Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Analysis of a Flexible Dual Loop Coil and its Experimental Validation for pre-Clinical Magnetic Resonance Imaging of Rodents at 7 T

Abstract A surface radio frequency coil was developed for small animal image acquisition in a pre-clinical magnetic resonance imaging system at 7 T. A flexible coil composed of two circular loops was developed to closely cover the object to be imaged. Electromagnetic numerical simulations were performed to evaluate its performance before the coil construction. An analytical expression of the mutual inductance for the two circular loops as a function of the separation between them was derived and used to validate the simulations. The RF coil is composed of two circular loops with a 5 cm external diameter and was tuned to 300 MHz and 50 Ohms matched. The angle between the loops was varied and the Q factor was obtained from the S11 simulations for each angle. B1 homogeneity was also evaluated using the electromagnetic simulations. The coil prototype was designed and built considering the numerical simulation results. To show the feasibility of the coil and its performance, saline-solution phantom images were acquired. A correlation of the simulations and imaging experimental results was conducted showing a concordance of 0.88 for the B1 field. The best coil performance was obtained at the 90° aperture angle. A more realistic phantom was also built using a formaldehyde-fixed rat phantom for ex vivo imaging experiments. All images showed a good image quality revealing clearly defined anatomical details of an ex vivo rat.
The Kinesin Protein Kif7 Is a Critical Regulator of Gli Transcription Factors in Mammalian Hedgehog Signaling

Kif7 may be the functional equivalent of the Drosophila kinesin-like protein Costal2. A Mammalian Cos2 Homolog Hedgehog (Hh) signaling is an evolutionarily conserved pathway required for tissue patterning and cell fate determination during development. In the absence of Hh, the Drosophila kinesin-like protein Costal2 (Cos2) mediates the proteolytic processing of the transcription factor Cubitus interruptus (Ci) into a repressor of gene transcription. However, in the presence of Hh, Cos2 interacts with the transmembrane protein Smoothened (Smo) and the repressor form of Ci is not produced. Cheung et al. now provide several lines of evidence suggesting that Kif7 functions in a similar capacity to Cos2 in mammalian Hh signaling. As would be expected for a Cos2 homolog, Kif7 interacted with mammalian Gli transcription factors (the mammalian homologs of Ci) and inhibited the activity of Gli2. In addition, genetic ablation of Kif7 in mice altered the abundance of activator and repressor forms of Gli transcription factors and produced skeletal defects reminiscent of those seen in Gli3-deficient mice. Furthermore, genetic analysis revealed that Kif7 functions downstream of Smo to exert positive and negative effects on Hh patterning of the ventral neural tube. These results suggest that Hedgehog signaling in Drosophila and vertebrates is more mechanistically similar than previously suspected. From insects to humans, the Hedgehog (Hh) signaling pathway has conserved roles in embryonic development and tissue homeostasis. However, it has been suggested that the lack of mammalian equivalents of Costal2 (Cos2) contributes to a divergence between the mechanism of Drosophila and mammalian Hh signal transduction. Here, we challenge this view by showing that the kinesin protein Kif7 is a critical regulator of Hh signaling in mice. Similar to Cos2, Kif7 physically interacted with Gli transcription factors and controlled their proteolysis and stability, and acted both positively and negatively in Hh signaling. Thus, Kif7 is a missing component of the mammalian Hh signaling machinery, implying a greater commonality between the Drosophila and mammalian system than the prevailing view suggests.
Expert Based Ecosystem Service Assessment in Coastal and Marine Planning and Management: A Baltic Lagoon Case Study

