Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Coupled Mode Theory for Electromagnetic Resonators

There is recent interest in the inter/intra-element interactions of metamaterial (MM) unit cells. To calculate the effects of these interactions, which can be substantial, an ab-initio general coupled mode equation, in the form of an eigenvalue problem, is derived. The solution of the master equation gives accurate estimates of the coupled frequencies and fields in terms of an arbitrary number of uncoupled modes, having equal or different frequency values. By doing so, the problem size is limited to the number of modes rather than the usually large discretized spatial and temporal domains obtained by rigorous full-wave solvers. Therefore, the method can be considered as an approximate numerical recipe, which determines the behavior of a complex system once its simpler ingredients are known. Besides quantitative analysis, the coupled mode equation proposes a pictorial view of the split rings' hybridization. It can be regarded as the electromagnetic analog of molecular orbital theory. Although being an approximate method, the solution of the eigenvalue problem for different configurations gives valued information and insight about the coupling of MMs unit cells. Unlike rigorous numerical techniques, closed-form expressions can be derived using the coupled mode equation. For instance, it is shown that the behavior of split rings as a function of the relative position and orientation can be systematically explained. This is done by singling out the effect of each relevant parameter such as the coupling coefficient and coupling induced frequency shift coefficients.
Remodeling Lipid Metabolism and Improving Insulin Responsiveness in Human Primary Myotubes

Objective Disturbances in lipid metabolism are strongly associated with insulin resistance and type 2 diabetes (T2D). We hypothesized that activation of cAMP/PKA and calcium signaling pathways in cultured human myotubes would provide further insight into regulation of lipid storage, lipolysis, lipid oxidation and insulin responsiveness. Methods Human myoblasts were isolated from vastus lateralis, purified, cultured and differentiated into myotubes. All cells were incubated with palmitate during differentiation. Treatment cells were pulsed 1 hour each day with forskolin and ionomycin (PFI) during the final 3 days of differentiation to activate the cAMP/PKA and calcium signaling pathways. Control cells were not pulsed (control). Mitochondrial content, 14C lipid oxidation and storage were measured, as well as lipolysis and insulin-stimulated glycogen storage. Myotubes were stained for lipids and gene expression measured. Results PFI increased oxidation of oleate and palmitate to CO2 (p<0.001), isoproterenol-stimulated lipolysis (p = 0.01), triacylglycerol (TAG) storage (p<0.05) and mitochondrial DNA copy number (p = 0.01) and related enzyme activities. Candidate gene and microarray analysis revealed increased expression of genes involved in lipolysis, TAG synthesis and mitochondrial biogenesis. PFI increased the organization of lipid droplets along the myofibrillar apparatus. These changes in lipid metabolism were associated with an increase in insulin-mediated glycogen storage (p<0.001). Conclusions Activation of cAMP/PKA and calcium signaling pathways in myotubes induces a remodeling of lipid droplets and functional changes in lipid metabolism. These results provide a novel pharmacological approach to promote lipid metabolism and improve insulin responsiveness in myotubes, which may be of therapeutic importance for obesity and type 2 diabetes.
Schrödinger invariant solutions of type IIB with enhanced supersymmetry

We construct the Killing spinors for a class of supersymmetric solutions of type IIB supergravity that are invariant under the non-relativistic Schrodinger algebra. The solutions depend on a five-dimensional Sasaki-Einstein space and it has been shown that they admit two Killing spinors. Here we will show that, for generic Sasaki-Einstein space, there are special subclasses of solutions which admit six Killing spinors and we determine the corresponding superisometry algebra. We also show that for the special case that the Sasaki-Einstein space is the round five-sphere, the number of Killing spinors can be increased to twelve.
Mean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks

Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this balanced state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.
Novel Rate Control Scheme Based on System Load and Channel Quality

In this paper, we propose an adaptive uplink rate control scheme based on the reverse activity bit (RAB) setting at base station (BS) and channel quality mobile station (MS) undergoes. According to the RAB and the increment of traffic to pilot ratio (T2P) broadcasted by BS, each MS adaptively adjusts its rate based on the maximum supported T2P in a deterministic manner according to the channel quality indication (CQI). It is proved by system level simulation that the proposed adaptive scheme can enlarge system throughput while guaranteeing quality of service (QoS) and fairness
Tissues Expression, Polymorphisms Identification of FcRn Gene and Its Relationship with Serum Classical Swine Fever Virus Antibody Level in Pigs

