Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards FHE in Embedded Systems: A Preliminary Codesign Space Exploration of a HW/SW Very Large Multiplier

The integration of fully homomorphic encryption (FHE) into embedded systems is limited due to its huge computational requirements. FHE requires multiplications of operands up to millions of bits. Current implementations use high-end and parallel processors, leading to high-power consumption. We propose a hardware-software system to benefit from the best of hardware (performance/low-power) and software (flexibility) capabilities. In this letter, we present our first codesign results for hardware dedicated multiplication units, which is used as atomic operations by the software layer. We report FPGA implementation results for those units and software performance estimations of their use in multiplications up to 16 millions-bit operands. In range of 10 W power consumption, our analysis show that good FHE performance is affordable.
LFA-1 Engagement Triggers T Cell Polarization at the HIV-1 Virological Synapse

ABSTRACT HIV-1 efficiently disseminates by cell-cell spread at intercellular contacts called virological synapses (VS), where the virus preferentially assembles and buds. Cell-cell contact triggers active polarization of organelles and viral proteins within infected cells to the contact site to support efficient VS formation and HIV-1 spread; critically, however, which cell surface protein triggers contact-induced polarization at the VS remains unclear. Additionally, the mechanism by which the HIV-1 envelope glycoprotein (Env) is recruited to the VS remains ill defined. Here, we use a reductionist bead-coupled antibody assay as a model of the VS and show that cross-linking the integrin LFA-1 alone is sufficient to induce active T cell polarization and recruitment of the microtubule organizing center (MTOC) in HIV-1-infected cells. Mutant cell lines coupled with inhibitors demonstrated that LFA-1-induced polarization was dependent on the T cell kinase ZAP70. Notably, immunofluorescent staining of viral proteins revealed an accumulation of surface Env at sites of LFA-1 engagement, with intracellular Env localized to a Golgi compartment proximal to the polarized MTOC. Furthermore, blocking LFA-1-induced MTOC polarization through ZAP70 inhibition prevented intracellular Env polarization. Taken together, these data reveal that LFA-1 is a key determinant in inducing dynamic T cell remodeling to the VS and suggest a model in which LFA-1 engagement triggers active polarization of the MTOC and the associated Env-containing secretory apparatus to sites of cell-cell contact to support polarized viral assembly and egress for efficient cell-cell spread. IMPORTANCE HIV-1 causes AIDS by spreading within immune cells and depletion of CD4 T lymphocytes. Rapid spread between these cells occurs by highly efficient cell-cell transmission that takes place at virological synapses (VS). VS are characterized by striking T cell remodeling that is spatially associated with polarized virus assembly and budding at sites of cell contact. Here, we show that the integrin LFA-1 triggers organelle polarization and viral protein recruitment, facilitating formation of the VS, and that this requires the T cell kinase ZAP70. Taken together, these data suggest a mechanism by which HIV-1-infected T cells sense and respond to cell contact to polarize viral egress and promote cell-cell spread. Understanding how cell-cell spread is regulated may help reveal therapeutic targets to specifically block this mode of HIV-1 dissemination.
Adding Papillomacular Bundle Measurements to Standard Optical Coherence Tomography Does Not Increase Sensitivity to Detect Prior Optic Neuritis in Patients with Multiple Sclerosis

Purpose To improve the detection of retinal nerve fiber layer (RNFL) thinning in multiple sclerosis (MS), a special peripapillary ring scanning algorithm (N-site RNFL, N-RNFL) was developed for spectral domain optical coherence tomography (SD-OCT). In contrast to the standard protocol (ST-RNFL) scanning starts nasally, not temporally, and provides an additional sector of analysis, the papillomacular bundle (PMB). We aimed to ascertain whether the temporal RNFL differs between the two techniques, whether N-RNFL is more sensitive than ST-RNFL to detect previous optic neuritis (ON), and whether analyzing the PMB adds additional sensitivity. Furthermore, we investigated whether RNFL is associated with disease severity and/or disease duration. Methods We conducted a cross-sectional case-control study of 38 patients with MS, of whom 24 had a history of ON, and 40 healthy controls (HC). Subjects with ON within the previous 6 months were excluded. Records included clinical characteristics, visual evoked potentials (VEP), and SD-OCT in both techniques. Results In a total of 73 evaluable MS eyes, temporal N-RNFL was abnormal in 17.8%, temporal ST-RNFL in 19.2%, and the PMB-RNFL in 21.9%. In ON eyes, the sensitivity of temporal N-RNFL and ST-RNFL did not differ significantly (37.0%/33.3%, p = 0.556). The sensitivity of VEP was 85.2%. RNFL thickness was associated with disease severity in all eyes, with and without a history of ON, and with disease duration. Conclusion The two OCT techniques detected previous ON with similar sensitivity, but the sensitivity of VEPs was superior to that of both N-RNFL and ST-RNFL. Our results indicate that the widely used ST-RNFL technique is appropriate for peripapillary RNFL measurements in MS patients.
An Extension of ETF Arbitrage to Sector Trading Using ANN