The assessment of ecosystem services, the benefits humans obtain from ecosystems, is a worldwide topic of growing interest, but examples addressing coastal and marine water are still a small minority. In this study, we carry out an expert based ecosystem service assessment for a concrete case study, the Szczecin (Oder) Lagoon located at the German/Polish border in the Baltic Sea region. We analyze to what extent, in which step and how it can be applied for supporting Integrated Coastal Zone Management (ICZM), the ecological-social-economic assessment within System Approach Frameworks (SAF) and Marine Spatial Planning (MSP). The large, shallow Szczecin Lagoon is heavily eutrophied. Therefore, several alternative measures (scenarios) are under discussion to improve its water quality and its ecological status. Scenarios include a large-scale mussel farm; the establishment of a local mussel farm that removes nutrients via harvest and increases water transparency; artificial mussel beds; groynes to reduce coastal current velocities, favor sediment accumulation, and promote the enlargement of macrophyte beds and other measures to enlarge macrophyte belts and coverage. We adapt and apply a computer aided ecosystem service assessment and visualization tool (ESAT). Eight experts and two student groups apply this tool and carry out an assessment of each scenario compared to the present state of the lagoon. The results show that the scenario assuming measures to enlarge macrophyte belts is perceived as the one that generates the highest additional ecosystem service output. However, more importantly our approach can serve as tool to catch the views of experts, can extract disagreements between experts and misunderstandings in the setup of scenarios as well as services that have highest priority for further consideration. Further, the results are useful for preparing stakeholder discussions and workshops. However, the expert assessments are influenced by the spatial scale of the assessed scenarios. As consequence, the assessment results itself cannot be regarded as a reliable basis for decision making. Today, Integrated Coastal Zone Management ideas and approaches are well reflected and integrated into Marine Spatial Planning and we show that a comparative ecosystem service assessment can be applied in different steps of each concept.
An accumulator model for primes and targets with independent response activation rates: Basic equations for average response times

In response priming tasks, speeded responses are performed toward target stimuli preceded by prime stimuli. Responses are slower and error rates are higher when prime and target are assigned to different responses, compared to assignment to the same response, and those priming effects increase with prime-target SOA. Here, we generalize Vorberg et al.'s (2003) accumulator model of response priming, where response activation is first controlled exclusively by the prime and then taken over by the actual target. Priming thus occurs by motor conflict because a response-inconsistent prime can temporarily drive the process towards the incorrect response. While the original model assumed prime and target signals to be identical in strength, we allow different rates of response activation (cf. Mattler & Palmer, 2012; Schubert et al., 2012). Our model predicts that stronger primes mainly increase priming effects in response times and error rates, whereas stronger targets mainly diminish response times and priming effects.
Type 2 diabetes is negatively associated with Alzheimer's disease neuropathology.

BACKGROUND In cross-sectional and longitudinal studies, type 2 diabetes has been positively associated with the risk of Alzheimer's disease (AD). The present descriptive study compared diabetic and nondiabetic subjects on the severity of neuritic plaques and neurofibrillary tangles (NFTs) in the cerebral cortex and in the hippocampus. METHODS The study included specimens from 385 consecutive autopsies of residents of a nursing home (15.8% diabetics). Mean age at death = 84 years [standard deviation (SD) = 10], 66% were female, Clinical Dementia Rating mean = 3.0 (SD = 1.6), and 32.5% had an APOE4 allele. Additional analyses limited the sample to 268 subjects (14.1% diabetics) without neuropathology other than AD. RESULTS Analyses of covariance controlling for age at death, dementia severity (Clinical Dementia Rating score), and APOE4 allele indicated that diabetics had significantly fewer neuritic plaques (p =.008) and NFTs (p =.047) in the cerebral cortex than did nondiabetics. In the hippocampus, diabetics had significantly lower plaque ratings than did nondiabetics (p =.019), but the lower ratings of NFTs did not achieve statistical significance (p =.082). In the entire sample, diabetics had significantly less AD-associated neuropathology in all four analyses. CONCLUSIONS These results raise the possibility that the varied associations observed between diabetes and AD may be specific to as yet ill-defined subgroups of dementia and diabetic patients or may be more characteristic of younger patients than of those who survive to a mean age of 84 years. Future studies are encouraged to examine a variety of other characteristics such as age that may interact with diabetes affecting the incidence of AD.
Temporal-based feature selection and transfer learning for text categorization

