Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting State Inconsistency Bugs in DApps via On-Chain Transaction Replay and Fuzzing

Decentralized applications (DApps) consist of multiple smart contracts running on Blockchain. With the increasing popularity of the DApp ecosystem, vulnerabilities in DApps could bring significant impacts such as financial losses. Identifying vulnerabilities in DApps is by no means trivial, as modern DApps consist of complex interactions across multiple contracts. Previous research suffers from either high false positives or false negatives, due to the lack of precise contextual information which is mandatory for confirming smart contract vulnerabilities when analyzing smart contracts. In this paper, we present IcyChecker, a new fuzzing-based framework to effectively identify State inconsistency (SI) Bugs – a specific type of bugs that can cause vulnerabilities such as re-entrancy, front-running with complex patterns. Different from prior works, IcyChecker utilizes a set of accurate contextual information for contract fuzzing by replaying the on-chain historical transactions. Besides, instead of designing specific testing oracles which are required by other fuzzing approaches, IcyChecker implements novel mechanisms to mutate a set of fuzzing transaction sequences, and further identify SI bugs by observing their state differences. Evaluation of IcyChecker over the top 100 popular DApps showed it effectively identifies a total number of 277 SI bugs, with a precision of 87%. By comparing IcyChecker with other state-of-the-art tools (i.e., Smartian, Confuzzius, and Sailfish), we show IcyChecker not only identifies more SI bugs but also with much lower false positives, thanks to its integration of accurate on-chain data and unique fuzzing strategies. Our research sheds light on new ways of detecting smart contract vulnerabilities in DApps.
Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels

Large-scale imputation reference panels are now available and have contributed to efficient genome-wide association studies through genotype imputation. However, it is still under debate whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations. We imputed genotypes of East Asian (EAS; 180k Japanese) subjects using the Trans-Omics for Precision Medicine (TOPMed) reference panel and found that the standard imputation quality metric (Rsq) substantially overestimated the dosage r2 (squared correlation between imputed dosage and true genotype). Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1, or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we uncovered that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could overestimate or underestimate dosage r2 for a subpopulation in the multi-ancestry panel and the deviation represents different imputed-dosage distributions. Finally, despite the impact of θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value has a substantial impact on the imputed dosage and the imputation quality metric value.
NEURAL CORRELATES OF PERCEPTUAL SIMILARITY MASKING IN PRIMATE V1

Visual detection is a fundamental natural task. Detection becomes more challenging as the similarity between the target and the background in which it is embedded increases, a phenomenon termed “similarity masking”. To test the hypothesis that V1 contributes to similarity masking, we used voltage sensitive dye imaging (VSDI) to measure V1 population responses while macaque monkeys performed a detection task under varying levels of target-background similarity. Paradoxically, we find that during an initial transient phase, V1 responses to the target are enhanced, rather than suppressed, by target-background similarity. This effect reverses in the second phase of the response, so that in this phase V1 signals are positively correlated with the behavioral effect of similarity. Finally, we show that a simple model with delayed divisive normalization can qualitatively account for our findings. Overall, our results support the hypothesis that a nonlinear gain control mechanism in V1 contributes to perceptual similarity masking.
C-terminal truncation of CXCL10 attenuates inflammatory activity but retains angiostatic properties

Interferon-γ-inducible protein of 10 kDa (IP-10/CXCL10) is a dual-function CXC chemokine that coordinates chemotaxis of activated T cells and natural killer (NK) cells via interaction with its G protein-coupled receptor (GPCR), CXC chemokine receptor 3 (CXCR3). As a consequence of natural posttranslational modifications, human CXCL10 exhibits a high degree of structural and functional heterogeneity. However, the biological effect of natural posttranslational processing of CXCL10 at the carboxy (C)-terminus has remained partially elusive. The truncated CXCL10 proteoform CXCL10(1-73), lacking the four endmost C-terminal amino acids, was previously identified in human cell culture supernatant. To further explore the functioning of CXCL10(1-73), we optimized its production in this study through Fmoc-based solid phase peptide synthesis (SPPS) and propose an SPPS strategy to efficiently generate human CXCL10 proteoforms. Compared to intact CXCL10(1-77), CXCL10(1-73) had diminished affinity for glycosaminoglycans including heparin, heparan sulfate and chondroitin sulfate A. Moreover, CXCL10(1-73) exhibited an attenuated capacity to induce CXCR3A-mediated signaling, as evidenced in calcium mobilization assays and through quantification of phosphorylated extracellular signal-regulated kinase-1/2 (ERK1/2) and protein kinase B/Akt. Furthermore, CXCL10(1-73) incited reduced primary human T lymphocyte chemotaxis in vitro and evoked less peritoneal ingress of CXCR3+ T lymphocytes in mice receiving intraperitoneal chemokine injections. In contrast, loss of the four endmost C-terminal residues did not affect the inhibitory properties of CXCL10 on spontaneous and/or FGF-2-induced migration, proliferation, wound healing, phosphorylation of ERK1/2, and sprouting of human microvascular endothelial cells. Thus, C-terminally truncated CXCL10 has attenuated inflammatory properties, but preserved anti-angiogenic capacity. GRAPHICAL ABSTRACT
Transposable Element Insertions Are Associated with Batesian Mimicry in the Pantropical Butterfly Hypolimnas misippus

