Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-standard Hubbard models in optical lattices: a review

Originally, the Hubbard model was derived for describing the behavior of strongly correlated electrons in solids. However, for over a decade now, variations of it have also routinely been implemented with ultracold atoms in optical lattices, allowing their study in a clean, essentially defect-free environment. Here, we review some of the vast literature on this subject, with a focus on more recent non-standard forms of the Hubbard model. After giving an introduction to standard (fermionic and bosonic) Hubbard models, we discuss briefly common models for mixtures, as well as the so-called extended Bose–Hubbard models, that include interactions between neighboring sites, next-neighbor sites, and so on. The main part of the review discusses the importance of additional terms appearing when refining the tight-binding approximation for the original physical Hamiltonian. Even when restricting the models to the lowest Bloch band is justified, the standard approach neglects the density-induced tunneling (which has the same origin as the usual on-site interaction). The importance of these contributions is discussed for both contact and dipolar interactions. For sufficiently strong interactions, the effects related to higher Bloch bands also become important even for deep optical lattices. Different approaches that aim at incorporating these effects, mainly via dressing the basis, Wannier functions with interactions, leading to effective, density-dependent Hubbard-type models, are reviewed. We discuss also examples of Hubbard-like models that explicitly involve higher p orbitals, as well as models that dynamically couple spin and orbital degrees of freedom. Finally, we review mean-field nonlinear Schrödinger models of the Salerno type that share with the non-standard Hubbard models nonlinear coupling between the adjacent sites. In that part, discrete solitons are the main subject of consideration. We conclude by listing some open problems, to be addressed in the future.
Interaction Protein for Cytohesin Exchange Factors 1 (IPCEF1) Binds Cytohesin 2 and Modifies Its Activity*

The ADP-ribosylation factor 6 (ARF6) small GTPase functions as a GDP/GTP-regulated switch in the pathways that stimulate actin reorganization and membrane ruffling. The formation of active ARF6GTP is stimulated by guanine nucleotide exchange factors (GEFs) such as cytohesins, which translocate to the plasma membrane in agonist-stimulated cells by binding the lipid second messenger phosphatidylinositol 3,4,5-trisphosphate through the pleckstrin homology domain with subsequent ARF6 activation. Using cytohesin 2 as bait in yeast two-hybrid screening, we have isolated a cDNA encoding a protein termed interaction protein for cytohesin exchange factors 1 (IPCEF1). Using yeast two-hybrid and glutathione S-transferase pull-down assays coupled with deletion mutational analysis, the specific domains required for the cytohesin 2-IPCEF1 interaction were mapped to the coiled-coil domain of cytohesin 2 and the C-terminal 121 amino acids of IPCEF1. IPCEF1 also interacts with the other members of the cytohesin family of ARF GEFs, suggesting that the interaction with IPCEF1 is highly conserved among the cytohesin family of ARF GEFs. The interaction of cytohesin 2 and IPCEF1 in mammalian cells was demonstrated by immunoprecipitation. Immunofluorescence analysis revealed that IPCEF1 co-localizes with cytohesin 2 to the cytosol in unstimulated cells and translocates to the plasma membrane via binding to cytohesin 2 in epidermal growth factor-stimulated cells. However, a deletion mutant of IPCEF1 that lacks the cytohesin 2 binding site failed to co-migrate with cytohesin 2 to the membrane in stimulated cells. The functional significance of the IPCEF1-cytohesin 2 interaction is demonstrated by showing that IPCEF1 increases the in vitro and in vivo stimulation of ARFGTP formation by cytohesin 2.
ANN for prognosis of abdominal pain in childhood : use of fuzzy modelling for convergence estimation

This paper focuses in two parallel objectives. First it aims in presenting a series of Artificial Neural Network models that are capable of performing prognosis of abdominal pain in childhood. Clinical medical data records have been gathered and used towards this direction. Its second target is the presentation and application of an innovative fuzzy algebraic model capable of evaluating Artificial Neural Networks’ performance [1]. This model offers a flexible approach that uses fuzzy numbers, fuzzy sets and various fuzzy intensification and dilution techniques to perform assessment of neural models under different perspectives. It also produces partial and overall evaluation indices. The produced ANN models have proven to perform the classification with significant success in the testing phase with first time seen data.
0 Returns to Capital and Informality