This paper addresses text categorization problem that training data may derive from a different time period from the test data. We present a method for text categorization that minimizes the impact of temporal effects. Like much previous work on text categorization, we used feature selection. We selected two types of informative terms according to corpus statistics. One is temporal independent terms that are salient across full temporal range of training documents. Another is temporal dependent terms which are important for a specific time period. For the training documents represented by independent/dependent terms, we applied boosting based transfer learning to learn accurate model for timeline adaptation. The results using Japanese data showed that the method was comparable to the current state-of-the-art biased-SVM method, as the macro-averaged F-score obtained by our method was 0.688 and that of biased-SVM was 0.671. Moreover, we found that the method is effective, especially when the creation time period of the test data differs greatly from that of the training data.
Sensei: An area-reduction advisor for FPGA high-level synthesis

High-level synthesis (HLS) provides an easy-to-use abstraction for designing hardware circuits. However, standard datatypes in high-level languages are over provisioned for typical applications, incurring extra area since the underlying FPGA hardware can support arbitrary bitwidths. This area inefficiency can be overcome by enabling the use of arbitrary-width datatypes at the source code level. However, this requires that HLS users spend time and effort on examining all program variables and quantifying their area impact, which can be intractable especially with large, complex programs and time-consuming synthesis. We propose Sensei, an advisor that predicts the post-synthesis area savings brought about by reducing bitwidth and presents users with a ranking of program variables and their area impact. Equipped with a convolutional neural network (CNN)-based predictor, Sensei achieves high area-prediction accuracy and enables rapid exploration of area-saving opportunities.
Thermorph: Democratizing 4D Printing of Self-Folding Materials and Interfaces

We develop a novel method printing complex self-folding geometries. We demonstrated that with a desktop fused deposition modeling (FDM) 3D printer, off-the-shelf printing filaments and a design editor, we can print flat thermoplastic composites and trigger them to self-fold into 3D with arbitrary bending angles. This is a suitable technique, called Thermorph, to prototype hollow and foldable 3D shapes without losing key features. We describe a new curved folding origami design algorithm, compiling given arbitrary 3D models to 2D unfolded models in G-Code for FDM printers. To demonstrate the Thermorph platform, we designed and printed complex self-folding geometries (up to 70 faces), including 15 self-curved geometric primitives and 4 self-curved applications, such as chairs, the simplified Stanford Bunny and flowers. Compared to the standard 3D printing, our method saves up to 60% - 87% of the printing time for all shapes chosen.
Confident leakage assessment — A side-channel evaluation framework based on confidence intervals

Cryptographic devices that potentially operate in hostile physical environments need to be secured against side-channel attacks. In order to ensure the effectiveness of the required countermeasures, scientists, developers, and evaluators need efficient methods to test the security level of a device. In this paper we propose a new framework based on confidence intervals that extends established t-test based approaches for test-vector leakage assessment (TVLA). In comparison to previous TVLA approaches the new methodology does not only enable the detection of leakage but can also assert its absence. The framework is robust against noise in the evaluation system and thereby avoids false negatives. These improvements can be achieved without overhead in measurement complexity and with a minimum of additional computational costs compared to previous approaches. We evaluate our method under realistic conditions by applying it to a protected implementation of AES.
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation

The translation of patents or scientiﬁc papers is a key issue that should be helped by the use of statistical machine translation (SMT). In this paper, we propose a method to improve Chinese–Japanese patent SMT by pre-marking the training corpus with aligned bilingual multi-word terms. We automatically extract multi-word terms from monolingual corpora by combining statistical and linguistic ﬁl-tering methods. We use the sampling-based alignment method to identify aligned terms and set some threshold on translation probabilities to select the most promising bilingual multi-word terms. We pre-mark a Chinese– Japanese training corpus with such selected aligned bilingual multi-word terms. We obtain the performance of over 70% precision in bilingual term extraction and a signiﬁcant improvement of BLEU scores in our experiments on a Chinese–Japanese patent parallel corpus.
Enhancement of QoS for fog computing model aspect of robust resource management

