Miracle-dz/UniFAR-FTUs huggingface dataset:
A single-phase, proximal path-following framework

We propose a new proximal, path-following framework for a class of constrained convex problems. We consider settings where the nonlinear---and possibly non-smooth---objective part is endowed with a proximity operator, and the constraint set is equipped with a self-concordant barrier. Our approach relies on the following two main ideas. First, we re-parameterize the optimality condition as an auxiliary problem, such that a good initial point is available; by doing so, a family of alternative paths towards the optimum is generated. Second, we combine the proximal operator with path-following ideas to design a single-phase, proximal, path-following algorithm. Our method has several advantages. First, it allows handling non-smooth objectives via proximal operators; this avoids lifting the problem dimension in order to accommodate non-smooth components in optimization. Second, it consists of only a \emph{single phase}: While the overall convergence rate of classical path-following schemes for self-concordant objectives does not suffer from the initialization phase, proximal path-following schemes undergo slow convergence, in order to obtain a good starting point \cite{TranDinh2013e}. In this work, we show how to overcome this limitation in the proximal setting and prove that our scheme has the same $\mathcal{O}(\sqrt{\nu}\log(1/\varepsilon))$ worst-case iteration-complexity with standard approaches \cite{Nesterov2004,Nesterov1994} without requiring an initial phase, where $\nu$ is the barrier parameter and $\varepsilon$ is a desired accuracy. Finally, our framework allows errors in the calculation of proximal-Newton directions, without sacrificing the worst-case iteration complexity. We demonstrate the merits of our algorithm via three numerical examples, where proximal operators play a key role.
Hierarchical Face Recognition Based on SVDD and SVM

Current face recognition methods are mainly based on face database. Face recognition task in natural environment demands face recognition algorithm has the rejection capability for the face samples out of face database, but existing methods lack this rejection ability for non-target samples. In this paper, a new hierarchical face recognition algorithm is proposed which can reject non-database test samples and classify model samples within database exactly. One-class recognition characteristics of support vector data description is firstly utilized to rejection recognition and then the excellent classification property of support vector machine is employed to recognize the accepted face samples. By way of simulated experiments, the effectiveness of proposed method is verified.
Respecting Markov Equivalence in Computing Posterior Probabilities of Causal Graphical Features

There have been many efforts to identify causal graphical features such as directed edges between random variables from observational data. Recently, Tian et al. proposed a new dynamic programming algorithm which computes marginalized posterior probabilities of directed edge features over all the possible structures in O(n3n) time when the number of parents per node is bounded by a constant, where n is the number of variables of interest. However the main drawback of this approach is that deciding a single appropriate threshold for the existence of the directed edge feature is difficult due to the scale difference of the posterior probabilities between the directed edges forming v-structures and the directed edges not forming v-structures. We claim that computing posterior probabilities of both adjacencies and v-structures is necessary and more effective for discovering causal graphical features, since it allows us to find a single appropriate decision threshold for the existence of the feature that we are testing. For efficient computation, we provide a novel dynamic programming algorithm which computes the posterior probabilities of all of n(n – 1)/2 adjacency and n(n–1 choose 2) v-structure features in O(n3 * 3n) time.
Geometrically superresolved lensless imaging using a spatial light modulator.

In this paper we introduce an imaging system based on a reflective phase-only spatial light modulator (SLM) in order to perform imaging with improved geometric resolution. By using the SLM, we combine the realization of two main abilities: a lens with a tunable focus and a phase function that, after proper free-space propagation, is projected as an amplitude distribution on top of the inspected object. The first ability is related to the realization of a lens function combined with a tunable prism that yields a microscanning of the inspected object. This by itself improves the spatial sampling density. The second ability is related to a projection of a phase function that is computed using an iterative beam-shaping Gerchberg-Saxton algorithm. After the free-space propagation from the SLM toward the inspected object, an amplitude pattern is generated on top of the object. This projected pattern and a set of low-resolution images with relative shift are interlaced and, after applying the proper regularization method, a geometrically superresolved image is reconstructed.
Characterizing nature and location of congestion on the public Internet

We address the following question in this study: what is the nature of Internet congestion and where does congestion really occur on a public Internet path? Answering this question helps service providers and content providers better engineer emerging services on the Internet. Our large-scale path measurement and analysis study indicates that congestion on the Internet exhibits a wide variety of packet loss and delay characteristics. Based on our classification using "congestion signatures", we find four dominant "types" of congestion which may be related to macroscopic behavior. A particularly frequent type of congestion we observe is "flash congestion", which creates significant bursty packet loss on a fairly long time-scale. Additionally, our study suggests that flash congestion predominantly occurs at the access provider network within the "last mile" on an Internet path. The Internet "cloud" does not contribute heavily to congestion. Consequently, prevalent approaches to bypass the cloud using "edge-based" content delivery networks and caching may not be effective in reducing congestion.
The Animal Model of Spinal Cord Injury as an Experimental Pain Model