In this paper we analyze the pattern of returns to capital in formal and informal manufacturing sector in Ethiopia using a rich panel dataset of formal sector manufacturing firms for the period 1996-2006 and two rounds of repeated cross-sectional data from the urban informal sector firms. Returns to capital in the informal sector are higher than that of the formal sector potentially explaining the growing informal sector in Ethiopia. Median return to capital in the formal sector is between 17-21% while that of the informal sector ranges from 51-122 percent. However investment in the informal sector is limited by the evidence that returns to capital is declining as the amount of time owner spent in the enterprise declines. This restricts the possibility of formal and informal firms to establish many identical firms in the informal sector to take advantage of the higher returns to capital in the sector. Unlike the prediction of poverty trap hypothesis, we find that returns to capital are decreasing with capital stock creating the opportunity for small firms to grow by re-investing their profit. Parametric and semi-parametric regression techniques are used to study the magnitude and pattern of returns to capital. Using dynamic panel re-investment models in the formal sector, we found that small firms with higher returns to capital make significantly higher re-investment though the coefficient estimates are low.
Uniform Selection as a Primary Force Reducing Population Genetic Differentiation of Cavitation Resistance across a Species Range

Background Cavitation resistance to water stress-induced embolism determines plant survival during drought. This adaptive trait has been described as highly variable in a wide range of tree species, but little is known about the extent of genetic and phenotypic variability within species. This information is essential to our understanding of the evolutionary forces that have shaped this trait, and for evaluation of its inclusion in breeding programs. Methodology We assessed cavitation resistance (P 50), growth and carbon isotope composition in six Pinus pinaster populations in a provenance and progeny trial. We estimated the heritability of cavitation resistance and compared the distribution of neutral markers (F ST) and quantitative genetic differentiation (Q ST), for retrospective identification of the evolutionary forces acting on these traits. Results/Discussion In contrast to growth and carbon isotope composition, no population differentiation was found for cavitation resistance. Heritability was higher than for the other traits, with a low additive genetic variance (h2 ns = 0.43±0.18, CVA = 4.4%). Q ST was significantly lower than F ST, indicating uniform selection for P 50, rather than genetic drift. Putative mechanisms underlying QST
Process-Parameter Optimization for Fused Deposition Modeling Based on Taguchi Method

Abstract: Aiming at such performance indexes in fused deposition modeling (FDM) as dimensional error (DE) and warpage deformation (WD), and selecting four process parameters, wire-width compensation (A), extrusion velocity (B), filling velocity (C), and layer thickness (D), as controlling factors, this paper optimized the four process parameters by means of Taguchi method combined with fuzzy comprehensive evaluation. The optimization results show that the parameter with the most significant influence on the performance indexes is A, followed by B, D, and C, and that the optimum combination of the process parameters is A1B1C2D3.
Identification and detection of Trypanosoma cruzi by using a DNA amplification fingerprint obtained from the ribosomal intergenic spacer

We designed a PCR assay targeted on repeated elements of the ribosomal intergenic spacer which produces highly polymorphic DNA band patterns for different strains of Trypanosoma cruzi. By labeling the PCR products with digoxigenin and by chemiluminescence detection, we improved the assay sensitivity by three orders of magnitude to get T. cruzi strain fingerprints in feces of the trypanosome-infected triatomine bug vector. We also developed a capture assay for the digoxigenin-labeled PCR products that allowed us to detect T. cruzi in triatomine bug vector feces and in human serum samples with a solid support.
Scalable Electrical Compact Modeling for Graphene FET Transistors

A new scalable electrical compact model for the Graphene FET devices is proposed. Starting from Thiele's quasianalytical model, the equations are modified to be fully compatible with SPICE-like circuit simulation. Compared to Meric et al. model, the charge model is improved. This large signal model has been implemented in Verilog-A code and can be used for simulation in a standard circuit design environment such as Cadence or ADS. This model has been verified using different measurements from the literature, and furthermore, its scalability is demonstrated.
Eyes Wide Shut: Amygdala Mediates Eyes-Closed Effect on Emotional Experience with Music

The perceived emotional value of stimuli and, as a consequence the subjective emotional experience with them, can be affected by context-dependent styles of processing. Therefore, the investigation of the neural correlates of emotional experience requires accounting for such a variable, a matter of an experimental challenge. Closing the eyes affects the style of attending to auditory stimuli by modifying the perceptual relationship with the environment without changing the stimulus itself. In the current study, we used fMRI to characterize the neural mediators of such modification on the experience of emotionality in music. We assumed that closed eyes position will reveal interplay between different levels of neural processing of emotions. More specifically, we focused on the amygdala as a central node of the limbic system and on its co-activation with the Locus Ceruleus (LC) and Ventral Prefrontal Cortex (VPFC); regions involved in processing of, respectively, ‘low’, visceral-, and ‘high’, cognitive-related, values of emotional stimuli. Fifteen healthy subjects listened to negative and neutral music excerpts with eyes closed or open. As expected, behavioral results showed that closing the eyes while listening to emotional music resulted in enhanced rating of emotionality, specifically of negative music. In correspondence, fMRI results showed greater activation in the amygdala when subjects listened to the emotional music with eyes closed relative to eyes open. More so, by using voxel-based correlation and a dynamic causal model analyses we demonstrated that increased amygdala activation to negative music with eyes closed led to increased activations in the LC and VPFC. This finding supports a system-based model of perceived emotionality in which the amygdala has a central role in mediating the effect of context-based processing style by recruiting neural operations involved in both visceral (i.e. ‘low’) and cognitive (i.e. ‘high’) related processes of emotions.
Reversion of resistance to oxaliplatin by inhibition of p38 MAPK in colorectal cancer cell lines: involvement of the calpain / Nox1 pathway

