the PDF. However, correlation among different groups of data and existence of noise on the data make it difficult to correctly detect the correct number of clusters. Furthermore, the assumption of the Gaussian distance for the PDF is not necessarily true in real applications. This paper presents a new clustering method via wavelet based probability density functions. For this purpose, first, a mixture of PDFs is estimated by the wavelet for each feature. After this, a multilevel thresholding method is implemented on the mixture of PDFs of each feature to obtain the clusters. Finally, a forward feature selection with memory is used to cluster the dataset based on combinations of the features. The profile alignment and agglomerative clustering (PAAC) index is applied for evaluating the number of clusters and features. Transcript expression throughout the various stages of prostate cancer is considered as a case study to identify patterns. The experimental results show the ability of the proposed method in detecting patterns of similar transcripts throughout disease progression. The results are promising in comparison with the other methods.
The status and developments of leather solid waste treatment: A mini review
Venue: Waste management research the journal of the International Solid Wastes and Public Cleansing Association, ISWA

Leather making is one of the most widespread industries in the world. The production of leather goods generates different types of solid wastes and wastewater. These wastes will pollute the environment and threat the health of human beings if they are not well treated. Consequently, the treatment of pollution caused by the wastes from leather tanning is really important. In comparison with the disposal of leather wastewater, the treatment of leather solid wastes is more intractable. Hence, the treatment of leather solid wastes needs more innovations. To keep up with the rapid development of the modern leather industry, various innovative techniques have been newly developed. In this mini review article, the major achievements in the treatment of leather solid wastes are highlighted. Emphasis will be placed on the treatment of chromium tanned solid wastes; some new approaches are also discussed. We hope that this mini review can provide some valuable information to promote the broad understanding and effective treatment of leather solid wastes in the leather industry.
Convergence Analysis for Rectangular Matrix Completion Using Burer Monteiro Factorization and Gradient Descent
Venue: ArXiv

We address the rectangular matrix completion problem by lifting the unknown matrix to a positive semidefinite matrix in higher dimension, and optimizing a nonconvex objective over the semidefinite factor using a simple gradient descent scheme. With $O( \mu r^2 \kappa^2 n \max(\mu, \log n) random observations of a $n_1 \times n_2$ \mu$ incoherent matrix of rank $r$ and condition number \kappa$ where $n \max(n_1, n_2) the algorithm linearly converges to the global optimum with high probability.
Milk Derived Antimicrobial Bioactive Peptides: A Review

In recent decades, bioactive peptides have attracted increasing interest as health promoting functional foods. A variety of naturally formed bioactive peptides have been found in fermented dairy products such as yogurt, sour milk, and cheese. Initially these peptides are inactive within the sequence of the parent protein molecule and can be generated by gastrointestinal digestion of milk, fermentation of milk with proteolytic starter cultures, and/or hydrolysis by proteolytic enzymes. Milk derived peptides exert a number of health beneficial activities, even upon oral administration. Bioactive peptides have a great impact on major body systems including the digestive, nervous, endocrine, cardiovascular, diabetes type II, obesity, and immune systems. Antimicrobial peptides are also an important ingredient of innate immunity, especially at mucosal surfaces such as lungs and small intestine that are constantly exposed to a range of potential pathogens. Therefore, it plays an important role in boosting natural immune protection by reducing the risk of chronic diseases. Bioactive peptides are considered as potent drugs with well defined pharmacological residues and also used to formulate health enhancing nutraceuticals.
Multi key FHE from LWE, Revisited
Venue: TCC

Traditional fully homomorphic encryption FHE schemes only allow computation on data encrypted under a single key. Lopez Alt, Tromer, and Vaikuntanathan STOC 2012 proposed the notion of multi key FHE, which allows homomorphic computation on ciphertexts encrypted under different keys, and also gave a construction based on a somewhat nonstandard assumption related to NTRU. More recently, Clear and McGoldrick CRYPTO 2015, followed by Mukherjee and Wichs EUROCRYPT 2016, proposed a multi key FHE that builds upon the LWE based FHE of Gentry, Sahai, and Waters CRYPTO 2013. However, unlike the original construction of Lopez Alt et al. these later LWE based schemes have the somewhat undesirable property of being "single hop for keys:" all relevant keys must be known at the start of the homomorphic computation, and the output cannot be usefully combined with ciphertexts encrypted under other keys unless an expensive "bootstrapping" step is performed. In this work we construct two multi key FHE schemes, based on LWE assumptions, which are multi hop for keys: the output of a homomorphic computation on ciphertexts encrypted under a set of keys can be used in further homomorphic computation involving additional keys, and so on. Moreover, incorporating ciphertexts associated with new keys is a relatively efficient "native" operation akini 3/4 ?to homomorphic multiplication, and does not require bootstrapping in contrast with all other LWE based solutions. Our systems also have smaller ciphertexts than the previous LWE based ones; in fact, ciphertexts in our second construction are simply GSW ciphertexts with no auxiliary data.
Two Round Multiparty Computation via Multi key FHE
Venue: EUROCRYPT

