Miracle-dz/UniFAR-FTUs huggingface dataset:
An Iterative Approach to Improved Local Phase Coherence Estimation

This paper introduces a novel iterative approach to estimating local phase coherence in situations characterized by low signal-to-noise ratios. Local phase coherence is used for a wide range of computer vision applications such as edge and corner detection and object description. An issue faced in extracting local phase coherence is the presence of image noise. While existing approaches to dealing with noise when estimating local phase coherence is effective for low noise situations, they are inadequate for situations contaminated by high levels of noise. In the proposed approach, the issue of high image noise is addressed by re-estimating both the local phase coherence and the underlying image content iteratively to improve local phase coherence estimates. This is performed using a feedback loop model, where the local phase coherence estimates are used to re-estimate the image content using a moment-adaptive bilateral estimation scheme and information from the re-estimated image content is used to re-estimate the local phase coherence. Experiment results show that the proposed approach can be used to provide improved local phase coherence estimates.
Effect of Acute Mental Stress on Heart Rate and QT Variability in Postmyocardial Infarction Patients

Emotionally charged events are associated with an increased risk of sudden cardiac death (SCD). In this study we assessed RR and QT variability index (QTVI) at baseline during anger recall test (AR). We calculated QTVI from a 5-min ECG recording and from a 10-beats segment around the presumed maximum sympathetic activation in thirty post-myocardial infarction patients under β -blocker therapy and 10 controls underwent. In all groups, the low-frequency component of RR and SBP increased during AR. In all recordings, the QTVI calculated on a 5-min ECG recording and the QTVI10 beats were higher in patients than in controls (P < 0.05). The QTVI during AR remained unchanged from baseline within each group. Conversely, during AR, the QTVI10 beats in controls diminished significantly (P < 0.05) from baseline whereas in patients remained unchanged. The inability to buffer an acute stress-induced increase in sympathetic activity could explain why events charged with acute stress are associated with an increased risk of ventricular arrhythmias in this setting of patients and support the role of cognitive behavior stress management strategies.
An adaptive particle filter tracking method based on homography and common FOV

In object tracking, methods based on a particle filter are widely used, but the technique alone often fails in various situations. Sometimes multi-camera systems using homography are tried to solve problems like occlusion. We propose an adaptive particle filter approach in a two-camera surveillance system. The proposed method is based on accurate homography, which is used to estimate the position of a tracked object in one camera view, using the position information in the other camera view. However, for each camera view, we divide particles into two groups - one group to follow the observed object in each camera view as usual, but the other group to follow the estimated position by homography. In this way, the particles placed at the estimated position could correct any mistakes or help solve other problems like partial/full occlusion and reappearance after temporary disappearance during tracking. Experiments show a good performance in tracking an object, even though the object is occluded or moves out of one camera view temporarily.
Simulation Metamodel Estimaiton with Penalized B-splines: a Second-order Cone Programming Approach

This paper approximates simulation models by B-splines with a penalty on high-order finite differences of the coefficients of adjacent B-splines. The penalty prevents overfitting. The simulation output is assumed to be nonnegative. The nonnegative spline simulation metamodel is casted as a second-order cone programming problem, which can be solved efficiently by modern optimization techniques. The method is implemented in MATLAB. 1 NONNEGATIVE SPLINE METAMODEL A simulation model can be represented as a function:
Chinese school teachers’ conceptions of high-stakes and low-stakes assessments: an invariance analysis

ABSTRACT The study investigated teachers’ conceptions of high-stakes and low-stakes assessments with a sample of 1,013 school teachers from China. In general, the assessment model indicated that school teachers in this study agreed with the most factors. They demonstrated a broad understanding of the improvement, evaluation, control, irrelevance, and challenges of assessment for a range of purposes. Multi-group confirmatory factor analysis for high-stakes vs low-stakes found that Chinese teachers perceived improvement, school accountability and examination purposes as highly positively correlated, though accountability was weakly correlated with irrelevance. Further, these teachers showed favourable attitudes toward low-stakes assessments which are believed more indicative of learning, teaching, examination, and school accountability. These results indicate that it is an appropriate option to adopt low-stakes assessments to remedy the unintended effects of high-stakes assessments in China. Possible explanations for major results are discussed, which may provide implications for other educational contexts.
Understanding the role of the Q338H MUTYH variant in oxidative damage repair