Pain, which remains largely unsolved, is one of the most crucial problems for spinal cord injury patients. Due to sensory problems, as well as motor dysfunctions, spinal cord injury research has proven to be complex and difficult. Furthermore, many types of pain are associated with spinal cord injury, such as neuropathic, visceral, and musculoskeletal pain. Many animal models of spinal cord injury exist to emulate clinical situations, which could help to determine common mechanisms of pathology. However, results can be easily misunderstood and falsely interpreted. Therefore, it is important to fully understand the symptoms of human spinal cord injury, as well as the various spinal cord injury models and the possible pathologies. The present paper summarizes results from animal models of spinal cord injury, as well as the most effective use of these models.
New insights into the evolution of chromosome 1

A complex low-repetitive human DNA probe (BAC RP11-35B4) together with two microdissection-derived region-specific probes of the multicolor banding (MCB) probe-set for chromosome 1 were used to re-analyze the evolution of human chromosome 1 in comparison to four ape species. BAC RP11-35B4 derives from 1q21 and contains 143 kb of non-repetitive DNA; however, it produces three specific FISH signals in 1q21, 1p12 and 1p36.1 of Homo sapiens (HSA). Human chromosome 1 was studied in comparison to its homologues in Hylobates lar (HLA), Pongo pygmaeus (PPY), Gorilla gorilla (GGO) and Pan troglodytes (PTR). A duplication of sequences homologous to human 1p36.1 could be detected in PPY plus an additional signal on PPY 16q. The region homologous to HSA 1p36.1 is also duplicated in HLA, and split onto chromosomes 7q and 9p; the region homologous to HSA 1q21/1p12 is present as one region on 5q. Additionally, the breakpoint of a small pericentric inversion in the evolution of human chromosome 1 compared to other great ape species could be refined. In summary, the results obtained here are in concordance with previous reports; however, there is evidence for a deletion of regions homologous to human 1p34.2→p34.1 during evolution in the Pongidae branch after separation of PPY.
Multi-Task Learning for Subspace Segmentation

Subspace segmentation is the process of clustering a set of data points that are assumed to lie on the union of multiple linear or affine subspaces, and is increasingly being recognized as a fundamental tool for data analysis in high dimensional settings. Arguably one of the most successful approaches is based on the observation that the sparsest representation of a given point with respect to a dictionary formed by the others involves nonzero coefficients associated with points originating in the same subspace. Such sparse representations are computed independently for each data point via l1-norm minimization and then combined into an affinity matrix for use in a final spectral clustering step. The downside of this procedure is two-fold. First, unlike canonical compressive sensing scenarios with ideally-randomized dictionaries, the data-dependent dictionaries here are unavoidably highly structured, disrupting many of the favorable properties of the l1 norm. Secondly, by treating each data point independently, we ignore useful relationships between points that can be leveraged for jointly computing such sparse representations. Consequently, we motivate a multi-task learning-based framework for learning coupled sparse representations leading to a segmentation pipeline that is both robust against correlation structure and tailored to generate an optimal affinity matrix. Theoretical analysis and empirical tests are provided to support these claims.
The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN)

OBJECTIVE Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.
Tocilizumab in early progressive rheumatoid arthritis: FUNCTION, a randomised controlled trial

