Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting mutant p53 in human tumors.

Evidence generated over the past 20 years has unequivocally shown that the p53 tumor suppressor is the most frequently altered gene in human tumors, with more than 25,000 mutations currently reported in the International Agency for Research on Cancer (IARC) TP53 database. p53 plays a central role in modulating response to cellular stress through transcriptional regulation of genes involved in cell-cycle arrest, DNA repair, apoptosis, and senescence. Inactivation of the p53 pathway is essential for the formation of the majority of human tumors, making p53 an attractive target for both basic and applied research. Efforts to target p53 therapeutically have been challenging and have yet to make an impact on patient care; however, the landscape may be changing. In the report that accompanies this article, Lehmann et al describe the results of a first-in-human clinical trial evaluating a small molecule that can bind mutant p53 and restore wild-type transcriptional properties to the protein. Results of the trial show that the APR-246 (PRIMA-1) molecule is safe at therapeutic plasma levels and can induce p53-dependent biologic effects. These findings advance mutant p53 as a viable target and represent a major step forward in targeting the most frequently altered pathway in cancer.
Neuromorphic Architecture With 1M Memristive Synapses for Detection of Weakly Correlated Inputs

Neuromorphic computing takes inspiration from the brain to build highly parallel, energy- and area-efficient architectures. Recently, hardware realizations of neurons and synapses using memristive devices were proposed and applied for the task of correlation detection. However, for weakly correlated signals, this task becomes challenging because of the variability and the asymmetric conductance response of the memristive devices. In this brief, we propose a high-density memristive system realized using nanodevices based on phase-change technology. We present a noise-robust phase-change implementation of a neuron and a synaptic learning rule that is capable of capturing patterns of weakly correlated inputs. We experimentally demonstrate the operation with a correlation coefficient as low as 0.2 using a record number of 1M phase-change synapses.
Development of extrusion-torsion simultaneous processing for grain refinement in Magnesium alloys

We have proposed a new extrusion process combined with torsion. Extrusion-torsion simultaneous processing is a very attractive technique for fabricating a rod-shape material with high strength and excellent workability. The grain size of AZ91D magnesium alloy was gradually decreased with increasing the torsion speed in as extrusion-twisted conditions. Grain refinement under 2um was achieved by the optimized extrusion-torsion condition. Hardness was increased by the addition of torsion speed comparing with as solution-treated and as extruded samples. Hardness change was dependent on the extrusion-torsion temperature.
Filed emission cathodes for electrodynamic tethers: identifying compatible cathode materials

This extended summary is a preliminary version of the final paper on the progress made in identifying the field emission array cathode materials required for compatibility with the electrodynamic tether application. A description of the desired cathode material characteristics, the materials under investigation, and the rationale for exploring these materials are presented. The work functions of selected material film candidates have been measured before their exposure to oxygen and are included in this summary.
Illusory memories in amnesic patients: conceptual and perceptual false recognition.

Little is known about the neuropsychology of false recognition. D.L. Schacter, M. Verfaellie, and D. Pradere (1996) induced false recognition in amnesic patients and normal controls by exposing them to numerous semantic associates of a nonstudied word and found that amnesics showed significantly reduced levels of false recognition. To determine whether this outcome is specific to the semantic domain, the authors examined false recognition after exposure to lists of conceptually and perceptually related words. In the control group, conceptual false recognition was associated with "remember" responses and perceptual false recognition was associated with "know" responses. Amnesic patients showed reduced levels of conceptual and perceptual false recognition that were approximately equally divided between remember and know responses.
An Evaluation of Rational-Emotive Imagery as a Component of Rational-Emotive Therapy in the Treatment of Test Anxiety

This study evaluated the efficacy of rational-emotive imagery as a component of rational-emotive therapy in reduction of college students' test anxiety. 11 volunteers met for 6 1-hr. group treatment sessions over a 3-wk. period. After 2 initial treatment sessions subjects were randomly assigned to groups given either rational-emotive therapy with rational-emotive imagery or rational-emotive therapy without imagery. Contrary to predictions, improvement between groups on self-report and performance measures was nonsignificant. Failure to obtain differences was attributed to similarities in content of treatment sessions and short treatment time. Combined groups reported significant improvement on all dependent measures. Although the study did not yield the predicted benefits of the imagery, results lend further support to the efficacy of rational-emotive therapy procedures in the reduction of test anxiety.
DARKNIGHT Wins Chinese Dark Chess Tournament