Neonatal Fc receptor (FcRn) gene encodes a receptor that binds the Fc region of monomeric immunoglobulin G (IgG) and is responsible for IgG transport and stabilization. In this report, the 8,900 bp porcine FcRn genomic DNA structure was identified and putative FcRn protein included 356 amino acids. Alignment and phylogenetic analysis of the porcine FcRn amino acid sequences with their homologies of other species showed high identity. Tissues expression of FcRn mRNA was detected by real time quantitative polymerase chain reaction (Q-PCR), the results revealed FcRn expressed widely in ten analyzed tissues. One single nucleotide polymorphism (SNP) (HQ026019:g.8526 C>T) in exon6 region of porcine FcRn gene was demonstrated by DNA sequencing analysis. A further analysis of SNP genotypes associated with serum Classical Swine Fever Virus antibody (anti-CSFV) concentration was performed in three pig populations including Large White, Landrace and Songliao Black pig (a Chinese indigenous breed). Our results of statistical analysis showed that the SNP had a highly significant association with the level of anti-CSFV antibody (At d 20; At d 35) in serum (p = 0.008; p = 0.0001). Investigation of expression and polymorphisms of the porcine FcRn gene will help us in further understanding the molecular basis of the antibody regulation pathway in the porcine immune response. All these results indicate that FcRn gene might be regarded as a molecular marker for genetic selection of anti-CSFV antibody level in pig disease resistance breeding programmes.
Neighboring vehicles modeling for tracking across nonoverlapping cameras

Tracking vehicles across nonoverlapping cameras is required by video-based intelligent transportation systems (ITS) to efficiently calculate traffic parameters; such as link travel times and origin/destination counts. In traffic monitoring applications, cameras are usually mounted far from each other to cover wide areas. As such, object features (i.e., color information, shape, and direction) change significantly from one camera to another. These space-time differences raise serious challenges on efficient tracking. In this paper, we have presented a probabilistic model to solve the multicamera tracking task in a network of disjoint view cameras, with attention paid on estimating the density function of different features such as space-time, appearance, and especially neighboring vehicles' relationships. As in highways each group of vehicles usually tend to keep their distances, using the similarity of neighboring vehicles plays an important role in finding the correspondent vehicles. A graph-based approach is used to solve the assignment problem. Experimental results show the efficiency of the proposed tracking method.
An implementation of iterative learning control in industrial production machines

This paper presents one implementation of an ILC (iterative learning control) algorithm in motion-focused production control systems. The implementation is an enhancement, but not a replacement of PID tuning. It is able to improve the system performance in terms of following error without re-tuning PID parameters when the operation conditions, such as mechanical device, load, and motion profile, change. The results are successfully demonstrated in Siemenspsila motion controller - Simotion, and utilized in industrial production lines. The proposed method possesses desirable implementation simplicity. According to the authorspsila knowledge, this paper is the first report of ILC implementation in the area of production machines.
Olfactory discrimination training up-regulates and reorganizes expression of microRNAs in adult mouse hippocampus

Adult male mice (strain C57Bl/6J) were trained to execute nose-poke responses for water reinforcement; then they were randomly assigned to either of two groups: olfactory discrimination training (exposed to two odours with reward contingent upon correctly responding to one odour) or pseudo-training (exposed to two odours with reward not contingent upon response). These were run in yoked fashion and killed when the discrimination-trained mouse reached a learning criterion of 70% correct responses in 20 trials, occurring after three sessions (a total of ∼40 min of training). The hippocampus was dissected bilaterally from each mouse (N = 7 in each group) and profiling of 585 miRNAs (microRNAs) was carried out using multiplex RT–PCR (reverse transcription–PCR) plates. A significant global up-regulation of miRNA expression was observed in the discrimination training versus pseudo-training comparison; when tested individually, 29 miRNAs achieved significance at P = 0.05. miR-10a showed a 2.7-fold increase with training, and is predicted to target several learning-related mRNAs including BDNF (brain-derived neurotrophic factor), CAMK2b (calcium/calmodulin-dependent protein kinase IIβ), CREB1 (cAMP-response-element-binding protein 1) and ELAVL2 [ELAV (embryonic lethal, abnormal vision, Drosophila)-like; Hu B]. Analysis of miRNA pairwise correlations revealed the existence of several miRNA co-expression modules that were specific to the training group. These in vivo results indicate that significant, dynamic and co-ordinated changes in miRNA expression accompany early stages of learning.
A No Reference Image Blur Detection using Cumulative probability Blur Detection (CPBD) Metric

