Miracle-dz/UniFAR-FTUs huggingface dataset:
Knot Floer homology of Whitehead doubles

In this paper we study the knot Floer homology invariants of the twisted and untwisted Whitehead doubles of an arbitrary knot, K . A formula is presented for the filtered chain homotopy type of b HFK.D . K;t// in terms of the invariants for K , where D . K;t/ denotes the t ‐twisted positive (resp. negative)-clasped Whitehead double of K . In particular, the formula can be used iteratively and can be used to compute the Floer homology of manifolds obtained by surgery on Whitehead doubles. An immediate corollary is that . DC.K;t//D1 if t 0 are not smoothly slice. Another corollary is a closed formula for the Floer homology of the three-manifold obtained by gluing the complement of an arbitrary knot, K , to the complement of the trefoil.
CS224W: Project Writeup

Network Alignment is the problem of uncovering the relationship that exists in the nodes from two graphs which model the same phenomenon. Simply put, given two graphs G = (V,E) and G′ = (V ′, E′), the goal is to find a one-to-one matching f : V → V ′ such that for v ∈ V , v′ ∈ V ′, f(v) = v′ iff. v and v′ represent the same entity. Common interesting applications of the Network Alignment problem include the discovery of similarity in the ways protein interact in different species (a matching is found between protein-interaction networks from different species, see [Berg and Lassig, 2004]), or de-anonymizing social networks (by matching an anonymized social network with another social network for which the identities are public, see [Narayanan and Shmatikov, 2009]). Another implication is the field of ontology matching, where one tries to build one large ontology given several overlapping ontologies. Each ontology is modeled by a graph of relationships between concepts, and thus the problem is in fact equivalent to graph alignment. In this paper, I first give an overview of the previous work in Network Alignment. Then I explain the reasons that led me to create a new dataset to benchmark Network Alignment algorithms, and the properties of the dataset I have created. I then describe my main contribution, which is a scalable algorithm that outperforms previous work in the domain. The last section gives a detailed overview of the results obtained, and explores the reasons that caused this performance.
Design And Implementation of Reconfigurable Rijndael Encryption Algorithms For Reconfigurable Mobile Terminals

In any wireless communication security is crucial during data transmission. The encryption and decryption of data is the major role in the wireless communication for security of the data. Encryption algorithms are used to ensure the security in the transmission channels. Similarly the area and the power consumption is another major thing to be viewed since most of the mobile terminals are battery operated. So a mobile terminal which has an encryption unit with less area and power consumption is appreciated. This paper deals with the Advanced Encryption Standard (AES) which works on a 128 bit data encrypting it with 128,192,256 bits of keys (ciphers) in a single hardware unit.
Coboost learning of visual categories with 1st and 2nd order features from Google images

Conventional object recognition techniques rely heavily on manually annotated image datasets to achieve good performances. However, collecting high quality datasets is really laborious. In this paper, we propose a semi-supervised framework for learning visual categories from Google Images. The 1st and 2nd order features, which define bag of words representation and spatial relationship between local features respectively, make up an independent and redundant feature split. We then integrate a cotraining algorithm CoBoost with these two features. We create two boosting classifiers based on the 1st and 2nd order features respectively in the training, during which one classifier provides labels for the other. Besides, the 2nd order features are generated dynamically rather than extracted exhaustively to avoid high computation. An active learning technique is also introduced to further improve the performance. We evaluate our method on the benchmark datasets, showing results competitive with the state-of-the-art unsupervised approaches and some supervised techniques.
Detecting Functional Groups of Arabidopsis Mutants by Metabolic Profiling and Evaluation of Pleiotropic Responses

Metabolic profiles and fingerprints of Arabidopsis thaliana plants with various defects in plastidic sugar metabolism or photosynthesis were analyzed to elucidate if the genetic mutations can be traced by comparing their metabolic status. Using a platform of chromatographic and spectrometric tools data from untargeted full MS scans as well as from selected metabolites including major carbohydrates, phosphorylated intermediates, carboxylates, free amino acids, major antioxidants, and plastidic pigments were evaluated. Our key observations are that by multivariate statistical analysis each mutant can be separated by a unique metabolic signature. Closely related mutants come close. Thus metabolic profiles of sugar mutants are different but more similar than those of photosynthesis mutants. All mutants show pleiotropic responses mirrored in their metabolic status. These pleiotropic responses are typical and can be used for separating and grouping of the mutants. Our findings show that metabolite fingerprints can be taken to classify mutants and hence may be used to sort genes into functional groups.
Vision and RFID-based person tracking in crowds from a mobile robot