Hypolimnas misippus is a Batesian mimic of the toxic African Queen butterfly (Danaus chrysippus). Female H. misippus butterflies use two major wing patterning loci (M and A) to imitate the four colour morphs of D. chrysippus found in different regions of Africa. In this study, we examine the evolution of the M locus and identify it as an example of adaptive atavism. This phenomenon involves a morphological reversion to an ancestral character that results in an adaptive phenotype. We show that H. misippus has re-evolved a wing pattern present in other Hypolimnas species for Batesian mimicry of a D. chrysippus morph. Using haplotagging, a linked-read sequencing technology, we discover two large transposable element (TE) insertions located at the M locus and establish that these insertions are present in the dominant allele responsible for producing the ancestral and mimetic phenotype. By conducting a comparative analysis involving additional Hypolimnas species, we demonstrate that the dominant allele is derived. This suggests that the TEs disrupt a cis-regulatory element, leading to the reversion to an ancestral phenotype that is then utilized for Batesian mimicry of a distinct model, a different morph of D. chrysippus. Our findings present a compelling instance of convergent evolution and adaptive atavism, in which the same pattern element has independently evolved multiple times in Hypolimnas butterflies, repeatedly playing a role in Batesian mimicry of diverse model species.
Novel genetic model of pediatric Diffuse Intrinsic Pontine Glioma in Drosophila melanogaster

Diffuse Intrinsic Pontine Glioma (DIPG) is a lethal pediatric type of brain tumor that grows in the bm and originated from glial cells. Its location and infiltrative nature impede surgical resection and make the treatment difficult and low effective. In consequence, affected children have a short life expectancy of 12 months. The most frequent mutation is a substitution of lysine to methionine at residue 27 of histone H3 (H3K27M). Secondary mutations in additional genes, including Myc, are required for the malignancy of glial cells. The lack of studies and tumor aggressiveness make it necessary to generate new experimental models that reproduce the fundamental aspects of the disease and allow to expand the knowledge about DIPG. Drosophila melanogaster presents advantages as an experimental model and stands out for its genetic tools, easy handling, and great genetic and cellular homology with humans. Drosophila has contributed to the investigation of different diseases, including glioblastoma (GB) and neurodegenerative diseases as Alzheimeŕs or Parkinsońs. Here we present a new genetic model of DIPG generated in Drosophila melanogaster. It is based on the overexpression of H3K27 and Myc in glial cells that produce an increase in the number of glial cells in the ventral nerve cord and the expansion of glial membranes in early developmental stages. However, this novel DIPG model does not produce tumoral features in adult brains, in line with the pediatric nature of this disease. We have evaluated the activation of different signaling pathways active in other glial tumors, in this model of DIPG. The results show that, unlike GB, JNK pathway is not upregulated in DIPG, and it is not determinant for the progression of DIPG. Besides, glial cells in the DIPG model accumulate MMP1 and MMP2 and increase the accumulation of Liprin-γ, previously associated to the formation of synaptic structures in GB cells. The results show that DIPG is a unique entity that differs from other high-grade gliomas such as GB and will require of a different therapeutic approach.
Genome-Wide Association Study for Yield and Yield related traits reveals Marker–Trait Associations in Germplasm lines of Rice