The MUTYH DNA–glycosylase is indirectly engaged in the repair of the miscoding 7,8-dihydro-8-oxo-2′-deoxyguanine (8-oxodG) lesion by removing adenine erroneously incorporated opposite the oxidized purine. Inherited biallelic mutations in the MUTYH gene are responsible for a recessive syndrome, the MUTYH-associated polyposis (MAP), which confers an increased risk of colorectal cancer. In this study, we functionally characterized the Q338H variant using recombinant proteins, as well as cell-based assays. This is a common variant among human colorectal cancer genes, which is generally considered, unrelated to the MAP phenotype but recently indicated as a low-penetrance allele. We demonstrate that the Q338H variant retains a wild-type DNA–glycosylase activity in vitro, but it shows a reduced ability to interact with the replication sensor RAD9:RAD1:HUS1 (9–1–1) complex. In comparison with Mutyh−/− mouse embryo fibroblasts expressing a wild-type MUTYH cDNA, the expression of Q338H variant was associated with increased levels of DNA 8-oxodG, hypersensitivity to oxidant and accumulation of the population in the S phase of the cell cycle. Thus, an inefficient interaction of MUTYH with the 9–1–1 complex leads to a repair-defective phenotype, indicating that a proper communication between MUTYH enzymatic function and the S phase checkpoint is needed for effective repair of oxidative damage.
Optimizing the Efficacy of Epitope-Directed DNA Vaccination1

An increasing number of clinical trials has been initiated to test the potential of prophylactic or curative vaccination with tumor Ag-encoding DNA vaccines. However, in the past years it has become apparent that for many Ags and in particular for tumor Ags the intracellular processing and presentation are suboptimal. To improve epitope-directed DNA vaccines we have developed a murine model system in which epitope-specific, DNA vaccine-induced T cell immunity can be followed by MHC tetramer technology directly ex vivo. We have used this well-defined model to dissect the parameters that are crucial for the induction of strong cytotoxic T cell immunity using two independent model Ags. These experiments have led to a set of five guidelines for the design of epitope-directed DNA vaccines, indicating that carboxyl-terminal fusion of the epitope to a carrier protein of foreign origin is the most favorable strategy. DNA vaccines that are based on these guidelines induce high-magnitude CD8+ T cell responses in >95% of vaccinated animals. Moreover, T cell immunity induced by this type of optimized DNA vaccine provides long-term protection against otherwise lethal tumor challenges.
Information Sharing and Coordination Mechanism for Supply Chain Management: A Case Study of the Automotive Industry

This study aims to investigate the problems of uncertainty embedded in the automotive supply chain and design the coordination mechanism of supply chain management (SCM) to reduce the uncertainty disturbances. In order to understand the uncertainty that occurs in the interaction among supply chain participants, this study conducts an investigation on two levels - vehicle manufacturers (VM) and suppliers. The results of case studies suggest an increased level of customization, which will create more uncertainty to the extant supply chains. This can be translated into requirement for demand policy objective. To accompany with supply chain process redesign towards market mediation and supplier involvement, combining information sharing with coordination mechanism can reinforce the existing IT infrastructure of the automotive industry to achieve the policy objective of VM.
Radiotherapy-Induced Anti-Tumor Immunity Contributes to the Therapeutic Efficacy of Irradiation and Can Be Augmented by CTLA-4 Blockade in a Mouse Model

Purpose There is growing evidence that tumor-specific immune responses play an important role in anti-cancer therapy, including radiotherapy. Using mouse tumor models we demonstrate that irradiation-induced anti-tumor immunity is essential for the therapeutic efficacy of irradiation and can be augmented by modulation of cytotoxic T lymphocyte (CTL) activity. Methods and Materials C57BL/6 mice, syngeneic EL4 lymphoma cells, and Lewis lung carcinoma (LL/C) cells were used. Cells were injected into the right femurs of mice. Ten days after inoculation, tumors were treated with 30 Gy of local X-ray irradiation and their growth was subsequently measured. The effect of irradiation on tumor growth delay (TGD) was defined as the time (in days) for tumors to grow to 500 mm3 in the treated group minus that of the untreated group. Cytokine production and serum antibodies were measured by ELISA and flow cytometry. Results In the EL4 tumor model, tumors were locally controlled by X-ray irradiation and re-introduced EL4 cells were completely rejected. Mouse EL4-specific systemic immunity was confirmed by splenocyte cytokine production and detection of tumor-specific IgG1 antibodies. In the LL/C tumor model, X-ray irradiation also significantly delayed tumor growth (TGD: 15.4 days) and prolonged median survival time (MST) to 59 days (versus 28 days in the non-irradiated group). CD8(+) cell depletion using an anti-CD8 antibody significantly decreased the therapeutic efficacy of irradiation (TGD, 8.7 days; MST, 49 days). Next, we examined whether T cell modulation affected the efficacy of radiotherapy. An anti-CTLA-4 antibody significantly increased the anti-tumor activity of radiotherapy (TGD was prolonged from 13.1 to 19.5 days), while anti-FR4 and anti-GITR antibodies did not affect efficacy. Conclusions Our results indicate that tumor-specific immune responses play an important role in the therapeutic efficacy of irradiation. Immunomodulation, including CTLA-4 blockade, may be a promising treatment in combination with radiotherapy.
Spectrum Assignment and Sharing for Delay Minimization in Multi-Hop Multi-Flow CRNs