This paper deals with the tracking of persons in a human cluttered environment. It is performed by an active perception system, consisting of a camera mounted on a pan-tilt unit and a 360° RFID detection system which are embedded on a mobile robot. Particle filters enable the fusion of heterogeneous data into the proposal distribution from which the particles are sampled. The information provided by the tracker is then used to build sensor-based dedicated control laws in order to make the robot follow the RFID tagged person. Finally, experiments on our mobile robot are presented in order to highlight the relevance and complementarity of the developed perceptual functions.
A Hybrid B+-tree as Solution for In-Memory Indexing on CPU-GPU Heterogeneous Computing Platforms

An in-memory indexing tree is a critical component of many databases. Modern many-core processors, such as GPUs, are offering tremendous amounts of computing power making them an attractive choice for accelerating indexing. However, the memory available to the accelerating co-processor is rather limited and expensive in comparison to the memory available to the CPU. This drawback is a barrier to exploit the computing power of co-processors for arbitrarily large index trees. In this paper, we propose a novel design for a B+-tree based on the heterogeneous computing platform and the hybrid memory architecture found in GPUs. We propose a hybrid CPU-GPU B+-tree, "HB+-tree," which targets high search throughput use cases. Unique to our design is the joint and simultaneous use of computing and memory resources of CPU-GPU systems. Our experiments show that our HB+-tree can perform up to 240 million index queries per second, which is 2.4X higher than our CPU-optimized solution.
A novel SoC design methodology combining adaptive software and reconfigurable hardware

Reconfigurable hardware is becoming a prominent component in a large variety of SoC designs. Reconfigurability allows for efficient hardware acceleration and virtually unlimited adaptability. On the other hand, overheads associated with reconfiguration and interfaces with the software component need to be evaluated carefully during the exploration phase. The aim of this paper is to identify the best trade-off considering application-specific features in software, which can lend itself to software-based acceleration and lead to a revision of the view that certain computationally intensive tasks can only be accelerated through hardware. In order to validate the effectiveness of our proposed techniques, we built an extensive development and experimental setup, bringing together the MLTon-based programming environment and physical mapping of the software and hardware onto a real dynamically reconfigurable SoC system.
Using multimodal analysis to unravel a ‘silent’ child’s learning

Although the English Foundation Stage Curriculum for children aged 3 to 5 years recognises that children learn through talk and play and through ‘movement and all their senses’ (DfEE & QCA, 2000: 20), there is comparatively little theoretical understanding of how children learn through diverse ‘modes’, such as body movement, facial expression, gaze, the manipulation of objects and talk, and there is little practical guidance on how practitioners can support children’s ‘multimodal’ learning. Indeed, mounting research evidence indicates that since the introduction of a national early years curriculum and early years assessment schemes, practitioners have felt under increased pressure to focus on children’s verbal skills in order to provide evidence of children’s literacy and numeracy skills in preparation for primary education (see Flewitt, 2005a & 2005b). In the context of these changes, this article relates the story of Tallulah, a 3-year-old girl with a late July birthday, who, like many summer-born children in England, spent one year in an early years setting before moving to primary school aged just 4 years. The article draws on data collected as part of an ESRC-funded study that explored the different ‘modes’ young children use to make and express meaning in the different social settings of home and a preschool playgroup (Flewitt, 2003). Examples are given of how Tallulah communicated her understandings at home through skilful combinations of talk, gaze direction, body movement and facial expression, and how others in the home supported Tallulah’s learning. These are then compared with examples of how Tallulah communicated in playgroup, primarily by combining the silent modes of gaze, body movement and facial expression. The article identifies how the different social settings of home and preschool impacted upon her choices and uses of different expressive modes.
AMPA Receptor Synaptic Targeting Regulated by Stargazin Interactions with the Golgi-Resident PDZ Protein nPIST

Regulation of AMPA receptors (AMPARs) at synapses plays a critical role in alterations of synaptic strength in the brain. Stargazin, an AMPAR-interacting protein, is critical for clustering and regulation of synaptic AMPARs. Stargazin interacts with AMPARs via its extracellular domain and with PDZ [postsynaptic density-95 (PSD-95)/Discs large (Dlg)/zona occludens-1 (ZO-1)] proteins via its C-terminal PDZ-binding motif, and these interactions are necessary for stargazin and AMPAR synaptic targeting. By studying the expression of stargazin mutant constructs in cultured hippocampal neurons, we identified a novel domain corresponding to residues 243-283 within the cytoplasmic C terminus of stargazin that is also required for stargazin and AMPAR synaptic clustering. To identify proteins that interact with this stargazin synaptic clustering domain, we performed a yeast two-hybrid assay and found that this stargazin domain binds to nPIST (neuronal isoform of protein-interacting specifically with TC10), a Golgi-enriched protein implicated in trafficking of transmembrane proteins. Using in situ hybridization, immunohistochemistry, coimmunoprecipitation studies, and biochemical fractionation, we found that stargazin and nPIST colocalize and interact in the brain. Finally, by studying AMPAR clustering in transfected hippocampal neurons, we found that overexpression of nPIST enhances AMPAR synaptic clustering, whereas transfection of a dominant-negative nPIST construct attenuates AMPAR synaptic clustering. These studies identify a novel stargazin domain necessary for synaptic clustering of AMPARs and suggest that nPIST and stargazin interactions play a critical role in AMPAR trafficking to the synapse.
Application of improved genetic algorithm on IIR filter optimization