Rice germplasm has abundant genetic diversity, which provides a feasible solution for mapping loci of multiple traits simultaneously. In this study, a set of 72 rice germplasm lines were evaluated for yield and yield-related traits, and significant phenotypic variation was observed among the lines. Three accessions with high yield performance were identified. The germplasm set comprised five sub-populations and genome-wide association study (GWAS) identified a total of 6 marker-trait associations (MTAs) for the studied traits. These MTAs were located on rice chromosomes 1, 3, 7, 9, and 12 and explained the trait phenotypic variances ranging from 17.8 to 26.3%. Six novel MTAs were identified for yield and yield-related traits. A total of 28 putative annotated candidate genes were identified in a genomic region spanning ∼200 kb around the MTAs respectively. Among the important genes underlying the novel MTAs were OsFBK12, bHLH, WRKY, HVA22, and ZmEBE-1, which are known to be associated with the identified novel QTLs. These MTAs provide a pathway for improving high yield in rice genotypes through molecular breeding.
OrgaMapper: A robust and easy-to-use workflow for analyzing organelle positioning

Eukaryotic cells are highly compartmentalized by a variety of organelles that carry out specific cellular processes. The position of these organelles within the cell is elaborately regulated and vital for their function. For instance, the position of lysosomes relative to the nucleus controls their degradative capacity and is altered in pathophysiological conditions. The molecular components orchestrating the precise localization of organelles remain incompletely understood. A confounding factor in these studies is the fact that organelle positioning is surprisingly non-trivial to address. E.g., perturbations that affect the localization of organelles often lead to secondary phenotypes such as changes in cell or organelle size. These phenotypes could potentially mask effects or lead to the identification of false positive hits. To uncover and test potential molecular components at scale, accurate and easy to use analysis tools are required that allow robust measurements of organelle positioning. Here, we present an analysis workflow for the faithful, robust, and quantitative analysis of organelle positioning phenotypes. Our workflow consists of an easy to use Fiji plugin and an R Shiny App. These tools enable users without background in image or data analysis to (1) segment single cells and nuclei and to detect organelles, (2) to measure cell size and the distance between detected organelles and the nucleus, (3) to measure intensities in the organelle channel plus one additional channel, and (4) to plot the results in informative graphs. Using simulated data and immunofluorescent images of cells in which the function of known factors for lysosome positioning has been perturbed, we show that the workflow is robust against common problems for the accurate assessment of organelle positioning such as changes of cell shape and size, organelle size and background.
Non-Perturbative Methods in Quantum Field Theory and Quantum Gravity

Title of dissertation: NON-PERTURBATIVE METHODS IN QUANTUM FIELD THEORY AND QUANTUM GRAVITY Anton de la Fuente, Doctor of Philosophy, 2016 Dissertation directed by: Professor Raman Sundrum Department of Physics This thesis considers non-perturbative methods in quantum field theory with applications to gravity and cosmology. In particular, there are chapters on black hole holography, inflationary model building, and the conformal bootstrap. NON-PERTURBATIVE METHODS IN QUANTUM FIELD THEORY AND QUANTUM GRAVITY by Anton de la Fuente Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2016 Advisory Committee: Professor Raman Sundrum, Chair/Advisor Professor Thomas Cohen Professor Jonathan Rosenberg Professor Zackaria Chacko Professor Rabindra Mohapatra
Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions

Model-based clustering is a probabilistic approach that views each cluster as a component in an appropriate mixture model. The Gaussian mixture model is one of the most widely used model-based methods. However, this model tends to perform poorly when clustering high-dimensional data due to the over-parametrized solutions that arise in high-dimensional spaces. This work instead considers the approach of combining dimension reduction techniques with clustering via a mixture of generalized hyperbolic distributions. The dimension reduction techniques, principal component analysis and factor analysis along with their extensions were reviewed. Then the aforementioned dimension reduction techniques were individually paired with the mixture of generalized hyperbolic distributions in order to demonstrate the clustering performance achieved under each method using both simulated and real data sets. For a majority of the data sets, the clustering method utilizing principal component analysis exhibited better classification results compared to the clustering method based on the extending the factor analysis model.
The algebra of symmetric analytic functions on L ∞

We consider the algebra of holomorphic functions on L ∞ that are symmetric, i.e. that are invariant under composition of the variable with any measure-preserving bijection of [0, 1]. Its spectrum is identified with the collection of scalar sequences such that is bounded and turns to be separable. All this follows from our main result that the subalgebra of symmetric polynomials on L ∞ has a natural algebraic basis.
The connection between inflow and outflow around accreting stellar-mass black holes

