Miracle-dz/UniFAR-FTUs huggingface dataset:
Delta expression in post‐mitotic neurons identifies distinct subsets of adult‐specific lineages in Drosophila

The Drosophila ventral nerve cord is comprised of numerous neuronal lineages, each derived from a stereotypically positioned neuroblast (NB). At the embryonic stage the unique identities of each NB, and several of their neuronal progeny, are well characterized by spatial and temporal expression patterns of molecular markers. These patterns of expression are not preserved at the larval stage and thus the identity of adult‐specific lineages remains obscure. Recent clonal analysis using MARCM has identified 24 adult‐specific lineages arising from thoracic NBs at the larval stage. In this study, we have explored a role for the Delta protein in development of the post‐embryonic Drosophila ventral nerve cord. We find that Delta expression identifies 7 of the 24 adult‐specific lineages of the thoracic ganglia by being highly enriched in clusters of newly born post‐mitotic neurons and their neurite bundles. The Delta lineages constitute the majority of bundles projecting to the ventral neuropil, consistent with a role in processing leg sensory information. Targeted knockdown of Delta in neurons using RNAi results in significantly decreased leg chemosensory response and a relatively unaffected leg mechanosensory response. Delta RNAi knockdown in Delta lineages also gives a more diffuse bundle terminal morphology while the overall path‐finding of neurite bundles is unaffected. We also identify a male‐specific Delta lineage in the terminal abdominal ganglia, implicating a role for Delta in development of sexually dimorphic neural networks. Examples of Delta‐expressing neurites contacting Notch‐expressing glia are also seen, but are not common to all Delta lineages. Altogether, these data reveal a fundamental pattern of Delta expression that is indicative of an underlying developmental program that confers identity to adult lineage neurons. © 2006 Wiley Periodicals, Inc. J Neurobiol 67: 23–38, 2007
Robust people counting system based on sensor fusion

This paper presents a novel robust people counting system based on fusing the depth and vision data. Conventional algorithms utilize the monoscopic or stereoscopic vision data to count people. However, these vision-based people counting methods often fail due to occasional illumination change and crowded environment. In the proposed algorithm, both the top-view vision and depth images are captured by a video-plus-depth camera mounted on the ceiling. The depth image is first processed by a morphological operator to alleviate depth artifacts such as the optical noise and lost data. Then the human object is extracted using a human model from the preprocessed depth image. Finally, the trajectory of the detected object is established by applying the bidirectional matching algorithm. Experimental results show that the proposed algorithm achieves over 98% accuracy in various testing environments.
Dimerization of Hsp90 Is Required for in Vivo Function

Heat shock protein 90 (Hsp90) plays a central role in signal transduction and has emerged as a promising target for anti-cancer therapeutics, but its molecular mechanism is poorly understood. At physiological concentration, Hsp90 predominantly forms dimers, but the function of full-length monomers in cells is not clear. Hsp90 contains three domains: the N-terminal and middle domains contribute directly to ATP binding and hydrolysis and the C domain mediates dimerization. To study the function of Hsp90 monomers, we used a single-chain strategy that duplicated the C-terminal dimerization domain. This novel monomerization strategy had the dual effect of stabilizing the C domain to denaturation and hindering intermolecular association of the ATPase domain. The resulting construct was predominantly monomeric at physiological concentration and did not function to support yeast viability as the sole Hsp90. The monomeric construct was also defective at ATP hydrolysis and the activation of a kinase and steroid receptor substrate in yeast cells. The ability to support yeast growth was rescued by the addition of a coiled-coil dimerization domain, indicating that the parental single-chain construct is functionally defective because it is monomeric.
Investigating an Open Source Cloud Storage Infrastructure for CERN-specific Data Analysis

We present a first case study where an open source storage cloud based on Openstack - SWIFT is used for handling data from CERN experiments using the ROOT software framework. This type of storage clouds promise to be easy to deploy and provide transparent access to data using standardized protocols. We examine the scalability and performance of the system using test cases which are derived from the normal usage and the structure of the ROOT software. The results show that cloud solutions like the SWIFT storage system could fulfill the requirements by the CERN scientific community. To verify this, a more extensive effort with many more tests and use-cases is needed. However, the impact of providing alternate storage solutions is large and further work is motivated.
Parallel hypergraph partitioning for scientific computing