—This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of cumulative probability blur detection with the just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness / blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. The blur perception information at each edge is then pooled over the entire image to obtain a final quality score by evaluating the cumulative probability of blur detection (CPBD) metric. Higher metric value represent sharper image. Images having small metric value denote blurred and noisy images . The main purpose of CPBD metric is in TELEMEDICINE and Image quality Measure.
Performance Analysis of Cooperative Sensing with Equal Gain Combination over Nakagami Channels in Cognitive Radio Networks

We address the problem of primary user detection in cognitive radio networks. The performance of cooperative spectrum sensing with equal gain combination over Nakagami channels is investigated in this paper, and the series form of detection probability is obtained. In the numerical results, the correctness of the theoretical analysis results is validated, and the detection performances with different parameters are shown.
Channel ranking based on packet delivery ratio estimation in wireless sensor networks

Wireless sensor networks (WSNs) operating in 2.4 GHz unlicensed bands must explore favorable channels in order to mitigate the effects of induced interference by co-existing wireless systems and frequency selective fading. In this context, we develop a packet delivery ratio (PDR) estimation method for channel ranking in WSNs. The PDR, in general, is defined as a function of signal-to-noise ratio (SNR) and signal-to-interferenceplus-noise ratio (SINR) at the sensor and the packet collision-time distribution of the sensor link. The collision-time distribution depends on the packet size and packet inter-arrival time distributions of both networks. Under limited channel measurements, the collision-time cannot be estimated satisfactorily. In order to bypass the collision-time estimation process, the proposed PDR estimation method utilizes signal level, interference and noise characteristics identified by spectrum measurements adjusted to the intended traffic pattern of the sensor link. The proposed method is validated against the empirical PDR using off-the-shelf sensor platform in emulated multi path wireless fading channels. The results reveal that the method is accurate in modeling the empirical PDR with limited channel energy measurements. In addition, we used the estimated PDR as a metric for channel ranking and verified its effectiveness by ranking the available channels to a WSN under interference from multiple WLANs in a real environment.
NLP Challenges for Eunomos a Tool to Build and Manage Legal Knowledge

In this paper, we describe how NLP can semi-automate the construction and analysis of knowledge in Eunomos, a legal knowledge management service which enables users to view legislation from various sources and find the right definitions and explanations of legal concepts in a given context. NLP can semi-automate some routine tasks currently performed by knowledge engineers, such as classifying norm, or linking key terms within legislation to ontological concepts. This helps overcome the resource bottleneck problem of creating specialist knowledge management systems. While accuracy is of the utmost importance in the legal domain, and the information should be verified by domain experts as a matter of course, a semi-automated approach can result in considerable efficiency gains.
Evolution Characteristics of the Network Core in the Facebook

Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL) and Facebook-wall(FW) datasets into 28 snapshots in terms of timestamps. By employing the k-core decomposition method to identify the core of each snapshot, we find that the core sizes of the FL and FW networks approximately contain about 672 and 373 nodes regardless of the exponential growth of the network sizes. Secondly, we analyze evolving topological properties of the core, including the k-core value, assortative coefficient, clustering coefficient and the average shortest path length. Empirical results show that nodes in the core are getting more interconnected in the evolving process. Thirdly, we investigate the life span of nodes belonging to the core. More than 50% nodes stay in the core for more than one year, and 19% nodes always stay in the core from the first snapshot. Finally, we analyze the connections between the core and the whole network, and find that nodes belonging to the core prefer to connect nodes with high k-core values, rather than the high degrees ones. This work could provide new insights into the online social network analysis.
Adaptive support weight aggregation in segmentationsfor stereo matching