We construct a general multiparty computation MPC protocol with only two rounds of interaction in the common random string model, which is known to be optimal. In the honest but curious setting we only rely on the learning with errors LWE assumption, and in the fully malicious setting we additionally assume the existence of non interactive zero knowledge arguments NIZKs. Previously, Asharov et al. EUROCRYPT '12 showed how to achieve three rounds based on LWE and NIZKs, while Garg et al. TCC '14 showed how to achieve the optimal two rounds based on indistinguishability obfuscation, but it was unknown if two rounds were possible under standard assumptions without obfuscation. Our approach relies on multi key fully homomorphic encryption MFHE, introduced by Lopez Alt et al. STOC '12, which enables homomorphic computation over data encrypted under different keys. We present a construction of MFHE based on LWE that significantly simplifies a recent scheme of Clear and McGoldrick CRYPTO '15. We then extend this construction to allow for a one round distributed decryption of a multi key ciphertext. Our entire MPC protocol consists of the following two rounds:1.Each party individually encrypts its input under its own key and broadcasts the ciphertext. All parties can then homomorphically compute a multi key encryption of the output.2.Each party broadcasts a partial decryption of the output using its secret key. The partial decryptions can be combined to recover the output in plaintext.
Lattice Based Fully Dynamic Multi key FHE with Short Ciphertexts
Venue: CRYPTO

We present a multi key fully homomorphic encryption scheme that supports an unbounded number of homomorphic operations for an unbounded number of parties. Namely, it allows to perform arbitrarily many computational steps on inputs encrypted by an a priori unbounded polynomial number of parties. Inputs from new parties can be introduced into the computation dynamically, so the final set of parties needs not be known ahead of time. Furthermore, the length of the ciphertexts, as well as the space complexity of an atomic homomorphic operation, grow only linearly with the current number of parties. Prior works either supported only an a priori bounded number of parties Lopez Alt, Tromer and Vaikuntanthan, STOC '12, or only supported single hop evaluation where all inputs need to be known before the computation starts Clear and McGoldrick, Crypto '15, Mukherjee and Wichs, Eurocrypt '16. In all aforementioned works, the ciphertext length grew at least quadratically with the number of parties. Technically, our starting point is the LWE based approach of previous works. Our result is achieved via a careful use of Gentry's bootstrapping technique, tailored to the specific scheme. Our hardness assumption is that the scheme of Mukherjee and Wichs is circular secure and thus bootstrappable. A leveled scheme can be achieved under standard LWE.
Towards a social practice theory of paradox

This paper explores the shared ontological basis of the paradox and practices perspectives. Advancing the emerging 'practice turn' in paradox, we outline the practice theoretical approach to studying paradox, articulating four main principles that define its research agenda. We describe each theoretical principle, explain its implications for the way we understand and study paradox, and illustrate it with an example of existing work. Next, we use these principles to reflect on the potential of a practice based view of paradox, highlighting avenues for future research. Herein we review, integrate and develop a foundation for practice based studies of paradox.
Dopamine selectively remediates 'model based' reward learning: a computational approach.
Venue: Brain a journal of neurology

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping in of stimulus response associations, hypothesized to arise computationally from 'model free' learning. The other, 'model based' learning, involves learning a model of the world that is believed to support goal directed behaviour. Much work has pointed to a role for dopamine in model free learning. But recent work suggests model based learning may also involve dopamine modulation, raising the possibility that model based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two step reward learning task which dissociates model free versus model based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment.
Sketch based image retrieval via Siamese convolutional neural network
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

Sketch based image retrieval (SBIR) is a challenging task due to the ambiguity inherent in sketches when compared with photos. In this paper, we propose a novel convolutional neural network based on Siamese network for SBIR. The main idea is to pull output feature vectors closer for input sketch image pairs that are labeled as similar, and push them away if irrelevant. This is achieved by jointly tuning two convolutional neural networks which linked by one loss function. Experimental results on Flickr15K demonstrate that the proposed method offers a better performance when compared with several state of the art approaches.
The Real Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri Rao Product
Venue: Sensors

There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real valued sparse DOA estimation algorithm based on the Khatri Rao (KR) product called the L1 RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real valued matrix via a unitary transformation so that a real valued sparse model is achieved. The real valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product's property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV) The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.
An efficient off grid DOA estimation approach for nested array signal processing by using sparse Bayesian learning strategies
Venue: Signal Process.