Graph partitioning is often used for load balancing in parallel computing, but it is known that hypergraph partitioning has several advantages. First, hypergraphs more accurately model communication volume, and second, they are more expressive and can better represent nonsymmetric problems. Hypergraph partitioning is particularly suited to parallel sparse matrix-vector multiplication, a common kernel in scientific computing. We present a parallel software package for hypergraph (and sparse matrix) partitioning developed at Sandia National Labs. The algorithm is a variation on multilevel partitioning. Our parallel implementation is novel in that it uses a two-dimensional data distribution among processors. We present empirical results that show our parallel implementation achieves good speedup on several large problems (up to 33 million nonzeros) with up to 64 processors on a Linux cluster
Activity Date Estimation in Timestamped Interaction Networks

We propose in this paper a new generative model for graphs that uses a latent space approach to explain timestamped interactions. The model is designed to provide global estimates of activity dates in historical networks where only the interaction dates between agents are known with reasonable precision. Experimental results show that the model provides better results than local averages in dense enough networks
Multi-task Forest for Human Pose Estimation in Depth Images

In this paper, we address the problem of human body pose estimation from depth data. Previous works Based on random forests relied either on a classification strategy to infer the different body parts or on a regression approach to predict directly the joint positions. To permit the inference of very generic poses, those approaches did not consider additional information during the learning phase, e.g. the performed activity. In the present work, we introduce a novel approach to integrate additional information at training time that actually improves the pose prediction during the testing. Our main contribution is a multi-task forest that aims at solving a joint regression-classification task: each foreground pixel from a depth image is associated to its relative displacements to the 3D joint positions as well as the activity class. Integrating activity information in the objective function during forest training permits a better partitioning of the 3D pose space that leads to a better modelling of the posterior. Thereby, our approach provides an improved pose prediction, and as a by-product, can give an estimate of the performed activity. We performed experiments on a dataset performed by 10 people associated with the ground truth body poses from a motion capture system. To demonstrate the benefits of our approach, poses are divided into 10 different activities for the training phase. Results on this dataset show that our multi-task forest provides improved human pose estimation compared to a pure regression forest approach.
Rerouting Cellular Electron Flux To Increase the Rate of Biological Methane Production

ABSTRACT Methanogens are anaerobic archaea that grow by producing methane, a gas that is both an efficient renewable fuel and a potent greenhouse gas. We observed that overexpression of the cytoplasmic heterodisulfide reductase enzyme HdrABC increased the rate of methane production from methanol by 30% without affecting the growth rate relative to the parent strain. Hdr enzymes are essential in all known methane-producing archaea. They function as the terminal oxidases in the methanogen electron transport system by reducing the coenzyme M (2-mercaptoethane sulfonate) and coenzyme B (7-mercaptoheptanoylthreonine sulfonate) heterodisulfide, CoM-S-S-CoB, to regenerate the thiol-coenzymes for reuse. In Methanosarcina acetivorans, HdrABC expression caused an increased rate of methanogenesis and a decrease in metabolic efficiency on methylotrophic substrates. When acetate was the sole carbon and energy source, neither deletion nor overexpression of HdrABC had an effect on growth or methane production rates. These results suggest that in cells grown on methylated substrates, the cell compensates for energy losses due to expression of HdrABC with an increased rate of substrate turnover and that HdrABC lacks the appropriate electron donor in acetate-grown cells.
Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes

In recent publications, we have presented a data-driven approach to representing the nonlinear structure of hyperspectral imagery using manifold coordinates. The approach relies on graph methods to derive geodesic distances on the high-dimensional hyperspectral data manifold. From these distances, a set of intrinsic manifold coordinates that parameterizes the data manifold is derived. Scaling the solution relied on divide-conquer-and-merge strategies for the manifold coordinates because of the computational and memory scaling of the geodesic coordinate calculations. In this paper, we improve the scaling performance of isometric mapping (ISOMAP) and achieve full-scene global manifold coordinates while removing artifacts generated by the original methods. The CPU time of the enhanced ISOMAP approach scales as O(Nlog 2(N)), where N is the number of samples, while the memory requirement is bounded by O(Nlog(N)). Full hyperspectral scenes of O(10 6) samples or greater are obtained via a reconstruction algorithm, which allows insertion of large numbers of samples into a representative "backbone" manifold obtained for a smaller but representative set of O(105) samples. We provide a classification example using a coastal hyperspectral scene to illustrate the approach
Second-Order Random Graphs For Modeling Sets Of Attributed Graphs And Their Application To Object Learning And Recognition

