Miracle-dz/UniFAR-FTUs huggingface dataset:
Yield and Reliability Improvement Techniques for Emerging Nonvolatile STT-MRAM

The emerging spin transfer torque magnetic random access memory (STT-MRAM) promises many attractive features, such as nonvolatile, high speed and low power etc, which enable it to be a promising candidate for the next-generation logic and memory circuits. However with the continuous scaling technology process, the chip yield and reliability of STT-MRAM face severe challenges due to the increasing permanent and transient faults. Due to the intrinsic fault features and the targeted application requirements of STT-MRAM, traditional fault tolerant design solutions, such as error correction code (ECC), redundancy repair (RR), and fault masking (FM) techniques, cannot be employed straightforwardly for STT-MRAM. In this paper, we propose a synergistic technique framework, named sECC, that integrates both the ECC and FM techniques to address simultaneously the permanent and transient faults. With such approach, permanent faults are masked while transient faults are corrected with the same codeword. Moreover taking into consideration the fact that most permanent faults are sparse [about 60%-70% single isolated faults (SIFs)], we propose further integrating the RR and sECC (named iRRsECC) to optimize the system performance. In this scenario, all the SIFs are masked and the transient faults are corrected with the proposed sECC, while other permanent faulty types (e.g., faulty rows or columns) are repaired with redundant rows or columns. A simulation tool is developed to evaluate the proposed techniques and the evaluation results show their good performance in terms of repair rate and hardware overhead.
Postexposure immunization with modified vaccinia virus Ankara or conventional Lister vaccine provides solid protection in a murine model of human smallpox.

BACKGROUND Decades after the cessation of smallpox vaccination, the potential of the deliberate release of pathogenic orthopoxviruses has forced a reconsideration of using these extremely efficient human vaccines. Scenarios of sudden biothreats have prompted demand for rapidly protective vaccination. However, the feasibility of short-term vaccination (i.e., vaccination shortly before exposure) with vaccinia virus (VACV) is uncertain. METHODS We tested the rapid protective capacity of vaccines based on VACV strain Lister (VACV-Lister) and on modified VACV Ankara (MVA) in different mouse models, comparing lethal infections with VACV strain Western Reserve (VACV-WR) or ectromelia virus (ECTV). RESULTS In contrast to VACV-WR challenge, we found extended incubation periods after ECTV challenge, allowing successful therapeutic immunization with VACV-Lister and MVA when applied 2-3 days after exposure. Rapid protection from respiratory tract ECTV infection was significantly affected by vaccine dose and was associated with occurrence of poxvirus-specific antibodies. Vaccinations in type I interferon receptor-deficient mice were protective, whereas recombination activating gene 1-deficient mice lacking mature T and B cells failed to mount immunity after short-term vaccination, confirming an essential role of adaptive immune responses. CONCLUSIONS ECTV infection in mice models the course of human smallpox. Our data provide evidence to substantiate historical data on the usefulness of postexposure vaccination with conventional VACV and the new candidate MVA to protect against fatal orthopoxvirus infections.
TOWARD A SECURE THERAPEUTIC BASE: THE RELATIONSHIP BETWEEN ADULT ATTACHMENT PATTERN AND THEORETICAL ORIENTATION AMONG CLINICAL SOCIAL WORKERS

In psychotherapy the relationship between social worker and client, or the working alliance, is thought to be the most robust predictor of therapeutic outcomes. The social worker’s attachment pattern impacts his or her ability to form an effective working alliance, utilize countertransference, cope with stress and use social supports. There is insufficient focus on how social worker attachment patterns may inform the social worker’s chosen theoretical orientation. Theoretical orientation serves as the foundation for clinical practice and provides a framework with which to view the client, the presenting problems and possible interventions. A theoretical orientation aligned with personal values and beliefs has been linked to job satisfaction, increased therapeutic efficacy, and reduced burnout. This study explored the relationship between adult attachment pattern and theoretical orientation among 170 clinical social workers providing psychotherapy in outpatient settings. The Experience in Close Relationships Scale (ECR-R), the Theoretical Orientation Profile Scale (TOPS-R), and the Theoretical Evaluation Self Test (TEST) were used in the online survey design. Findings highlight the large number of clinical social workers with fearful, or disorganized, attachment patterns. No relationship was found between attachment pattern and theoretical orientation, indicating that perhaps clinical social workers are not actively engaging the use of self when choosing theoretical orientations. Careful assessment of social worker attachment patterns needs to be incorporated into graduate education and ongoing clinical practice. Awareness of the social worker’s attachment pattern may serve as a protective factor with regard to career satisfaction, clinical effectiveness, and reduction of burnout. Degree Type Dissertation Degree Name Doctor of Social Work (DSW) First Advisor Katherine C. Ledwith, DSW Second Advisor Jeffrey Applegate, PhD
Designing P2P Networks Tolerant to Attacks and Faults Based on Bimodal Degree Distribution