Computing the dissimilarity between the central pixel and other pixels within the support window and determining the size and shape of the support window are core ideas of the adaptive support weight approach in local methods of stereo matching. In this paper we focus on the latter issue and propose a new strategy to find that window. First, the reference image is segmented into a set of irregular regions; second, for each pixel, the support weight and aggregation is calculated within the segmentation it belongs to; third, all disparities are selected by winner take all strategy. Through the segmentation technique, the proposed approach implements an adaptive support window without the size and shape limitation. Experiment results based on the Middlebury benchmark show that, compared with the original approach, the new approach effectively improves the accuracy of result disparity maps.
NetCo: Reliable Routing With Unreliable Routers

Software-Defined Networks (SDNs) are typically designed and operated under the assumption that the underlying routers (and switches) are trustworthy. Recent incidents, however, suggest that this assumption is questionable. The possibility of incorrect or even malicious router behavior introduces a wide range of security problems. The problem is exacerbated by the fact that governments and companies do not have the expertise nor budget to build their own trusted high-performance routing hardware. This paper presents NetCo, an approach to build secure routing using insecure routers. NetCo is inspired by the robust combiner concept known from cryptography, and leverages redundancy to compile a secure whole from insecure parts. We present the basic design of NetCo, and report on a prototype implementation in OpenFlow.
An Uncertainty Model of Stereo Vision and its Application to Vision-Motion Planning of Robot

This paper describes an uncertainty model of stereo vision and its application to a visionmotion planning for a mobile robot. In general, recognition of an environment requires much computation and the recognition result includes uncertainty. In planning, therefore, a trade-o must be considered between the cost of visual recognition and the e ect of information obtained by recognition. Such a trade-o must be formulated on the basis of a model of vision which describes the required time for visual processing and uncertainty of information to be obtained. In this paper, an uncertainty model of stereo vision is described, in which not only the quantization error but also false matchings of features are considered. A strategy for resolving ambiguous matchings is also proposed. Using the uncertainty model in the planner, an optimal plan for a real world problem is generated. An e cient solving strategy is also described which employs a pruning method based on the lower bound of the total cost calculated by the assumption of perfect sensor information.
SUBMILLIMETER NARROW EMISSION LINES FROM THE INNER ENVELOPE OF IRC+10216

A spectral-line survey of IRC+10216 in the 345 GHz band has been undertaken with the Submillimeter Array. Although not yet completed, it has already yielded a fairly large sample of narrow molecular emission lines with line widths indicating expansion velocities of ∼ 4 km s−1, less than three times the well known value of the terminal expansion velocity (14.5 km s−1) of the outer envelope. Five of these narrow lines have now been identified as rotational transitions in vibrationally excited states of previously detected molecules: the v = 1, J = 17–16, and J = 19–18 lines of Si34S and 29SiS and the v = 2, J = 7–6 line of CS. Maps of these lines show that the emission is confined to a region within ∼ 60 AU of the star, indicating that the narrow-line emission is probing the region of dust formation where the stellar wind is still being accelerated.
Echocardiographic Assessment of Structural and Hemodynamic Changes in Hypertension-Related Pregnancy

Background Pregnancy induces dramatic cardiovascular changes in order to meet the increasing metabolic needs. Adaptive change of left ventricle (LV) might be modified in pregnancy complicated by hypertension. Methods Data from 193 consecutive pregnant women were analyzed. Clinical and echocardiographic data were compared in normotensive and hypertensive women. Results Significantly higher LV mass indexed by height was observed in hypertensive women compared with normotensive women (84 ± 21 g/m vs. 97 ± 20 g/m, p = 0.001). Diastolic function measured by the ratio of peak velocity of early diastolic transmitral blood flow to early diastolic mitral annular velocity was impaired in hypertensive women (11.0 ± 3.0 vs. 9.2 ± 2.5, p < 0.001). Such change was more prominent in women with gestational hypertension (GH) than those with chronic hypertension (CH). Heavy maternal weight was an independent factor associated with LV hypertrophy (LVH) in both normotensive and hypertensive women. Overt eccentric LVH was more frequent than concentric remodeling/hypertrophy (24% vs. 8.4%) in GH, while the opposite result was observed in CH (14% vs. 23%). Conclusion Hypertensive pregnancy is associated with significant LVH and diastolic dysfunction. CH seems to induce different LV remodeling pattern from GH. Heavy maternal weight during pregnancy might intensify the unfavorable remodeling of LV, particularly in hypertensive pregnancy.