The game of Chinese Dark Chess has evolved from and is played with the pieces and half board of Chinese Chess. The 32 pieces are all face-down and randomly put on the 4×8 board when the game starts. Two players alternately flip, move or capture a piece. The player who first captures all pieces of the opponent wins the game. The game draws when no flipping or capturing occurs within 180 plies, or when the same position occurs three times. More details of the rules of playing Chinese dark chess are described in (Chen et al., 2010; Yen et al., 2015).
Interactive visualization of multivariate trajectory data with density maps

We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.
Constraints over Lambda-Structures in Semantic Underspecification

We introduce a first-order language for semantic underspecification that we call Constraint Language for Lambda-Structures (CLLS). A λ-structure can be considered as a λ-term up to consistent renaming of bound variables (λ-equality); a constraint of CLLS is an underspecified description of a λ-structure. CLLS solves a capturing problem omnipresent in underspecified scope representations. CLLS features constraints for dominance, lambda binding, parallelism, and anaphoric links. Based on CLLS we present a simple, integrated, and underspecified treatment of scope, parallelism, and anaphora.
Measurement of hepatic glucose output, krebs cycle, and gluconeogenic fluxes by NMR analysis of a single plasma glucose sample.

13C and 1H NMR spectroscopy of plasma glucose was used to resolve the isotopomer contributions from tracer levels of [1,6-13C2]glucose, a novel tracer of glucose carbon skeleton turnover, and [U-13C]propionate, a tracer of hepatic citric acid cycle metabolism. This allowed simultaneous measurements of hepatic glucose production and citric acid cycle fluxes from the NMR analysis of a single plasma glucose sample in fasted animals. Glucose carbon skeleton turnover, as reported by the dilution of [1,6-13C2]glucose, was 56 +/- 2 micromol/kg/min in the presence of labeling from [U-13C]propionate and 53 +/- 4 micromol/kg/min in its absence. Therefore, as expected, the labeling contributions from [U-13C]propionate metabolism did not have a significant effect on the measurement of glucose turnover. For the group infused with both tracers, citric acid cycle flux estimates from the analysis of glucose C2 isotopomer ratios were consistent with those from our recent experiments where only [U-13C]propionate was infused, verifying that the presence of [1,6-13C2]glucose did not interfere with these measurements. This integrated analysis of hepatic glucose output and citric acid cycle fluxes from plasma glucose isotopomers yielded a noninvasive estimate of hepatic citrate synthase flux of 74 +/- 12 micromol/kg/min for 24-h fasted rats.
A Memory-Friendly Multi-modal Emotion Analysis for Smart Toy

The recent advance in deep learning technologies has provided many opportunities to the industries in developing smarter products, such as smart toys, smart cars and smart homes. Unfortunately, a common practical issue to these deep learning methods is the high requirements in computing power even for the prediction part. As the deep learning models' complexities increase, the memory requirements for these models usually exceed the limit of many low end computing devices, such as mobile IoT devices. It is even worse when a multimodel approach is employed. In this paper, we will demonstrate with the development of multimodal emotion analysis on a smart toy, where the user's speech, facial expression and action are used for the understanding of user's emotion. By trimming down DNN complexity or replaced by other learning approaches, we are able to sequeeze four classifiers in 800MB of memory. Finally, results of these methods are ensembled with a fusion approach using a fully connected neural network to obtain a more accuracy and stable result. Our multimodal approach achieved an improvement of about 20% when comparing any unimodal emotion analysis.
Towards the automated detection of unknown malware on live systems

In this paper, we propose a new system monitoring framework that can serve as an enabler for automated malware detection on live systems. Our approach takes advantage of the increased availability of hardware assisted virtualization capabilities of modern CPUs, and its basic novelty consists in launching a hypervisor layer on the live system without stopping and restarting it. This hypervisor runs at a higher privilege level than the OS itself, thus, it can be used to observe the behavior of the analyzed system in a transparent manner. For this purpose, we also propose a novel system call tracing method that is designed to be configurable in terms of transparency and granularity.
Segmentation approach of Arabic manuscripts text lines based on multi agent systems