We design and deploy a trading strategy that mirrors the Exchange Traded Fund (ETF) arbitrage technique for sector trading. Artiﬁcial Neural Networks (ANNs) are used to capture pricing relationships within a sector using intra-day trade data. The fair price of a target security is learnt by the ANN. Signiﬁcant deviations of the true price from the computed price (ANN predicted price) are exploited. To facilitate arbitrage, output function of the trained ANN is locally linearly approximated. The strategy has been backtested on intra-day data from September 2005. Results are very promising, with a high percentage of proﬁtable trades. With low average trade durations and ease of computation, this strategy is well suited for algorithmic trading systems.
Artificial Neural Network Based Modeling of Injection Pressure in Diesel Engines

Injection pressure in diesel engines has an important effect on the engine performance and soot formation. During performing the experimental work, the measurement of the torque, power, specific fuel consumption (SFC) and soot formation values in the diesel engines is a time consuming work and it also requires specific tools, an expert. In addition to the difficulties mentioned earlier, some of the operating points can be only investigated and evaluated because of difficulties of measuring the parameters at the operating conditions. In this study, to overcome these difficulties, an artificial neural network (ANN) is used for prediction of performance and soot formation in diesel engines. The training data for ANN is obtained from experimental measurements. In comparison of performance analysis of ANN, the deviation coefficients of torque, power, SFC, and soot formation for the test pressure conditions are less than 1.66, 3.2, 2.89, and 3.47, respectively. The statistical coefficient of multiple determinations for the investigated cases is about 0.9934 to 0.9983. The degree of accuracy is acceptable in predicting the parameters of the system. So, it can be concluded that ANN provides a feasible method in predicting the system parameters. Key-Words: Artificial neural network, injection pressure, diesel engine
Neuron geometry extraction by perceptual grouping in ssTEM images

In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.
Faking it: Synthetic Text-to-speech Synthesis for Under-resourced Languages – Experimental Design

Speech synthesis or text-to-speech (TTS) systems are currently available for a number of the world’s major languages, but for thousands of the world’s ‘minor’ languages no such technology is available. While awaiting the development of such technology, we would like to try the stop-gap solution of using an existing TTS system for a major language (the base language) to ‘fake’ TTS for a minor language (the target language). This paper describes the design for an experiment which involves finding a suitable base language for the Australian Aboriginal language Pitjantjajara as a target language, and evaluating its usability in the real-life situation of providing language technology support for speakers of the target language whose understanding of the local majority language is limited, for example in the scenario of going to the doctor.
Family context assessment and positive parenting policies*

ABSTRACT This study presents the descriptive data of a family context assessment carried out using a new instrument which includes a wide range of variables identified in recent scientific literature as influencing children's psychological development. Participants were 772 families with children aged between 7 and 11. The results reveal variability between the families as regards the quality levels of key family context variables. They also enable intervention aims to be established, focused specifically on those variables in which quality levels indicate possible room for improvement. These include (among others) encouraging reading, fostering autonomy and self-esteem and reducing parental stress. In accordance with the principles of positive parenting, a series of intervention strategies are proposed, using the strengths that every family owns to address the weakest areas detected in the family.
Real option pricing with mean-reverting investment and project value

In this work, we are concerned with valuing the option to invest in a project when the project value and the investment cost are both mean-reverting. Previous works on stochastic project and investment cost concentrate on geometric Brownian motions (GBMs) for driving the factors. However, when the project involved is linked to commodities, mean-reverting assumptions are more meaningful. Here, we introduce a model and prove that the optimal exercise strategy is not a function of the ratio of the project value to the investment V/I – contrary to the GBM case. We also demonstrate that the limiting trigger curve as maturity approaches traces out a nonlinear curve in (V, I) space and derive its explicit form. Finally, we numerically investigate the finite-horizon problem, using the Fourier space time-stepping algorithm of Jaimungal and Surkov [2009. Lev´y based cross-commodity models and derivative valuation. SIAM Journal of Financial Mathematics, to appear. http://www.ssrn. com/abstract=972837]. Numerically, the optimal exercise policies are found to be approximately linear in V/I; however, contrary to the GBM case they are not described by a curve of the form V*/I*=c(t). The option price behavior as well as the trigger curve behavior nicely generalize earlier one-factor model results.
The Radical Potentials of Human Experience: Maslow, Leary, and the Prehistory of Qualitative Inquiry