The aim of this article is to present a random graph representation, that is based on second-order relations between graph elements, for modeling sets of attributed graphs (AGs). We refer to these models as Second-Order Random Graphs (SORGs). The basic feature of SORGs is that they include both marginal probability functions of graph elements and second-order joint probability functions. This allows a more precise description of both the structural and semantic information contents in a set of AGs and, consequently, an expected improvement in graph matching and object recognition. The article presents a probabilistic formulation of SORGs that includes as particular cases the two previously proposed approaches based on random graphs, namely the First-Order Random Graphs (FORGs) and the Function-Described Graphs (FDGs). We then propose a distance measure derived from the probability of instantiating a SORG into an AG and an incremental procedure to synthesize SORGs from sequences of AGs. Finally, SORGs are shown to improve the performance of FORGs, FDGs and direct AG-to-AG matching in three experimental recognition tasks: one in which AGs are randomly generated and the other two in which AGs represent multiple views of 3D objects (either synthetic or real) that have been extracted from color images. In the last case, object learning is achieved through the synthesis of SORG models.
A hybrid program slicing framework

Program slicing is a decomposition technique that transforms a large program into a smaller one that contains only statements relevant to the computation of a selected function. Applications of program slicing can be found in software testing, debugging, and maintenance by reducing the amount of data that has to be analyzed in order to comprehend a program or parts of its functionality. In this paper, we present a general dynamic and static slicing algorithm. Both algorithms are based on the notion of removable blocks and compute executable slices for object-oriented programs. In the second part of the paper we present our hybrid-slicing framework that was designed to take advantage of static and dynamic slicing algorithms that share the common notion of removable blocks, to enhance traditional slicing techniques. The hybrid-slicing framework is an integrated part of our existing MOOSE software comprehension framework that is used to demonstrate the applications and usability of these algorithms for the comprehension of software systems.
Autoencoders, Minimum Description Length and Helmholtz Free Energy

An autoencoder network uses a set of recognition weights to convert an input vector into a code vector. It then uses a set of generative weights to convert the code vector into an approximate reconstruction of the input vector. We derive an objective function for training autoencoders based on the Minimum Description Length (MDL) principle. The aim is to minimize the information required to describe both the code vector and the reconstruction error. We show that this information is minimized by choosing code vectors stochastically according to a Boltzmann distribution, where the generative weights define the energy of each possible code vector given the input vector. Unfortunately, if the code vectors use distributed representations, it is exponentially expensive to compute this Boltzmann distribution because it involves all possible code vectors. We show that the recognition weights of an autoencoder can be used to compute an approximation to the Boltzmann distribution and that this approximation gives an upper bound on the description length. Even when this bound is poor, it can be used as a Lyapunov function for learning both the generative and the recognition weights. We demonstrate that this approach can be used to learn factorial codes.
Full-Duplex Wireless-Powered Communication Network With Energy Causality

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.
X-linked Severe Combined Immunodeficiency Syndrome: The First Korean Case with γc Chain Gene Mutation and Subsequent Genetic Counseling