In this paper, we address the problem of sparsity inducing direction of arrival (DOA) estimation in the context of array covariance vectors when a recently proposed nested array structure is utilized. The proposed approach is based on a Sparse Bayesian learning (SBL) perspective while the off grid effects inherent in traditional sparse recovery algorithms are taken into account simultaneously. Specifically, the notable advantages of the developed approach can be attributed to two aspects. First, through a linear transformation, the unknown noise variance can be eliminated effectively and the estimation error between the actual and sample covariance matrix can be normalized to an identity matrix, thereby facilitating a noise free sparse representation. Second, a computationally tractable polynomial rooting procedure is introduced to calculate the discretized sampling grid error one by one based on the reconstructed spatial power spectrum, thus this novel algorithm can maintain high estimation accuracy even under a coarse sampling grid. Extensive simulation results finally demonstrate the superiority of the proposed approach in terms of DOA estimation precision and computational efficiency over off the shelves techniques. A sparsity inducing DOA estimation method applied in nested array is proposed.The proposed method is based on a Sparse Bayesian learning perspective.The off grid effects inherent in sparse recovery framework are taken into account.The unknown noise variance can be eliminated easily.The off grid distances can be calculated by using a polynomial rooting procedure.
DOA estimation via sparse recovering from the smoothed covariance vector

A direction of arrival(DOA) estimation algorithm is proposed using the concept of sparse representation. In particular, a new sparse signal representation model called the smoothed covariance vector(SCV) is established, which is constructed using the lower left diagonals of the covariance matrix. DOA estimation is then achieved from the SCV by sparse recovering, where two distinguished error limit estimation methods of the constrained optimization are proposed to make the algorithms more robust. The algorithm shows robust performance on DOA estimation in a uniform array, especially for coherent signals. Furthermore, it significantly reduces the computational load compared with those algorithms based on multiple measurement vectors(MMVs) Simulation results validate the effectiveness and efficiency of the proposed algorithm.
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. These approaches have been extensively tested on such unconstrained datasets, on the Labeled Faces in the Wild and YouTube Faces, to name a few. However, their capability to handle individual appearance variations caused by factors such as head pose, illumination, occlusion, and misalignment has not been thoroughly assessed till now. In this paper, we present a comprehensive study to evaluate the performance of deep learning based face representation under several conditions including the varying head pose angles, upper and lower face occlusion, changing illumination of different strengths, and misalignment due to erroneous facial feature localization. Two successful and publicly available deep learning models, namely VGG Face and Lightened CNN have been utilized to extract face representations. The obtained results show that although deep learning provides a powerful representation for face recognition, it can still benefit from preprocessing, for example, for pose and illumination normalization in order to achieve better performance under various conditions. Particularly, if these variations are not included in the dataset used to train the deep learning model, the role of preprocessing becomes more crucial. Experimental results also show that deep learning based representation is robust to misalignment and can tolerate facial feature localization errors up to 10% of the interocular distance.
Transferring deep representation for NIR VIS heterogeneous face recognition
Venue: 2016 International Conference on Biometrics (ICB)

One task of heterogeneous face recognition is to match a near infrared (NIR) face image to a visible light (VIS) image. In practice, there are often a few pairwise NIR VIS face images but it is easy to collect lots of VIS face images. Therefore, how to use these unpaired VIS images to improve the NIR VIS recognition accuracy is an ongoing issue. This paper presents a deep TransfeR NIR VIS heterogeneous facE recognition neTwork (TRIVET) for NIR VIS face recognition. First, to utilize large numbers of unpaired VIS face images, we employ the deep convolutional neural network (CNN) with ordinal measures to learn discriminative models. The ordinal activation function (Max Feature Map) is used to select discriminative features and make the models robust and lighten. Second, we transfer these models to NIR VIS domain by fine tuning with two types of NIR VIS triplet loss. The triplet loss not only reduces intra class NIR VIS variations but also augments the number of positive training sample pairs. It makes fine tuning deep models on a small dataset possible. The proposed method achieves state of the art recognition performance on the most challenging CASIA NIR VIS 2.0 Face Database. It achieves a new record on rank 1 accuracy of 95.74% and verification rate of 91.03% at FAR=0.001. It cuts the error rate in comparison with the best accuracy [27] by 69%