Objectives The efficacy of tocilizumab (TCZ), an anti-interleukin-6 receptor antibody, has not previously been evaluated in a population consisting exclusively of patients with early rheumatoid arthritis (RA). Methods In a double-blind randomised controlled trial (FUNCTION), 1162 methotrexate (MTX)-naive patients with early progressive RA were randomly assigned (1:1:1:1) to one of four treatment groups: 4 mg/kg TCZ+MTX, 8 mg/kg TCZ+MTX, 8 mg/kg TCZ+placebo and placebo+MTX (comparator group). The primary outcome was remission according to Disease Activity Score using 28 joints (DAS28–erythrocyte sedimentation rate (ESR) <2.6) at week 24. Radiographic and physical function outcomes were also evaluated. We report results through week 52. Results The intent-to-treat population included 1157 patients. Significantly more patients receiving 8 mg/kg TCZ+MTX and 8 mg/kg TCZ+placebo than receiving placebo+MTX achieved DAS28-ESR remission at week 24 (45% and 39% vs 15%; p<0.0001). The 8 mg/kg TCZ+MTX group also achieved significantly greater improvement in radiographic disease progression and physical function at week 52 than did patients treated with placebo+MTX (mean change from baseline in van der Heijde–modified total Sharp score, 0.08 vs 1.14 (p=0.0001); mean reduction in Health Assessment Disability Index, −0.81 vs −0.64 (p=0.0024)). In addition, the 8 mg/kg TCZ+placebo and 4 mg/kg TCZ+MTX groups demonstrated clinical efficacy that was at least as effective as MTX for these key secondary endpoints. Serious adverse events were similar among treatment groups. Adverse events resulting in premature withdrawal occurred in 20% of patients in the 8 mg/kg TCZ+MTX group. Conclusions TCZ is effective in combination with MTX and as monotherapy for the treatment of patients with early RA. Trial registration number ClinicalTrials.gov, number NCT01007435
trans-3-Chloroacrylic Acid Dehalogenase from Pseudomonas pavonaceae 170 Shares Structural and Mechanistic Similarities with 4-Oxalocrotonate Tautomerase

ABSTRACT The genes (caaD1 and caaD2) encoding the trans-3-chloroacrylic acid dehalogenase (CaaD) of the 1,3-dichloropropene-utilizing bacterium Pseudomonas pavonaceae 170 were cloned and heterologously expressed in Escherichia coli andPseudomonas sp. strain GJ1. CaaD is a protein of 50 kDa that is composed of α-subunits of 75 amino acid residues and β-subunits of 70 residues. It catalyzes the hydrolytic cleavage of the β-vinylic carbon-chlorine bond intrans-3-chloroacrylic acid with a turnover number of 6.4 s−1. On the basis of sequence similarity, oligomeric structure, and subunit size, CaaD appears to be related to 4-oxalocrotonate tautomerase (4-OT). This tautomerase consists of six identical subunits of 62 amino acid residues and catalyzes the isomerization of 2-oxo-4-hexene-1,6-dioate, via hydroxymuconate, to yield 2-oxo-3-hexene-1,6-dioate. In view of the oligomeric architecture of 4-OT, a trimer of homodimers, CaaD is postulated to be a hexameric protein that functions as a trimer of αβ-dimers. The sequence conservation between CaaD and 4-OT and site-directed mutagenesis experiments suggested that Pro-1 of the β-subunit and Arg-11 of the α-subunit are active-site residues in CaaD. Pro-1 could act as the proton acceptor/donor, and Arg-11 is probably involved in carboxylate binding. Based on these findings, a novel dehalogenation mechanism is proposed for the CaaD-catalyzed reaction which does not involve the formation of a covalent enzyme-substrate intermediate.
On comparative performance analysis of color, edge and texture based histograms for content based color image retrieval

Image Retrieval is a system which extracts the relevant set of images and matches with query image from large collection of dataset. It is used in variety of domains including finger print identification, biodiversity information system, digital library, medical imaging etc. An effective and efficient system is required to improve its retrieval performance. In CBIR, the images are indexed according to image contents. Contents of images are color, shape and texture that are derived from images. The minimum distance between query and dataset images implies that the dataset image is similar to query image. In this paper, we study and compare different approaches used in CBIR and similarity measure taken for finding the similarity between two images. Strengths and weaknesses of these methods are observed using performance parameters such as precision, recall and similarity measures.
Mitochondrial Import Driving Forces: Enhanced Trapping by Matrix Hsp70 Stimulates Translocation and Reduces the Membrane Potential Dependence of Loosely Folded Preproteins

ABSTRACT The mitochondrial heat shock protein Hsp70 (mtHsp70) is essential for driving translocation of preproteins into the matrix. Two models, trapping and pulling by mtHsp70, are discussed, but positive evidence for either model has not been found so far. We have analyzed a mutant mtHsp70, Ssc1-2, that shows a reduced interaction with the membrane anchor Tim44, but an enhanced trapping of preproteins. Unexpectedly, at a low inner membrane potential, ssc1-2 mitochondria imported loosely folded preproteins more efficiently than wild-type mitochondria. The import of a tightly folded preprotein, however, was not increased in ssc1-2 mitochondria. Thus, enhanced trapping by mtHsp70 stimulates the import of loosely folded preproteins and reduces the dependence on the import-driving activity of the membrane potential, directly demonstrating that trapping is one of the molecular mechanisms of mtHsp70 action.
Graphene-based materials for biosensing and bioimaging