This paper presents an improved GA which modified the GA based on allele gene adaptive mutation of mutation and crossover operation. There are three modified strategies to improve the performance of GA, elitist strategy is adopted to speed up convergence rate; the crossover operation is modified for effective searching; and the allele gene adaptive mutation exploits individuals' allele gene in the population to maintain an appropriate level of diversity. Finally, simulation results of test function of optimization problems and IIR filter design are shown to illustrate the effectiveness and performance of the proposed improved GA.
Effective Extraction of Gabor Features for False Positive Reduction and Mass Classification in Mammography

Digital mammography is considered to be the most effective imaging modality for early detection of breast cancer. Masses and microcalcifications are two early signs of breast cancer. For the detection of masses, segmentation of mammograms results in ROIs (regions of interest) which not only include masses but suspicious normal tissues as well, which lead to false positives. The problem is to reduce the false positives by classifying ROIs as masses and normal tissues. In addition, the detected masses are required to be classified as malignant and benign. We address these two problems using textural properties of masses. Gabor filter bank is used in a novel way to extract the most representative and discriminative textural properties of masses present at different orientations and scales. SVM with Gaussian kernel is employed for classification. The method is evaluated over 1024 (512 masses and 512 normal) ROIs extracted from DDSM database. Experiments have been performed with different parameter settings to find the best set of parameters. Gabor filter Banks with different choices of orientations (3, 5, 6, 8) and scales (2, 3, 4, 5) have been tested on 4 ROI resolutions (64×64, 128×128, 256×256, 512×512). For the first problem i.e. to classify ROIs as masses and normal tissues, the best result (Az = 0.96±0.02) is obtained when Gabor filter bank with 5 orientations and 3 scales and RIOs with size 512×512 is used. Gabor filter bank with 8 orientations and 5 scales on mass ROIs of size 128×128 gives the best result (Az = 0.87±0.05) for the second problem (i.e. to classify mass ROIs as benign and malignant). Comparison with state-of-the-art methods reveals that the proposed method performs better than the existing methods.
Therapeutic use of colchicine and its derivatives: a patent review

ABSTRACT Introduction: Colchicine, the major alkaloid extracted from the meadow saffron (Colchicum autumnale) is one of the most prominent natural products belonging to the class of organic compounds known as tropones. Colchicine’s medicinal properties have been long-known, but it has been attracting renewed attention due to its action as an antimitotic agent. Areas covered: This review presents an update and analysis of the patents claiming therapeutic activity of colchicine and its derivatives published in the last five years (2010–2015). Other patents claiming the application of colchicine in the areas of biotechnology, veterinary, and agriculture are therefore excluded from this review. Expert opinion: Rapid increase in the number of publications and patent filings in recent years for the significant therapeutic potential of colchicine, for several conditions beyond gout, clearly indicates the growing interest of research on this molecule. Although the molecular structure of colchicine fulfils the four Lipinski’s requisites for high oral bioavailability, the toxic side effects resulting from its fast metabolism need to be assessed. Further efforts are therefore required to explore the appropriate dosage and possible administration techniques of colchicine for practical use.
The Effective Stability of Adaptive Timestepping ODE Solvers

We consider the behavior of certain adaptive timestepping methods, based upon embedded explicit Runge--Kutta pairs, when applied to dissipative ODEs. It has been observed numerically that the standard local error controls can impart desirable stability properties, but this has been rigorously verified only for very special, low-order, Runge--Kutta pairs. The rooted-tree expansion of a certain quadratic form, central to the stability theory of Runge--Kutta methods, is derived. This, together with key assumptions on the sequence of accepted time-steps and the local error estimate, provides a general explanation for the observed stability of such algorithms on dissipative problems. Under these assumptions, which are expected to hold for "typical" numerical trajectories, two different results are proved. First, for a large class of embedded Runge--Kutta pairs of order $(1,2)$, controlled on an error-per-unit-step basis, all such numerical trajectories will eventually enter a particular bounded set. This occurs for sufficiently small tolerances independent of the initial conditions. Second, for pairs of arbitrary orders $(p-1,p)$, operating under either error-per-step or error-per-unit-step control, similar results are obtained when an additional structural assumption (that should be valid for many cases of interest) is imposed on the dissipative vector field. Numerical results support both the analysis and the assumptions made.
3D brain mapping using a deformable neuroanatomy