The new era of Internet of Everything (IoE) is progressively making items online, but centralized cloud data processing does not scale to desires of the environment. The performance degradation can be seen in the cloud applications which are deadline like health monitoring and which need lesser response time and delay (instigated by transfer of data to cloud and then cloud to application). Fog computing appears as a solution to reduce the network congestion and latency as in this approach cloud is boosted to the area of the network. Fog computing is a model for handling a virtual and extremely distributed environment which delivers network and compute services between cloud data centres and users. In this paper, a refinement of resource management technique is proposed for efficient management of resources which considers execution time.
Optimizing Training-Based Transmission for Correlated MIMO Systems with Hybrid Feedback

In this paper, we consider multiple-input multiple-output (MIMO) communication systems with combined channel covariance feedback (CCF) and channel gain feedback (CGF), hereafter called hybrid CCF-CGF systems. Using an ergodic capacity lower bound as the figure of merit, we investigate the optimal training and data transmission strategies as well as the optimal transmit resource allocation. We prove that the optimal structure for data transmission follows a water-filling solution according to the estimated channel gains, rotated and truncated into the trained eigen-directions. We analytically find the range of the optimal training length. Through numerical evaluations we also show that a closed-form solution of the training power allocation achieves near optimal performance. Finally, we show that the capacity of hybrid CCF-CGF systems can be significantly increased by adding extra transmit antennas without increasing the training resources or feedback overhead.
Combination of debulking single-tract percutaneous nephrolithotomy followed by retrograde intrarenal surgery for staghorn stones in solitary kidneys

Abstract Objective. The aim of this study was to report a therapeutic approach comprising a combination of debulking single-tract percutaneous nephrolithotomy (PCNL) and subsequent retrograde intrarenal surgery (RIRS) for treatment of staghorn stones in patients with solitary kidney. Materials and methods. A retrospective review was undertaken of 56 patients with staghorn stones in a solitary kidney who underwent the above-mentioned combination therapy. PCNL was first performed for stone debulking and RIRS was then used to remove residual stones after an interval of 2–4 days. Data were collected on operative parameters, stone-free rate (SFR), complications and renal functions. Results. The staghorn stones had a mean stone burden of 2548 ± 1028 mm2 (range 1438–3956 mm2). The SFR after 3 months was 89.3%. The overall blood transfusion rate was 7.1%. Complications were observed in nine patients (16.1%), including selective renal artery embolization, fever, gross haematuria and steinstrasse in one, two, three and three cases, respectively. At a mean follow-up of 31 months, available in 38 patients, kidney function was stable in 71%, improved in 26.3% and worsened in 2.7%, according to serum creatinine levels that remained within ±20%, or that increased or decreased outside this range. No patient required haemodialysis. Conclusions. Combining single-tract PCNL with subsequent RIRS was an effective strategic option for treating staghorn stones in solitary kidneys. The method gave an excellent SFR, satisfactory preservation of renal function, reduced bleeding risk and potentially less morbidity than that associated with multiple-tract PCNL.
Diversity reception in Antenna Cancellation based full duplex system for WLAN applications

Full Duplex communication is an emerging research area with tremendous potential for the future of wireless communication. It involves transmission and reception in the same frequency band at the same time slot. Self-Interference is the primary limiting factor of such communication systems, and a variety of methods exist to suppress this phenomenon. Antenna Cancellation is a widely researched technique for Self-Interference Suppression. However, as antenna cancellation involves placing the transmit and receive antennae in the same transceiver close to each other, far-field approximation that was used in existing studies is not accurate. The placement of the receiving antenna for antenna cancellation has to be revised considering the antenna dimensions. The extension of the antenna cancellation scheme to a two receiver diversity system is also discussed. The performance of the resulting system is also evaluated for WLAN frequency bands.
Sexual dimorphism in ALS: Exploring gender-specific neuroimaging signatures