This paper investigates the problem of spectrum assignment and sharing to minimize the total delay of multiple concurrent flows in multi-hop cognitive radio networks. We first analyze the expected per-hop delay, which incorporates the sensing delay and transmission delay characterizing the PU activities and spectrum capacities. Then we formulate a minimum delay optimization problem with interference constraints, and propose an approximation algorithm termed MCC to solve the problem. According to our theoretical analysis, MCC has a bounded performance ratio and a low computational complexity. Finally, we exploit the minimum potential delay fairness in spectrum sharing to mitigate the inter-flow contentions. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms with that of the state-of-the-art.
Feasibility and Acceptability of a Gender-Specific Group Treatment for Men with Substance Use Disorders

ABSTRACT Gender-specific group therapy for substance use disorders (SUDs) has primarily focused on women. The purpose of this study was to (a) develop a gender-specific group therapy for men with SUDs who are heterogeneous with respect to their substances of use and demographic and clinical characteristics, and (b) conduct a pilot study to assess satisfaction and feasibility. Overall, participants (N = 10) reported high satisfaction with the group. From baseline to posttreatment, there was a significant decrease in alcohol use severity and in substance use. The results of this study provide preliminary support for group therapy that focuses on gender-specific themes in an all-male setting for men with SUDs.
Background signal estimation from multi-sensor signals based on outer product and non-linear filters

The source separation based on a statistical and computational technique is one of the most exciting topic in a multivariate signal analysis. We proposed a novel source separation technique using a tensor product expansion. This technique is the signal separation that the background noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where the absolute error is used as the error function (TPE-AE). The effectiveness of TPE-AE for artificial signals and real signals is represented in [1]. However, TPE-AE has two problems; one is that a result of TPE-AE is strongly affected by Gaussian random noise, another one is that an estimated signal varies widely due to a random search. In order to solve these problems, signal estimation techniques based on a median and Modified Trimmed Mean (MTM) are proposed in this paper. These methods calculate the outer product using a non-linear filter to estimate the background noise. Results show that novel techniques can separate the background noise more accurately than the conventional TPE-AE.
Adaptive approximate Bayesian computation

Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm. Copyright 2009, Oxford University Press.
Utility of the orbitocranial approach for clipping of anterior communicating artery aneurysms: significance of dissection of the interhemispheric fissure and the sylvian fissure.

OBJECTIVE To investigate the importance of sufficient dissection of the interhemispheric and sylvian fissures, an orbitocranial approach was used for clipping of ruptured anterior communicating artery aneurysms. PATIENTS AND METHODS From January 1998 through March 2009, 41 patients underwent surgery for subarachnoid hemorrhage caused by rupture of an anterior communicating artery aneurysm. Their mean age was 58.4 years, with a range of 37 to 84 years. The preoperative World Federation of Neurosurgical Societies grade was I to III in 32 patients and IV to V in 9 patients. The direction of the aneurysm was upward in 23 patients, forward in 14 patients, and backward in 4 patients. Seven patients had a large aneurysm. RESULTS All patients underwent surgery during the acute stage following the subarachnoid hemorrhage (day 0-2). A right orbitocranial approach was used for most patients, but a left orbitocranial approach was used for 9 patients because of the presence of a complicated aneurysm and the positional relationship of the left-right A2 segment. In 12 patients, external decompression was performed. The outcome, using the Glasgow Outcome Scale, was good recovery in 24 patients, moderately disabled in 8 patients, and severely disabled in 4 patients, and 5 patients died. Temporary eye movement disorders developed after surgery in 5 patients but resolved in all patients within 2 months. No patients had olfactory disturbance. DISCUSSION Using the orbitocranial approach and sufficient dissection of the interhemispheric and sylvian fissures, we could secure a broad field of vision and surgical field, which contributed to a safe operation. The only postoperative complication caused by the surgical approach was temporary eye movement disorder. Thus, for some patients with aneurysms of the anterior communicating artery, the orbitocranial approach contributes to improved outcomes.
Comparative analysis of resource reservation cost for different RSVP extension schemes in mobile environments

In wireless networks, advanced resource reservation becomes a necessary requirement for fast handover with QoS guarantee because of the mobility of nodes. However, advanced resource reservation would unavoidably lead to system resource wasting. In this paper, taking the representative RSVP extension schemes with mobility support, MRSVP, Multicast RSVP and Fast RSVP as examples, we analyze factors affecting resource reservation costs and present formalized expressions of reservation costs for these different resource reservation schemes. On this basis, we quantify and compare the reservation costs for different resource reservation schemes, and then give recommendations on resource reservation schemes design for mobile environments.
Efficient traffic matrix estimation for data center networks