Presents two different mathematical methods that can be used separately or in conjunction to accommodate shape variabilities between normal human neuroanatomies. Both methods use a digitized textbook to represent the complex structure of a typical normal neuroanatomy. Probabilistic transformations on the textbook coordinate system are defined to accommodate shape differences between the textbook and images of other normal neuroanatomies. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second. Results presented in this paper demonstrate how a single deformable textbook can be used to accommodate normal shape variability.
Estimating a Function from Noisy Sensor Data: A Factor Graph Approach

The combination of graphical models and belief propagation algorithms has found wide acceptance in the design of communication systems. We extend the general framework of joint source-channel decoding on graphs to account for estimation problems in which the goal is not to decode the entire data set but to estimate a function of the transmitted data. This problem is deemed relevant e.g. in the context of wireless sensor networks.
A binarization algorithm specialized on document images and photos

In this paper, a new method for document images or photos binarization is presented. The method is simple, fast and robust and appropriate for normal as well as for special cases of documents like photos, historical documents etc. The proposed method is applied to problematic cases of documents and it is compared to other traditional methods.
Development of advanced Active Appearance Model for facial emotion recognition

We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.
Atypical E2fs Control Lymphangiogenesis through Transcriptional Regulation of Ccbe1 and Flt4

Lymphatic vessels are derived from venous endothelial cells and their formation is governed by the Vascular endothelial growth factor C (VegfC)/Vegf receptor 3 (Vegfr3; Flt4) signaling pathway. Recent studies show that Collagen and Calcium Binding EGF domains 1 protein (Ccbe1) enhances VegfC-dependent lymphangiogenesis. Both Ccbe1 and Flt4 have been shown to be indispensable for lymphangiogenesis. However, how these essential players are transcriptionally regulated remains poorly understood. In the case of angiogenesis, atypical E2fs (E2f7 and E2f8) however have been recently shown to function as transcriptional activators for VegfA. Using a genome-wide approach we here identified both CCBE1 and FLT4 as direct targets of atypical E2Fs. E2F7/8 directly bind and stimulate the CCBE1 promoter, while recruitment of E2F7/8 inhibits the FLT4 promoter. Importantly, inactivation of e2f7/8 in zebrafish impaired venous sprouting and lymphangiogenesis with reduced ccbe1 expression and increased flt4 expression. Remarkably, over-expression of e2f7/8 rescued Ccbe1- and Flt4-dependent lymphangiogenesis phenotypes. Together these results identified E2f7/8 as novel in vivo transcriptional regulators of Ccbe1 and Flt4, both essential genes for venous sprouting and lymphangiogenesis.
Evaluating error of homography

In this paper, an exact computation of the geometric error fo r h mography is derived. We assume the Gaussian noise model for the perturbation of image coordina tes and formulate the problem as a least squares minimization. This paper shows how to compute accur te geometric error through solving a polynomial of degree eight. This approach avoids falling in to local minima that may occur when iterative methods are used. An application where this method can impro ve accuracy is discussed. Experiments comparing the geometric error with its approximation by the Sampson error are presented.
Differential requirement for MuSK and dystroglycan in generating patterns of neuromuscular innervation

Vertebrates display diverse patterns of neuromuscular innervation, but little is known about how such diversity is generated. In mammals, neuromuscular junctions form predominantly at equatorial locations, giving rise to a focal innervation pattern along a central endplate band. In addition, vertebrate striated muscles exhibit two nonfocal neuromuscular patterns, myoseptal and distributed innervation. Although agrin-MuSK-rapsyn signaling is essential for the focal innervation pattern, it is unknown whether the same genetic program also controls synaptogenesis at nonfocal innervation sites. Here we show that one of three transcripts generated by the zebrafish unplugged locus, unplugged FL, encodes the zebrafish MuSK ortholog. We demonstrate that UnpFL/MuSK is critical for the assembly of focal synapses in zebrafish and that it cooperates with dystroglycan in the formation of nonfocal myoseptal and distributed synapses. Our results provide the first genetic evidence that neuromuscular synapse formation can occur in the absence of MuSK and that the combinatorial function of UnpFL/MuSK and dystroglycan generates diverse patterns of vertebrate neuromuscular innervation.