Abraham Maslow and Timothy Leary are 2 of the most well-known American psychologists from the mid-20th century. Less well-known, however, is their pioneering methodological work. In this article we explicate their transgressive research, their epistemological visions, and their struggles to enact a more existential, historical, relational, participatory, and experientially focused human science. Using their personal documents, as well as published and unpublished works, we weave their stories to create an assemblage of these unknown, unacknowledged, or forgotten histories. We try to show that, for both Maslow and Leary, the phenomena and questions they sought to understand drove them from the prevailing modernist ethos and toward new ways of thinking and working. In the process, they fashioned methods for, and visions of, science that have striking echoes in the contemporary qualitative traditions—experimenting with unquantified stories and texts as data, with iterative interpretive methods, with participatory research relationships, and with existential and postmodern philosophies of science. Of course, these bold forays into the unsanctioned forward edge of psychological inquiry were disciplined in different ways—expulsion for Leary and assimilation for Maslow, erasure for both—and this also is instructive for us. The experiences of these influential scholars reveal how the challenges and potentials of the use of personal documents in research were (and are) embedded in a broader struggle over the scientific and political value of human experience.
Multi Objective Optimization of Surface Grinding Process by Combination of Response Surface Methodology and Enhanced Non-dominated Sorting Genetic Algorithm

The present study is focused on the multi-objective optimization of performance parameters such as specific energy (u), metal removal rate (MRR) and surface roughness(Ra) obtained in grinding of Al-SiC35P composites. The enhanced elitist non-dominated sorting genetic algorithm (NSGA -II) is used to solve this multi-objective optimization problem. Al-SiC specimens containing 8 vol. %, 10 vol. % and 12 vol. % of silicon carbide particles of mean diameter 35μm, feed and depth of cut were chosen as process variables. A mathematical predictive model for each of the performance parameters was developed using response surface methodology (RSM). Further, an enhanced NSGA-II algorithm is used to optimize the model developed by RSM. Finally, the experiments were carried out to validate the results obtained from RSM and enhanced NSGA-II. The results obtained were in close agreement, which indicates that the developed model can be effectively used for the prediction.
Analyzing online news dissemination via structure learning: An experimental view

Online information dissemination has attracted unprecedented attention with the proliferation of Internet. This paper investigates how news is disseminated through key online media. Key media are defined to include two categories: a) leader media, whose reports will be reproduced by numerous other media; b) source media, serving as the information counselor for leader ones. Through analyzing the appearance of the same report on various online media, we are able to locate key media in news dissemination and predict the path of dissemination. We provide the initial experimental results on real-life datasets, and the results presented in the form of Bayesian network indicate that the unique influence of online media in three different categories during the process of report.
Site-specific biotinylation of RNA molecules by transcription using unnatural base pairs

Direct site-specific biotinylation of RNA molecules was achieved by specific transcription mediated by unnatural base pairs. Unnatural base pairs between 2-amino-6-(2-thienyl)purine (denoted by s) and 2-oxo(1H)pyridine (denoted by y), or 2-amino-6-(2-thiazolyl)purine (denoted as v) and y specifically function in T7 transcription. Using these unnatural base pairs, the substrate of biotinylated-y (Bio-yTP) was selectively incorporated into RNA, opposite s or v in the DNA templates, by T7 RNA polymerase. This method was applied to the immobilization of an RNA aptamer on sensor chips, and the aptamer accurately recognized its target protein. This direct site-specific biotinylation will provide a tool for RNA-based biotechnologies.
Multi-features fusion based CRFs for face segmentation

Face segmentation is quite challenging due to the diversity of hair styles, head poses, clothing, occlusions, and other phenomena. To improve the accuracy of face segmentation from the images with complex scenes, we present a method based on Conditional Random Fields (CRFs) in this paper. The CRFs model is defined on a graph, in which each node corresponds to a superpixel and each edge connects a pair of neighboring superpixels. The features of color and texture are used to define the node energy function, and the position distance and differences of features between adjacent superpixels are used to define the edge energy function. Segmentation is performed by inferring the CRFs model built by fusing node energy function and edge energy function. We evaluate the performance of the proposed method on two unconstrained face databases. Experimental results demonstrate that the proposed method can efficiently partition face images into regions of face, hair, and background.
BVSSD: build built-in versioning flash-based solid state drives