We address the problem of estimating the real-time nature of traffic flows in data center networks, using the light-weight SNMP data. Unlike the problem of estimating the traffic matrix (TM) across origin-destination (OD) pairs in ISP networks, the traffic flows across servers or ToR (Top of Rack) switch pairs in data center networks are notoriously more irregular and volatile. Although numerous methods have been proposed in past several years to solve the TM estimation problem in ISP networks, none of them could be applied to data center networks directly. In this paper, we make the first step to decompose the data center topology to several clusters by leveraging the characteristics of prevailing data center architecture, which makes TM inference problems in data center networks easy to handle. We also state a lemma to obtain the coarse-grained traffic characteristics of these clusters unbiasedly. Two efficient TM inference algorithms are proposed based on the decomposed topology and the coarse-grained traffic information, which improves the state-of-the-art tomography methods without requiring any additional instrumentation. Finally, comparing with a recent representative TM inference algorithm through intensive simulations, the results show that, i) the data center TM inference problem could be well handled after the decomposition step, ii) our two algorithms outperforms the former one in both speed and accuracy.
Low-Power TCAMs for Very Large Forwarding Tables

Ternary content-addressable memories (TCAMs) may be used to obtain a simple and very fast implementation of a router's forwarding engine. The applicability of TCAMs is, however, limited by their size and high power requirement. Zane et al. proposed a method and associated algorithms to reduce the power needed to search a forwarding table using a TCAM. We improve on both the algorithms proposed by them. Additionally, we show how to couple TCAMs and high-bandwidth SRAMs so as to overcome both the power and size limitations of a pure TCAM forwarding engine. By using one of our novel TCAM-SRAM coupling schemes (M-12 Wb), we are able to reduce TCAM memory by a factor of about 5 on IPv4 data sets and by a factor of about 2.5 on IPv6 data sets; TCAM power requirement is reduced by a factor of about 10 on IPv4 data sets and by a factor of about 6 on IPv6 data sets. These comparisons are with respect to the improved TCAM algorithms we have developed for the strategies of Zane et al. The stated improvements come at the cost of increasing SRAM requirement by a factor 2.5 for IPv4 data and a factor of 5 for IPv6 data. This cost is unimportant given that SRAMs are relatively quite cheap and have much less power requirement. For another of our novel TCAM-SRAM coupling schemes (1-12Wc), the TCAM memory and power reduced by factors of about 4 and 12 for IPv4 data sets, respectively, and by factors of about 2 and 10 for IPv6 data sets. The SRAM required, however, increased by factors of 3 and 7, respectively. These improvements come with no loss in the time (as measured by the number of TCAM searches and SRAM accesses) to do a lookup.
Energy minimization design of fixed- and flex-grid optical networks

Core networks offer high capacities by harvesting the high bandwidth-distance product of optical technologies. However they consume a non-negligible amount of power, while their traffic volume is forecasted to grow at very high rates for the 10 or 15 coming years. Thus, energy-efficiency in core and metro networks is mandatory for the sustainability of the future Internet. In this context, in this work we used Mantis, our network planning and operation tool, to design and carry out a comparative study of energy efficiency of current and next generation optical networks. In particular, we examined the cases of fixed-grid single-line-rate (SLR) Wavelength Division Multiplexing (WDM) optical networks, which are now deployed in the core, and next-generation mixed-line-rates (MLR) WDM and flex-grid networks. Under realistic network scenarios we profiled the total energy consumption of the optical layer and showed that through energy-aware algorithms we can achieve significant power savings.
MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model

Given two sentences, participating systems assign a semantic similarity score in the range of 0 - 5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also combined these two runs linearly (cor responding to run 3). Our results indicate that the two techniques perform comparably while the combination outperforms the individual ones on four out of five datasets , namely answer - answer, headlines, plagiarism, and question - question , and on the overall weighted mean of STS 2016 and 2015 datasets .
Nicolai mapping versus exact chiral symmetry on the lattice

The two-dimensional $N=2$ Wess-Zumino model is constructed on the lattice through Nicolai mapping with a Ginsparg-Wilson fermion. The Nicolai mapping requires a certain would-be surface term in the bosonic action which ensures the vacuum energy cancellation even on the lattice, but inevitably breaks chiral symmetry. With the Ginsparg-Wilson fermion, the holomorphic structure of the would-be surface term is maintained, leaving a discrete subgroup of the exact chiral symmetry intact for a monomial scalar potential. Through this feature both the boson and fermion can be kept massless on the lattice without any fine-tuning.