Huge amount of handwritten documents can be treated by intelligent systems in order to retrieve information using text queries instead of manually searching in the scanned images. These systems often consist of five stages: pre-processing, segmentation, feature extraction, decision-making (character recognition) and post-processing. This paper deals with the segmentation stage, in which we present an approach inspired by the perception mechanisms involved in the human reading process to automatically extract text lines from Arabic handwritten documents. The proposed approach is based on multi-agent systems to detect and group connected components that belong to the same line. Experimental results on a data-set of Arabic handwritten documents show that this approach is a promising solution for extracting handwritten curved text lines.
Fast Unit-Modulus Least Squares With Applications in Beamforming

Unit-modulus least squares (ULS) problems arise in many applications, including phase-only beamforming, sensor network localization, synchronization, phase retrieval, and radar code design. ULS formulations can always be recast as unit-modulus quadratic programs, to which semidefinite relaxation (SDR) can be applied, and is often the state-of-the-art approach. However, SDR lifts the problem dimension (i.e., the number of variables) from <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$N^2$</tex-math></inline-formula>, which drastically increases the memory burden and computational cost when the problem size is already large—e.g., when designing phase-only beamformer weights for massive multiple-input-multiple-output systems. This paper focuses on scalable first-order algorithms for the ULS problem and some of its variants. It advocates using simple gradient projection (GP) as a starting point for solving the ULS problem, establishes global convergence of GP to a Karush–Kuhn–Tucker point for this NP-hard problem, and bounds its iteration complexity. Then it proposes ULS extensions tailored to reflect practical beamformer design objectives, bringing in and exploiting new degrees of freedom to improve the beampattern designs. Simple variants of GP are proposed to deal with these extended ULS problems. Simulations are used to showcase the effectiveness of the proposed algorithms in both the plain ULS problem and in the context of phase-only beamforming.
(α, k)-Minimal Sorting and Skew Join in MPI and MapReduce

As computer clusters are found to be highly effective for handling massive datasets, the design of efficient parallel algorithms for such a computing model is of great interest. We consider ({\alpha}, k)-minimal algorithms for such a purpose, where {\alpha} is the number of rounds in the algorithm, and k is a bound on the deviation from perfect workload balance. We focus on new ({\alpha}, k)-minimal algorithms for sorting and skew equijoin operations for computer clusters. To the best of our knowledge the proposed sorting and skew join algorithms achieve the best workload balancing guarantee when compared to previous works. Our empirical study shows that they are close to optimal in workload balancing. In particular, our proposed sorting algorithm is around 25% more efficient than the state-of-the-art Terasort algorithm and achieves significantly more even workload distribution by over 50%.
Superiorization: An optimization heuristic for medical physics

PURPOSE To describe and mathematically validate the superiorization methodology, which is a recently developed heuristic approach to optimization, and to discuss its applicability to medical physics problem formulations that specify the desired solution (of physically given or otherwise obtained constraints) by an optimization criterion. METHODS The superiorization methodology is presented as a heuristic solver for a large class of constrained optimization problems. The constraints come from the desire to produce a solution that is constraints-compatible, in the sense of meeting requirements provided by physically or otherwise obtained constraints. The underlying idea is that many iterative algorithms for finding such a solution are perturbation resilient in the sense that, even if certain kinds of changes are made at the end of each iterative step, the algorithm still produces a constraints-compatible solution. This property is exploited by using permitted changes to steer the algorithm to a solution that is not only constraints-compatible, but is also desirable according to a specified optimization criterion. The approach is very general, it is applicable to many iterative procedures and optimization criteria used in medical physics. RESULTS The main practical contribution is a procedure for automatically producing from any given iterative algorithm its superiorized version, which will supply solutions that are superior according to a given optimization criterion. It is shown that if the original iterative algorithm satisfies certain mathematical conditions, then the output of its superiorized version is guaranteed to be as constraints-compatible as the output of the original algorithm, but it is superior to the latter according to the optimization criterion. This intuitive description is made precise in the paper and the stated claims are rigorously proved. Superiorization is illustrated on simulated computerized tomography data of a head cross section and, in spite of its generality, superiorization is shown to be competitive to an optimization algorithm that is specifically designed to minimize total variation. CONCLUSIONS The range of applicability of superiorization to constrained optimization problems is very large. Its major utility is in the automatic nature of producing a superiorization algorithm from an algorithm aimed at only constraints-compatibility; while nonheuristic (exact) approaches need to be redesigned for a new optimization criterion. Thus superiorization provides a quick route to algorithms for the practical solution of constrained optimization problems.
Cache-based compaction: a new technique for optimizing Web transfer