Recently, in contrast with the centralized net- works (e.g., traditional client/server systems), the distributed networks such as Peer-to-Peer (P2P) networks and grid networks have attracted much attention due to their scal- ability. While the distributed networks have the advantage of allowing the node(s) to join or leave the network easily, the issue of lack of resiliency to both attacks and faults still remains. In this paper, we classify the existing distributed networks based on their degree distributions. Then, we demonstrate that they are not resilient to attacks and/or faults. For example, unstructured P2P networks, which have a power-law degree distribution, are vulnerable to attacks such as DOS. To address and resolve this issue, we propose a method to construct a network following bimodal degree distribution, which is robust to deal with both attacks and faults. Performance evaluation is conducted through computer simulations, which show that the proposed method can achieve higher resilience compared with other existing networking approaches.
FRAME DRAGGING AND THE KINEMATICS OF GALACTIC-CENTER STARS

We calculate the effects of frame dragging on the Galactic-Center stars. Assuming the stars are only slightly relativistic, we derive an approximation to the Kerr metric, which turns out to be a weak-field Schwarzschild metric plus a frame-dragging term. By numerically integrating the resulting geodesic equations, we compute the effect on Keplerian elements and the kinematics. We find that the kinematic effect at pericenter passage is proportional to (a(1 − e2))−2. For known Galactic-center stars it is of order 10 m s−1. If observed, this would provide a measurement of the spin of the black hole.
PMR-reduction for continuous time OFDM transmit signals by selected mapping

In order to cope with the problems of multipath propagation in communication systems, OFDM is currently the most popular choice amongst digital modulation schemes. The main drawback of OFDM is the high peak-to-average power ratio (PAPR), which PAPR necessitates a highly linear high power amplifier (HPA) in Class-A mode to avoid out-of-band radiation. This kind of amplifiers usually suffers from a bad power efficiency. A recent approach is to deploy different amplifier architectures with potentially higher power efficiencies, such as polar amplifiers. These kinds of amplifiers necessitate signals with low peak-to-minimum power ratio (PMR). In this paper, the PMR of the continuous time transmit signal is analyzed and reduced via Selected Mapping (SLM). To achieve this reduction in the domain of digital signal processing an approximation method for the PMR is derived.
Action Recognition Using Spatio-Temporal Distance Classifier Correlation Filter

The problem of recognizing human actions is characterized by complex dynamics and strong variations in their executions. Despite this inconvenience, space-time correlations provide valuable clues for their discrimination. Therefore, space-time correlators like \emph{Maximum Average Correlation Height} (MACH) filters have successfully been used for action recognition with encouraging results. However, their utility is challenged due to number of factors: (i) these filters are trained only for one class at a time and separate filters are required for each class increasing computational overhead, (ii) these filters simply take average of similar action instances and behave no better than average filters and (iii) misaligned action datasets create problems for these filters as they are not shift-invariant. In this paper, we address these issues by posing action recognition as a multi-class discrimination problem and propose a \emph{single} 3D frequency domain filter, named Action ST-DCCF for multiple action classes that mitigates inherent discrepancies of correlation filters. It presents a different interpretation of correlation filters as a method of applying spatio-temporal transformation to the data rather than simply minimizing correlation energy across all possible shifts. Experiments on a variety of action datasets are performed to evaluate our approach. Experimental results are comparable to the existing action recognition approaches.
On the ring structure of spark characters

We give a new description of the ring structure on the differential characters of a smooth manifold via the smooth hyperspark complex. We show the explicit product formula, and as an application, calculate the product for differential characters of the unit circle. Applying the presentation of spark classes by smooth hypersparks, we give an explicit construction of the isomorphism between groups of spark classes and the $(p,p)$ part of smooth Deligne cohomology groups associated to a smooth manifold. We then give a new direct proof that this is an isomorphism of ring structures.
Social Media and Everyday Politics