Graphene, a free-standing two-dimensional crystal with one-atom thickness, exhibits distinct properties that are highly attractive for biosensing and bioimaging, such as a high electrical conductivity, a large planar area, and an excellent ability to quench fluorescence. This article selectively reviews recent advances in the field of graphene-based materials for biosensing and bioimaging. In particular, graphene-based enzyme biosensors, DNA biosensors, and immunosensors are summarized in detail. Graphene-based biotechnology for cell imaging is also described. Future perspectives and possible challenges in this rapidly developing area are also discussed.
Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-agent cooperation without sufficient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate actions by changing internal rewards given to the agents, and extends the reinforcement learning methods (Q-learning and Profit Sharing) to enable the agents to acquire the appropriate Q-values updated according to the derived condition. Concretely, the internal rewards change when the agents can only find better solution than the current one. The intensive simulations on the maze problems as one of testbeds have revealed the following implications:(1) our proposed method successfully enables the agents to select their own appropriate cooperating actions which contribute to acquiring the minimum steps towards to their goals, while the conventional methods (i.e., Q-learning and Profit Sharing) cannot always acquire the minimum steps; and (2) the proposed method based on Profit Sharing provides the same good performance as the proposed method based on Q-learning.
How Should We Revise the Paratactic Theory

This paper takes another look at Davidson's paratactic theory of indirect discourse and evaluates some revisions to it, proposed recently by Ian Rumfitt (Mind, 1993). Davidson's original version of the theory – according to which indirect speech reports refer to token utterances – has a problem dealing with ambiguity. Rumfitt suggests that we can solve this problem by supposing that the immediate objects of verbs in indirect speech are token representations of disambiguated LF tree-structures. I argue that this proposal is inadequate and suggest that it is better to think of indirect speech as relating speakers to utterance types.
Induction of cardiac beta-adrenergic receptor kinase 1 in rat heart failure caused by coronary ligation.

Beta-adrenergic receptor kinase 1 (beta ARK1) participates in the desensitization of beta-adrenergic receptors by uncoupling the signal transduction. The present study was designed to examine whether neurohumoral increase is crucial for the activation of beta ARK1 in heart failure. Four weeks after the ligation of rat coronary artery, LV dP/d t max was reduced, cardiac response to isoproterenol was impaired, and ratio of right ventricular weight to body weight, an index of cardiac hypertrophy, was increased. At the same time, beta ARK1 expression and activity were augmented in the hypertrophied hearts. In addition, plasma norepinephrine content was enhanced in accordance with cardiac hypertrophy, cardiac beta ARK1 expression, LV dP/d t max, and LVEDP. These results of the present study suggest that beta ARK1 is augmented in concert with circulating norepinephrine level and that beta ARK1 may account for, at least in part, the cardiac dysfunction in rat with myocardial infarction.
Spatial frequency domain imaging (SFDI): a technology overview and validation of an LED-based clinic friendly device

We present an overview of Spatial Frequency Domain Imaging (SFDI), a non-contact near infrared (NIR) imaging approach that enables rapid, quantitative determination of the optical properties and in vivo concentrations of chromophores over a wide field-of-view. SFDI is capable of rapidly rendering quantitative two-dimensional maps of oxy and deoxy hemoglobin, total hemoglobin (related to blood volume), tissue oxygen saturation, water content, and scattering coefficient (related to tissue structure).
Robust Model Predictive Control for humanoids standing balancing

This paper presents the implementations of Model Predictive Control for the standing balance control of a humanoid to reject external disturbances. The strategies allow the robot to have a compliant behaviour against external forces resulting in a stable and smooth response. The first, ZMP based controller, compensates for the center of mass deviation while the second, attitude controller, regulates the orientation of the body to counterbalance the external disturbances. These two control strategies are combined as an integrated stabilizer, which further increases the effectiveness. Simulation studies on the COMAN humanoid are presented and the data are analysed. The simulations show significant improvements in rejection of external disturbances compared to an existing compliant stabilizer.
The Importance of Locality in Scheduling and Load Balancing for Multiprocessors

This paper addresses the importance of locality when migrating tasks of a parallel program between processors for load balancing in a multiprocessor. Static and preprocessing task scheduling algorithms work well for certain applications, but irregular problems often require dynamic load balancing. Many heuristics have been developed for scheduling the proper number of iterations of a parallel loop on each processor. Other techniques dynamically balance load by relocating tasks from busy processors to idle ones. Most of these approaches, however, neglect the cost of migrating a task from the processor on which it was originally created or assigned. This paper proposes using data aanity to help determine which tasks should be migrated from a heavily loaded processor to an idle one. AAnity is a measure of cost of moving the task and can be generated by the programmer or compiler, or it can be computed at runtime. This paper is intended to fullll the area examination requirement in the