We propose and study a new technique, which we call cache-based compaction for reducing the latency of Web browsing over a slow link. The compaction technique trades computation for bandwidth. The key observation is that an object can be coded in a highly compact form for transfer if similar objects that have been transferred earlier can be used as references. The contributions of this paper are: (1) an efficient selection algorithm for selecting similar objects as references, and (2) an encoding/decoding algorithm that reduces the size of a Web object by exploiting its similarities with the reference objects. We verify the efficacy of our proposal through detailed experimental evaluations. This compaction technique significantly generalizes previous work on optimizing Web transfer using compression or differencing, and provides a systematic foundation that ties together caching, compression and prefetching.
Plagiarism Detection on the Student Assignment from Internet using Words n-grams Fingerprints

Cheating is one of major problem in education. With the increase in the use of computers and the Internet the oppor- tunities for students to plagiarize have increased many times. Electronic source material coupled with word processed sub- mission facilitates a 'cut and paste' mentality where a stu- dent's engagement with a subject can be bypassed. The students share their work and collaborate with other stu- dents through email or copy material from published works or the internet without proper citation. In computer pro- gramming classes, the students can easily share their source code for programming assignments. The e-learning give ben- eflt for educational process but it has high plagiarism op- portunity. There is a need of plagiarism detection tool to help a lecturer to give student assessment fairly. This work was designed the plagiarism detection tool in the e-learning system. The aim of any plagiarism detection process is to ensure that copied material is detected, and that no stu- dent is unfairly accused of copying. Software exists to help achieve this aim, but (obviously) no system is perfect, and no fully automatic plagiarism detection system exists that will identify all and only cheaters. Similarity issue insure the plagiarism accuracy and time consuming insure the quality of service because it involves the amount of document and amount of ideas inside documents. Using flngerprint tech- nique by tokenisation of words n-grams it has accuracy and faster detection process.
Analysis of daily business reports based on sequential text mining method

This paper proposes a new method that discovers characteristic sequential patterns in textual data. The data are composed of three kinds of information: time information, attributes, and text. The method gathers items of the data with the same attribute values, arranges the gathered items in order of the time, and generates sequences. The method also extracts events from each text by using a text mining method. Finally, the method discovers characteristic sequential patterns, composed of sets of events, from sequences by a sequential mining method. In this paper, we apply the method to business reports collected by our sales force automation system and try to discover characteristic sequential patterns. We verify whether the patterns are valid by investigating texts relating to the patterns.
Angiotensin II activates the human renin promoter in an in vitro model: the role of c-Jun-N-terminal kinase.

BACKGROUND Angiotensin II (Ang II) down-regulates renin expression in juxtaglomerular cells, however, recent experimental evidence obtained in transgenic mice suggested that Ang II may "paradoxically" increase transcriptional activity of the proximal renin promoter. METHODS To dissect signalling mechanisms contributing to the up-regulation of the proximal renin promoter by Ang II, porcine proximal tubular cells stably expressing the rabbit AT(1) receptor (LLC-PK/AT(1)) were transiently transfected with a luciferase reporter construct containing the 582 bp long piece of the human renin promoter. Activation of mitogen-activated protein kinases (MAPK) was detected by western blotting using phospho-MAPK-specific antibodies. RESULTS Ang II dose-dependently stimulated the transcriptional activity of the human renin promoter (10(-7) M Ang II: 3.50+/-1.25-fold stimulation). In these cells Ang II activated both extracellular signal-regulated kinase (ERK) and the c-Jun-N-terminal kinase (JNK). Inhibition of the ERK cascade did not reduce the stimulation of the renin promoter by Ang II, however, two expression vectors designed to inhibit the JNK pathway, the dominant negative JNK and the Jun-kinase interacting peptide inhibited the fold stimulation induced by Ang II (2.75+/-0.69 vs 1.6+/-0.23 and 1.8+/-0.34, respectively; P<0.01). Furthermore, genistein, a tyrosine kinase inhibitor, blocked the effect of Ang II both on the JNK and the promoter activation. CONCLUSION Ang II may have a stimulatory effect on the proximal renin promoter in proximal tubular cells and this effect is mediated by tyrosine kinases and the JNK cascade.