From selfies and memes to hashtags and parodies, social media are used for mundane and personal expressions of political commentary, engagement, and participation. The coverage of politics reflects the social mediation of everyday life, where individual experiences and thoughts are documented and shared online. In Social Media and Everyday Politics, Tim Highfield examines political talk as everyday occurrences on Twitter, Facebook, blogs, Tumblr, Instagram, and more. He considers the personal and the political, the serious and the silly, and the everyday within the extraordinary, as politics arises from seemingly banal and irreverent topics. The analysis features international examples and evolving practices, from French blogs to Vines from Australia, via the Arab Spring, Occupy, #jesuischarlie, Eurovision, #blacklivesmatter, Everyday Sexism, and #illridewithyou. This timely book will be a valuable resource for students and scholars in media and communications, internet studies, and political science, as well as general readers keen to understand our contemporary media and political contexts.
A note on the translation of conceptual data models into description logics: disjointness and covering assumptions

Conceptual modeling is nowadays mostly done using languages such as Entity-Relationship (ER) Models, Unified Modeling Language (UML), and Object-Role Modeling (ORM). These models are used to depict the ontological organization of relevant concepts or entities. Such formalisms share a common modeling approach, based on the notions of class or entity and the relations or associations between classes or entities. Recent developments in knowledge representation using logic-based ontologies have created new possibilities for conceptual data modeling. It also raises the question of how existing conceptual models using ER, UML or ORM could be translated into Description Logics (DLs), a family of logics that have proved to be particularly appropriate for formalizing ontologies and reasoning about them. Given a conceptual data model, two assumptions are usually made that are not explicitly stated but need to be clarified for its DL translation: (1) disjointness assumption: all the classes are to be assumed pairwise disjoint if not specified otherwise; and (2) covering assumption: the content of every class must correspond to the union of its immediate subclasses (this includes the assumption that we do not consider anything apart from what is expressed in the model). In this paper we propose two simple procedures to assist modelers with integrating these assumptions into their models, thereby allowing for a more complete translation into DLs.
FHMS: Fuzzy Logic-Based Handoff Management Scheme for Wireless Network

Error-free and fast handoff support in excessive mobile world is challenging issue. Various earlier related works require improvement of QoS, reduction of packet loss advanced support of fast handoff etc. In this paper we propose one simple, flexible, modular and inexpensive fuzzy logic-based handoff management scheme FHMS for wireless networks that reduces packet losses and forced termination probability while not increasing call-blocking probability considerably. It also attempts to raise the accuracy of system behavioral representation and increasing precision. Fast handoff support, QoS enhancement, location update cycle time reduction, performance advancement are also parts of our effort. The simulation of it also supports its capability and potential .
A New Generation of Brain-Computer Interface Based on Riemannian Geometry

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.
Switching head group selectivity in mammalian sphingolipid biosynthesis by active-site-engineering of sphingomyelin synthases

SM is a fundamental component of mammalian cell membranes that contributes to mechanical stability, signaling, and sorting. Its production involves the transfer of phosphocholine from phosphatidylcholine onto ceramide, a reaction catalyzed by SM synthase (SMS)1 in the Golgi and SMS2 at the plasma membrane. Mammalian cells also synthesize trace amounts of the SM analog, ceramide phosphoethanolamine (CPE), but the physiological relevance of CPE production is unclear. Previous work revealed that SMS2 is a bifunctional enzyme producing both SM and CPE, whereas a closely related enzyme, SMS-related protein (SMSr)/SAMD8, acts as a monofunctional CPE synthase in the endoplasmic reticulum. Using domain swapping and site-directed mutagenesis on enzymes expressed in defined lipid environments, we here identified structural determinants that mediate the head group selectivity of SMS family members. Notably, a single residue adjacent to the catalytic histidine in the third exoplasmic loop profoundly influenced enzyme specificity, with Glu permitting SMS-catalyzed CPE production and Asp confining the enzyme to produce SM. An exchange of exoplasmic residues with SMSr proved sufficient to convert SMS1 into a bulk CPE synthase. This allowed us to establish mammalian cells that produce CPE rather than SM as the principal phosphosphingolipid and provide a model of the molecular interactions that impart catalytic specificity among SMS enzymes.
Recent perspectives on the delivery of biologics to back of the eye

ABSTRACT Introduction: Biologics are generally macromolecules, large in size with poor stability in biological environments. Delivery of biologics to tissues at the back of the eye remains a challenge. To overcome these challenges and treat posterior ocular diseases, several novel approaches have been developed. Nanotechnology-based delivery systems, like drug encapsulation technology, macromolecule implants and gene delivery are under investigation. We provide an overview of emerging technologies for biologics delivery to back of the eye tissues. Moreover, new biologic drugs currently in clinical trials for ocular neovascular diseases have been discussed. Areas covered: Anatomy of the eye, posterior segment disease and diagnosis, barriers to biologic delivery, ocular pharmacokinetic, novel biologic delivery system Expert opinion: Anti-VEGF therapy represents a significant advance in developing biologics for the treatment of ocular neovascular diseases. Various strategies for biologic delivery to posterior ocular tissues are under development with some in early or late stages of clinical trials. Despite significant progress in the delivery of biologics, there is unmet need to develop sustained delivery of biologics with nearly zero-order release kinetics to the back of the eye tissues. In addition, elevated intraocular pressure associated with frequent intravitreal injections of macromolecules is another concern that needs to be addressed.
Optical Coherence Tomography – Segmentation Performance and Retinal Thickness Measurement Errors