This thesis presents our multiwavelength monitoring of the Galactic black hole candidate X-ray binary MAXI J1836−194 during its 2011 outburst, in which the system underwent a dramatic brightening due to increased mass inflow (onto the black hole). The outburst peaked in the hard intermediate state before fading towards quiescence, and the compact jet remained on throughout. Quasisimultaneous radio, sub-mm, infrared, optical, ultraviolet and X-ray observations have been used to probe the relationship between the inflow and outflow around this accreting stellar-mass black hole with unprecedented detail. The results of this study deliver new information on the evolution of the accretion and ejection processes and how they are coupled, providing insight into how accretion powered jets are launched on all physical scales, including those from active galactic nuclei (AGN). Modelling the multiwavelength spectrum during the outburst cycle reveals the physical evolution of the compact jet and of the accretion flow. The broadband modelling shows that the jet spectral break, which is the transition between optically thick and optically thin synchrotron emission, shifted by 3 orders of magnitude in frequency during the outburst, and did not appear to be set by any one specific property of the accretion flow. In particular, the results show that the frequency of the break did not scale with the X-ray luminosity or with the inner radius of the accretion disk and instead was possibly set by the same accretion properties that drive the X-ray hardness. The evolution of the jet spectral break indicates that the size of the region where the electrons are first accelerated into a power-law at the base of the jet decreased by ∼103 gravitational radii as the outburst decayed. The modelling also implies that the high-energy cooling break in the jet was located in the optical band early in the outburst (between ≈ 3.2× 10 and 4.5× 10 Hz). This is the first detection of the cooling break in an X-ray binary, enabling an accurate measurement of the total radiative power of the jet.
Mumford-Shah type variational models and fast algorithms for image segmentation

Image segmentation, which aims to extract interesting objects from a given image, is one fundamental task in image processing and computer vision. Among several popular variational models for this purpose, we focus on the seminal Mumford and Shah (MS) model in this thesis. Build upon the MS model, we present various modifications that adapt to different segmentation tasks, and develop efficient minimization algorithms for the proposed modified models. Below, we guide you through the main contents and contributions of this dissertation. (i) We develop efficient dual-algorithm for two-phase image segmentation using TV-Allen-Cahn model (see Chapter 2). The main purpose is to overcome the difficulties resulted from the non-differentiability of the total variation (TV) term, and effectively handle the constraints of the binary level-set function. The use of a splitting-penalty method results in TV-Allen-Cahn type models associated with a very attractive “double-well” potential, which allows for the implementation of the Chambolle’s dual algorithm. Moreover, we present a new dual algorithm based on an edge-featured penalty of the dual variable, which only requires to solve a vectorial Allen-Cahn type equation with linear ∇(div)-diffusion rather than fully nonlinear diffusion in the Chambolle’s approach. Consequently, efficient numerical algorithms such as time-splitting method and Fast Fourier Transform (FFT) can be implemented. Various numerical tests show that two dual algorithms are much faster and stabler than the primal gradient descent approach, and the new
Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.
A Local-Time Semantics for Negotiations

Negotiations, introduced by Esparza et al., are a model for concurrent systems where computations involving a set of agents are described in terms of their interactions. In many situations, it is natural to impose timing constraints between interactions -- for instance, to limit the time available to enter the PIN after inserting a card into an ATM. To model this, we introduce a real-time aspect to negotiations. In our model of local-timed negotiations, agents have local reference times that evolve independently. Inspired by the model of networks of timed automata, each agent is equipped with a set of local clocks. Similar to timed automata, the outcomes of a negotiation contain guards and resets over the local clocks. As a new feature, we allow some interactions to force the reference clocks of the participating agents to synchronize. This synchronization constraint allows us to model interesting scenarios. Surprisingly, it also gives unlimited computing power. We show that reachability is undecidable for local-timed negotiations with a mixture of synchronized and unsynchronized interactions. We study restrictions on the use of synchronized interactions that make the problem decidable.
Spectrally constrained L1-norm improves quantitative accuracy of diffuse optical tomography

We consider L1-regularization of spectrally constrained DOT. Three popular algorithms are investigated: iteratively reweighted least square algorithm (IRLS), alternating directional method of multipliers (ADMM) and fast iterative shrinkage-thresholding algorithm (FISTA). We evaluate different regularizers and algorithms on a 3D simulated multi-spectral example.