Abstract Our objective was to explore neuroanatomical differences between female and male ALS patients in the context of sexual dimorphism in healthy controls. Fourteen female ALS patients, 13 male ALS patients, 22 healthy male controls and 20 healthy female controls were recruited into a comprehensive neuroimaging study. Cortical thickness measurements and diffusion tensor imaging (DTI) were utilized to explore gender-specific anatomical vulnerability. DTI analysis across all study groups revealed higher fractional anisotropy in association with male gender in the brainstem, cerebellum, fornix, thalamus, anterior forceps and corticospinal tracts accounting for diagnosis and age. While females showed a trend of higher age-adjusted cortical thickness in the right parieto-occipital and left mid-frontal regions, males demonstrated higher cortical thickness in the left lingual and left superior temporal regions, accounting for diagnosis. Significant multifocal white matter differences have also been identified between healthy male and female controls. In conclusion, sexual dimorphism is an overlooked and potentially confounding factor in admixed ALS neuroimaging studies. Our results suggest that gender is an additional dimension of disease heterogeneity in ALS. Given the significant pre- and post-morbid gender differences, we feel that ALS imaging studies should be controlled for gender or, alternatively, single gender studies should be considered.
Magnetic skyrmions for future potential memory and logic applications: Alternative information carriers

Magnetic skyrmions are swirling topological configurations, which are mostly induced by chiral interactions between atomic spins in non-centrosymmetric magnetic bulks or in thin films with broken inversion symmetry. They hold promise as information carriers in future ultra-dense, low-power memory and logic devices owing to the nanocale size and extremely low spin-polarized currents needed to move them. To date, an intense research effort has led to the identification, creation/annihilation, motion and manipulation of skyrmions at room temperature. Meanwhile, a rich variety of skyrmion-based device concepts and prototypes have been proposed, indicating the considerable potential of magnetic skyrmions in future electronic applications. However, current studies mainly focus on physical or principle investigations, whereas the electrical design methodology, implementation and evaluations are still lacking. In this paper, we will bring the readers in the “design, automation and test (DAT) society” the current status and outlook of skyrmions in relation to future potential racetrack memory and neuromorphic computing applications. Most importantly, we also want to evoke the effort from the DAT society to address the challenges, e.g., all-electrical manipulation of skyrmions at room temperature, for the research and development of practical skyrmion-based electronics.
Submodular Optimization Over Sliding Windows

Maximizing submodular functions under cardinality constraints lies at the core of numerous data mining and machine learning applications, including data diversification, data summarization, and coverage problems. In this work, we study this question in the context of data streams, where elements arrive one at a time, and we want to design low-memory and fast update-time algorithms that maintain a good solution. Specifically, we focus on the sliding window model, where we are asked to maintain a solution that considers only the last W items. In this context, we provide the first non-trivial algorithm that maintains a provable approximation of the optimum using space sublinear in the size of the window. In particular we give a 1/3 - ε approximation algorithm that uses space polylogarithmic in the spread of the values of the elements, δ, and linear in the solution size k for any constant ε > 0. At the same time, processing each element only requires a polylogarithmic number of evaluations of the function itself. When a better approximation is desired, we show a different algorithm that, at the cost of using more memory, provides a 1/2 - ε approximation, and allows a tunable trade-off between average update time and space. This algorithm matches the best known approximation guarantees for submodular optimization in insertion-only streams, a less general formulation of the problem. We demonstrate the efficacy of the algorithms on a number of real world datasets, showing that their practical performance far exceeds the theoretical bounds. The algorithms preserve high quality solutions in streams with millions of items, while storing a negligible fraction of them.