Optical coherence tomography (OCT) has become an indispensable tool in the assessment of macular pathology in clinical settings and an integral part of many clinical trials. However, as with any imaging technology, some limitations exist. In this review, the author describes and discusses the various causes that might compromise automated retinal thickness measurements. The segmentation software might perform less accurately in the presence of scan artefacts (e.g. ‘out-of-range’, mirror, blink and motion artefacts), a low signal:noise ratio, dense media opacities and specific retinal pathological features (e.g. pigment epithelial detachment, subretinal fluid, fibrotic tissue, hard exudates and full-thickness macular holes). The awareness of the clinician and the particular search for, and recognition of, measurement errors would improve the accuracy of OCT interpretation and should be an integral part of OCT scan analysis.
Biophysics of Risk Aversion Based on Neurotransmitter Receptor Theory

Decision under risk and uncertainty has been attracting attention in neuroeconomics and neuroendocrinology of decision-making. This paper demonstrated that the neurotransmitter receptor theory-based value (utility) function can account for human and animal risk-taking behavior. The theory predicts that (i) when dopaminergic neuronal response is efficiently coupled to the formation of ligand-receptor complex, subjects are risk-aversive (irrespective of their satisfaction level) and (ii) when the coupling is inefficient, subjects are risk-seeking at low satisfaction levels, consistent with risk-sensitive foraging theory in ecology. It is further suggested that some anomalies in decision under risk are due to inefficiency of the coupling between dopamine receptor activation and neuronal response. Future directions in the application of the model to studies in neuroeconomics of addiction and neuroendocrine modulation of risk-taking behavior are discussed.
Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation

Unfixed and fixed postmortem multiple sclerosis (MS) brain is being used to probe pathology underlying quantitative MR (qMR) changes. Effects of fixation on qMR indices in MS brain are unknown. In 15 postmortem MS brain slices T1, T2, MT ratio (MTR), macromolecular proton fraction (fB), fractional anisotropy (FA), and mean, axial, and radial diffusivity (MD, Dax, and Drad) were assessed in white matter (WM) lesions (WML) and normal appearing WM (NAWM) before and after fixation in formalin. Myelin content, axonal count, and gliosis were quantified histologically. Student's t‐test and regression were used for analysis. T1, T2, MTR, and fB obtained in unfixed MS brain were similar to published values obtained in patients with MS in vivo. Following fixation T1, T2 (NAWM, WML) and MTR (NAWM) dropped, whereas fB (NAWM, WML) increased. Compared to published in vivo data all diffusivity measures were lower in unfixed MS brain, and dropped further following fixation (except for FA). MTR was the best predictor of Tmyelin (inversely related to myelin) in unfixed MS brain (r = −0.83; P < 0.01) whereas postfixation T2 (r = 0.92; P < 0.01), T1 (r = 0.89; P < 0.01), and fB (r = −0.86; P < 0.01) were superior. All diffusivity measures (except for Dax in unfixed tissue) were predictors of myelin content. Magn Reson Med 59:268–277, 2008. © 2008 Wiley‐Liss, Inc.
Identification of Motor Imagery Movements from EEG Signals Using Automatically Selected Features in the Dual Tree Complex Wavelet Transform Domain

The decoding of human brain electrical func- tions by electroencephalogram (EEG) signal is the most important step in brain computer interface (BCI) based systems. So, in this paper, an automatic feature selection method has been proposed to classify imagery left and right hand movements from the EEG signals in the Dual Tree Complex Wavelet Transform domain. First, the EEG signals are decomposed into several bands of real and imaginary coefficients and then, some statistical features like Shannon entropy and variance have been calculated. These features are combined into a single feature space and after that optimal features have been selected automatically imposing some feature selection criteria from this combined feature space. The selected features have been shown to be promising to distinguish different kinds of EEG signals by statistical hypothesis testing (e.g., one way ANOVA) as well as graphical analysis (e.g., scatter plots, box plots). Finally, k-nearest neighbor based classifiers are developed using these selected features to identify left and right hand imagery movements. A mean accuracy of 90.00% is achieved in pub- licly available BCI competition II Graz motor imagery data set which is shown to be better than some existing techniques.