Time-traveling ability, which enables storage state to be reverted to any previous timepoints, is a highly desirable functionality in modern storage systems to ensure storage continuity. Continuous Data Protection (CDP) is a typical time-traveling implementation mechanism. CDP can guard well against software bugs, unintentional errors, malicious attacks, all of which are often beyond the capabilities of traditional periodical backup schemes. Broadly speaking, CDP can be implemented in two different ways, i.e., either integrate it seamlessly to the target file systems or more generally make it sit at the device block level. However, the state-of-the-art CDP implementations suffer from various limitations, e.g., huge implementation complexity, non-trivial performance interference. In this study, we introduce BVSSD, a new block level versioning system specifically designed for the emerging flash-based SSD. BVSSD realizes CDP functionality through positively and usefully exploiting the inherent idiosyncrasies of flash, which is the well-known "no-overwritten" property. Specifically, BVSSD simply keeps track of the SSD FTL metadata changes, which essentially represent the dynamics of the SSD storage state, and restores them to past timepoints to perform recoveries. Compared with existing block-level CDP schemes, BVSSD is much more light-weight, less performance-interfering, easier to realize, and more importantly, it requires no intrusive modifications to the upper file systems and applications. Our trace-driven simulation results with a number of different realistic enterprise-scale workload traces have shown that BVSSD only incurs marginal performance overheads, somewhere between 3% and 8% performance degradation, while with minimum additional RAM requirement, which is an acceptable price for the high reliability that BVSSD can provide. Furthermore, given most of the typcial SSDs deployment scenarios and their ever-increasing capacity trend, BVSSD is realistically poised to be feasible to be deployed in actual situations.
Chromosomal position effects on AAV-mediated gene targeting

The effects of chromosomal position and neighboring genomic elements on gene targeting in human cells remain largely unexplored. To study these, we used a shuttle vector system in which murine leukemia virus (MLV)-based proviral targets present at different chromosomal locations and containing mutations in the neomycin phosphotransferase (neo) gene were corrected by adeno-associated virus (AAV)-mediated gene targeting. Sixteen identical target loci present in HT-1080 human sarcoma cells were all successfully corrected by gene targeting. The gene targeting frequencies varied by as much as 10-fold, and there was a clear bias for correction of one of the targets in clones containing two target sites. The targeting frequency at each site was correlated to the proximity and density of various genomic elements, and we found a significant association of higher targeting frequencies at loci near a subset of dinucleotide microsatellite repeats (r = –0.55, P < 0.05), in particular GT repeats (r = –0.87, P < 0.0001). Additionally, there was a correlation between meiotic recombination rates and targeting frequencies at the target loci (r = 0.52, P < 0.05). There was no correlation between surrounding chromosomal transcription units and targeting frequencies. Our results indicate that certain chromosomal positions are preferred sites for gene targeting in human cells.
Predictive Data Grouping and Placement for Cloud-Based Elastic Server Infrastructures

Workload variations on Internet platforms such as YouTube, Flickr, LastFM require novel approaches to dynamic resource provisioning in order to meet QoS requirements, while reducing the Total Cost of Ownership (TCO) of the infrastructures. The economy of scale promise of cloud computing is a great opportunity to approach this problem, by developing elastic large scale server infrastructures. However, a proactive approach to dynamic resource provisioning requires prediction models forecasting future load patterns. On the other hand, unexpected volume and data spikes require reactive provisioning for serving unexpected surges in workloads. When workload can not be predicted, adequate data grouping and placement algorithms may facilitate agile scaling up and down of an infrastructure. In this paper, we analyze a dynamic workload of an on-line music portal and present an elastic Web infrastructure that adapts to workload variations by dynamically scaling up and down servers. The workload is predicted by an autoregressive model capturing trends and seasonal patterns. Further, for enhancing data locality, we propose a predictive data grouping based on the history of content access of a user community. Finally, in order to facilitate agile elasticity, we present a data placement based on workload and access pattern prediction. The experimental results demonstrate that our forecasting model predicts workload with a high precision. Further, the predictive data grouping and placement methods provide high locality, load balance and high utilization of resources, allowing a server infrastructure to scale up and down depending on workload.
Evaluating usability and precision of visual search engine

In this paper, we evaluate a visual search engine that is designed to solve the real problem of browsing and searching for documents in a vast repository of colleges/courses located at Western Kentucky University (WKU). The architectural design of this interface combines Formal Concept Analysis (FCA) with Semantic Factoring to decompose complex, vast concepts into their primitives in order to develop knowledge representation for the HyperManyMedia plat-form. The main objective of this study is to test: (a) the efficiency of ranking the documents using precision, and (b) the usability of the visual search engine. This approach has been implemented and used by online students at WKU.