Oxaliplatin is a major treatment for metastatic colorectal cancer, however its effectiveness is greatly diminished by the development of resistances. Our previous work has shown that oxaliplatin efficacy depends on the reactive oxygen species (ROS) produced by Nox1. In this report, we investigated Nox1 involvement in the survival mechanisms of oxaliplatin resistant cell lines that we have selected. Our results show that basal ROS production by Nox1 is increased in resistant cells. Whereas the transitory Nox1-dependent production of superoxide contributes to the cytotoxicity of oxaliplatin in sensitive cells, oxaliplatin treatment of resistant cells leads to a decrease in the production of superoxide associated with an increase of H2O2 and a decreased cytotoxicity of oxaliplatin. We have shown that calpains regulate differently Nox1 according to the sensitivity of the cells to oxaliplatin. In sensitive cells, calpains inhibit Nox1 by cleaving NoxA1 leading to a transient ROS production necessary for oxaliplatin cytotoxic effects. In contrast, in resistant cells calpain activation is associated with an increase of Nox1 activity through Src kinases, inducing a strong and maintained ROS production responsible for cell survival. Using a kinomic study we have shown that this overactivation of Nox1 results in an increase of p38 MAPK activity allowing the resistant cells to escape apoptosis. Our results show that the modulation of Nox1 activity in the context of anticancer treatment remains complex. However, a strategy to maximize Nox1 activation while inhibiting the p38 MAPK-dependent escape routes appears to be an option of choice to optimize oxaliplatin efficiency.
Joint User Pairing, Subchannel, and Power Allocation in Full-Duplex Multi-User OFDMA Networks

In this paper, the resource allocation and scheduling problem for a full-duplex (FD) orthogonal frequency-division multiple-access network is studied where an FD base station simultaneously communicates with multiple pairs of uplink (UL) and downlink (DL) half-duplex (HD) users bidirectionally. In this paper, we aim to maximize the network sum-rate through joint UL and DL user pairing, OFDM subchannel assignment, and power allocation. We formulate the problem as a non-convex optimization problem. The optimal algorithm requires an exhaustive search, which will become prohibitively complicated as the numbers of users and subchannels increase. To tackle this complex problem more efficiently, we formulate the user-pairing and subchannel allocation problem as a three-sided matching problem, and propose a novel low-complexity near-optimal matching algorithm. The algorithm is analyzed, and we prove that it converges to a stable matching. Simulation results show that the FD scheme can significantly improve the spectrum efficiency compared with the HD scheme. The proposed algorithm performs very close to the optimal algorithm, and significantly outperforms other resource allocation schemes.
A Short-Term and Long-Term Learning Approach for Content-Based Image Retrieval

This paper proposes a short-term and long-term learning approach for content-based image retrieval. The proposed system integrates the user's positive and negative feedback from all iterations to construct a semantic space to remember the user's intent in terms of the high-level hidden semantic features. The short-term learning further refines the query by updating its associated weight vector using both positive and negative examples together with the long-term-learning-based semantic space. The similarity score is computed as the dot product between the query weight vector and the high-level features of each image stored in the semantic space. Our proposed retrieval approach demonstrates a promising retrieval performance for an image database of 6000 general-purpose images from COREL, as compared with the conventional retrieval systems
Plasma carotenoid levels and cognitive performance in an elderly population: results of the EVA Study.

BACKGROUND The hypothesis of carotenoids having a preventive role in cognitive impairment is suggested by their antioxidant properties. METHODS We examined, in a cross-sectional analysis, the relationship between cognitive performance (assessed by the Mini-Mental State Examination, Trail Making Test Part B, Digit Symbol Substitution, Finger Tapping Test, and Word Fluency Test) and different plasma carotenoids (lutein, zeaxanthin, beta-cryptoxanthin, lycopene, alpha-carotene, and trans-beta-carotene and cis-beta-carotene) in a healthy elderly population (the EVA,"Etude du Vieillissement Artériel," study; n = 589, age = 73.5 +/- 3 years). RESULTS Logistic regression showed that participants with the lowest cognitive functioning (<25th percentile) had a higher probability of having low levels of specific plasma carotenoids (<1st quartile): lycopene and zeaxanthin. For zeaxanthin, odds ratios (ORs) were as follows: OR(DSS) = 1.97 (95% confidence interval [CI] = 1.21-3.20), OR(FTT) = 1.70 (CI = 1.05-2.74), and OR(WFT) = 1.82 (CI = 1.08-3.07); for lycopene, OR(DSS) = 1.93 (CI = 1.20-3.12) and OR(TMTB) = 1.64 (CI = 1.04-2.59). CONCLUSION Even if it is not possible to affirm if these low levels of carotenoids precede or are the consequence of cognitive impairment, our results suggest that low carotenoid levels could play a role in cognitive impairment. The biological significance of our findings needs further research.
Strategic Voting and Social Networks: (Doctoral Consortium)