X-linked severe combined immunodeficiency (X-SCID) is a rare, life-threatening immune disorder, caused by mutations in the γc chain gene, which encodes an essential component of the cytokine receptors for interleukin-2 (IL-2), IL-4, IL-7, IL-9, IL-15, and IL-21. A 13-month-old boy with recurrent infections who had reduced serum immunoglobulin levels and decreased numbers of CD3, CD16/56 cells was evaluated for γc chain gene mutation and protein expression. The patient had a C-to-T point mutation at nucleotide position 690, one of the hot spots, resulting in a single amino acid substitution of cysteine for arginine (R226C), as determined by direct sequencing and PCR-RFLP. The patient's mother was a heterozygous carrier. Percutaneous umbilical cord blood sampling was performed at the 6-month of gestation in a subsequent pregnancy. As the immunophenotype of the fetus showed an identical pattern, the pregnancy was terminated and genetic analysis of the abortus confirmed recurrence. This is the first report of the molecular diagnosis of X-SCID in Korea. Genetic analysis of the γc chain gene is useful for definite diagnosis and genetic counseling for X-SCID.
IBM Research Report Online Power and Performance Estimation for Dynamic Power Management

Power management is at the forefront of challenges facing next-generation computing systems. Multiple mechanisms for dynamic regulation of processor power can be used aggressively to address power and thermal issues in real time; however, to do so effectively requires knowledge of the power and performance impact of the power-regulation choices. The problem is complex since the impact of each mechanism is both systemand workload-dependent. In this paper, we present our methodology and models for online predictions driven by data from processor performance counters. They estimate power consumption and performance when multiple power-management features are used simultaneously. Our goal is to provide effective, efficient power and performance prediction capabilities, which allow OS schedulers and workload/power managers to gauge the power and performance impact of a state change before making it. We focus on near-term estimation over 10s of millisecond intervals, rather than long-term averages. This approach allows power managers to fine-tune the power state of the processor for scheduling changes and workload variations. It also offers fast response for controlling the demand on the power supply and the cooling subsystem. We implement our methodology in a software service running on a Pentium M system and evaluate the power and performance models using the SPEC CPU2000 benchmarks. We also demonstrate their use with two new dynamic power management applications: Power Saver that optimizes for saving power within a specified bound on performance loss and a Performance Maximizer that optimizes for best performance while meeting a specified power limit.
Breaking RSA Generically Is Equivalent to Factoring

Let <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> be a random variable distributed according to some appropriate distribution over the set of products of two primes, such that factoring <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is believed to be hard. The RSA assumption states that, given an <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> chosen uniformly at random from <inline-formula> <tex-math notation="LaTeX">$ {\mathbb {Z}}_{N}$ </tex-math></inline-formula> and an <inline-formula> <tex-math notation="LaTeX">$e \in {\mathbb {N}} \setminus \{1\}$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$\gcd (e, \phi (N)) = 1$ </tex-math></inline-formula>, it is computationally hard to find an <inline-formula> <tex-math notation="LaTeX">$x\in {\mathbb {Z}} _{N}$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$x^{e} - a \equiv 0 \pmod N$ </tex-math></inline-formula>. When complexity-theoretic (relative) lower bounds for certain cryptographic problems in a general model of computation seem to elude discovery, a common practice in cryptography is to give proofs of computational security in meaningful restricted models of computation. An example of such a restricted model that is interesting in cryptography is the generic group model that has been used for proving lower bounds for the discrete logarithm problem and other related problems. A generic model captures that an algorithm does not exploit the bit representation of the elements other than for testing equality. In this paper, we prove that the problem of factoring <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> can be efficiently reduced to solving the RSA problem on <inline-formula> <tex-math notation="LaTeX">$ {\mathbb {Z}}_{N}$ </tex-math></inline-formula> in the generic ring model of computation, where an algorithm can perform ring operations, inverse ring operations, and test equality. This provides evidence toward the soundness of the RSA encryption and digital signature scheme, in particular showing that under the factoring assumption, they are not vulnerable to certain kinds of cryptanalytic attacks.
The Data Transport Computer: a 3-dimensional massively parallel SIMD computer

The author describes a computer that has been built primarily to support both three-dimensional simulation of physical phenomena, as well as other applications that require three-dimensional models, and visualization of the results as volume data. This computer is a massively parallel single-instruction-stream, multiple-data-stream (SIMD) machine whose processors are interconnected in a three-dimensional rectangular lattice, together with a controller that extends this lattice to a virtual lattice with many more nodes than there are processors. It is called the Data Transport Computer because its interprocessor communication structure is capable of moving large amounts of data in parallel-not only among its processors, but also to and from I/O devices. <<ETX>>