The effects of social networks are pervasive in everyday life, from the subtle effects on mundane, moment-to-moment choices, to political revolutions driven through social media. The last few years have seen a rapid growth of interest in this area from researchers within artificial intelligence and multiagent systems communities. Until recently, relatively little attention has been paid on understanding the effects of these networks on the social aggregation of opinions. My main research question I wish to consider is the impact of social networks on social choice. Specifically, how does communication within social networks affect the strategic voting behavior of the population, and how does it affect the voting outcome?
Effective Hardware Based Data Prefetching for High-Performance Processors

Memory latency and bandwidth are progressing at a much slower pace than processor performance. In this paper, we describe and evaluate the performance of three variations of a hardware function unit whose goal is to assist a data cache in prefetching data accesses so that memory latency is hidden as often as possible. The basic idea of the prefetching scheme is to keep track of data access patterns in a reference prediction table (RPT) organized as an instruction cache. The three designs differ mostly on the timing of the prefetching. In the simplest scheme (basic), prefetches can be generated one iteration ahead of actual use. The lookahead variation takes advantage of a lookahead program counter that ideally stays one memory latency time ahead of the real program counter and that is used as the control mechanism to generate the prefetches. Finally the correlated scheme uses a more sophisticated design to detect patterns across loop levels. These designs are evaluated by simulating the ten SPEC benchmarks on a cycle-by-cycle basis. The results show that 1) the three hardware prefetching schemes all yield significant reductions in the data access penalty when compared with regular caches, 2) the benefits are greater when the hardware assist augments small on-chip caches, and 3) the lookahead scheme is the preferred one cost-performance wise. >
Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data Mining

We describe current research in temporal, spatial, and spatiotemporal data mining. In these types of data mining, a model of time, space, or space-time plays a nontrivial role. As an example of current research, we describe our MegaMiner prototype software. The DGG-Discover 5.2 module of MegaMiner is based on expected distribution domain generalization graphs (EDDGGs), which allow detailed domain knowledge about temporal and spatial generalization relationships to be specified, and then applied during the data mining process. As well, user expectations about the data can be specified and updated during the mining process. We illustrate the current state of the MegaMiner software by applying it to a previously unseen data set, describing the weather of the province of Saskatchewan for the period 1900 to 1949. We were able to find temporal and spatial relationships, but not spatio-temporal ones.
Nucleic Acid Extraction, Amplification, and Detection on Si-Based Microfluidic Platforms

This paper gives a brief overview of silicon-based microfluidic platforms developed over the years by our group for the extraction, amplification, and detection of nucleic acids. Extraction of both genomic and viral nucleic acids from whole blood has been demonstrated on Si-based microfluidics. Rapid amplification has been achieved by polymerase chain reaction in Si-based thermal reactors (micro-PCR). Detection has been realized by a chip-to-chip integration of the micro-PCR and micro capillary electrophoresis (micro-CE).
Resource-Aware Mining with Variable Granularities in Data Streams

For data stream applications, both approximation and adaptability are important issues for effective mining. We explore in this paper a fundamental problem that how the limited resources, e.g., memory space and computation power, can be well utilized to produce accurate estimates. Two important features for tracking mined patterns with properly utilized resources are examined. The first issue is temporal granularity which refers to the phenomenon that as time advances, people are more interested in recent events, meaning that more resources can be utilized to explore more recent data with finer granularities. Second, with the mining task of discovering frequent temporal patterns, more resources are expected to be allocated to the processing of those borderline patterns whose statistics, e.g., occurrence frequencies, are close to the specified threshold so as to have proper frequent itemset identification. This feature is called mining with support count granularity. Consequently, algorithm RAM-DS (ResourceAware Mining for Data Streams) is designed to not only reduce the memory required for data storage but also retain good approximation of target time series. Experimental results have shown that the memory required for storing significant wavelet coefficients is very small and the quality of approximation is stable when performing incremental data updates, indicating that algorithm RAM-DS is feasible and suitable for adaptive mining in data streams.