Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.
A Robust Longitudinal Co-culture of Obligate Anaerobic Gut Microbiome With Human Intestinal Epithelium in an Anoxic-Oxic Interface-on-a-Chip

The majority of human gut microbiome is comprised of obligate anaerobic bacteria that exert essential metabolic functions in the human colon. These anaerobic gut bacteria constantly crosstalk with the colonic epithelium in a mucosal anoxic-oxic interface (AOI). However, in vitro recreation of the metabolically mismatched colonic AOI has been technically challenging. Furthermore, stable co-culture of the obligate anaerobic commensal microbiome and epithelial cells in a mechanically dynamic condition is essential for demonstrating the host-gut microbiome crosstalk. Here, we developed an anoxic-oxic interface-on-a-chip (AOI Chip) by leveraging a modified human gut-on-a-chip to demonstrate a controlled oxygen gradient in the lumen-capillary transepithelial interface by flowing anoxic and oxic culture medium at various physiological milieus. Computational simulation and experimental results revealed that the presence of the epithelial cell layer and the flow-dependent conditioning in the lumen microchannel is necessary and sufficient to create the steady-state vertical oxygen gradient in the AOI Chip. We confirmed that the created AOI does not compromise the viability, barrier function, mucin production, and the expression and localization of tight junction proteins in the 3D intestinal epithelial layer. Two obligate anaerobic commensal gut microbiome, Bifidobacterium adolescentis and Eubacterium hallii, that exert metabolic cross-feeding in vivo, were independently co-cultured with epithelial cells in the AOI Chip for up to a week without compromising any cell viability. Our new protocol for creating an AOI in a microfluidic gut-on-a-chip may enable to demonstrate the key physiological interactions of obligate anaerobic gut microbiome with the host cells associated with intestinal metabolism, homeostasis, and immune regulation.
Phosphodiesterase 1b (PDE1B) Regulates Spatial and Contextual Memory in Hippocampus

Augmentation of cyclic nucleotide signaling through inhibition of phosphodiesterase (PDE) activity has long been understood to enhance memory. Efforts in this domain have focused predominantly on PDE4, a cAMP-specific phosphodiesterase implicated in consolidation. But less is known about the function of other PDEs expressed in neuroanatomical regions critical to memory. The PDE1 isoforms are the only PDEs to regulate neuronal cAMP and cGMP levels in a Ca2+/Calmodulin (CaM) dependent manner. Here, we show that knock-down of PDE1B in hippocampus of adult mice enhances contextual and spatial memory without effect on non-cognitive behaviors. Pharmacological augmentation of memory in rats was observed with a selective inhibitor of PDE1 dosed before and immediately after training, but not with drug dosed either 1 h after training or before recall. Our data clearly demonstrate a role for the PDE1B isoforms as negative regulators of memory, and they implicate PDE1 in an early phase of consolidation, but not retrieval. Inhibition of PDE1B is a promising therapeutic mechanism for treating memory impairment.
The most optimal way to deal with negative consumer review: Can positive brand and customer responses rebuild product purchase intentions?

Purpose After a negative consumer review (NCR) has been posted on an online shopping site such as Amazon.com, the immediate concern of a brand holder should be to focus on the steps the brand should take to rebuild the unhappy consumers’ trust. The purpose of this paper is to employ the signalling theory to analyse whether a brand response, a customer response or a response that combines both when responding to a NCR leads to better product purchase intentions at the customer end. Design/methodology/approach In a laboratory study comprising 351 respondents, six different response scenarios are tested, both for a well-known and an unknown brand. The experiment employs a 6 (response scenario: single brand response, single customer response, brand response and one customer response or vice versa, brand response and three customer responses or vice versa)×2 (customer-based brand equity: strong/weak) between-subject design. Findings The findings show that after a NCR, the subjects perceive a customer response as more trustworthy than a response from an unknown brand. However, customer-based brand equity changes the whole story. If a strong brand responds, the purchase intentions of the subjects are similar to those generated by a single customer’s response. In addition, after considering multiple responses, it can be seen that a response combining a brand and a customer response has a higher effect than from a single response. Furthermore, the authors demonstrate that perceptions are more favourable if several customer responses are sent in case of an unknown brand. Originality/value The originality of this paper lies in the fact that it tries to explore how the consumers perceive multiple responses from different sources after a NCR has been posted. The results highlight that a response that combines a brand and a customer response has a significantly higher effect than what is achieved from a single response. It must also be noted that customer-based brand equity plays a key role.
On Application of Parabolic Equation Method to Propagation Modeling in Millimeter-Wave Bands

This research studies the millimeter-wave (mmWave) propagation mechanisms and corresponding numerical methods. 5G mobile communication systems are expected to operate in mmWave bands, thus the development of the mmWave propagation models is vital for the wireless communication study. In contrast to most other publications, this research is based on the electromagnetic theory and rigorous differential equations. Diffraction, scattering, refraction and attenuation of the radio waves can be computed and visualized. Arbitrary terrain profiles and weather conditions can be accounted. The widely used parabolic equation (PE) framework is applied. State-of-the-art PE based numerical methods are considered with a focus on the mmWave propagation. Numerical examples under various propagation conditions are given.
Measuring Participation in Distance Education Online Discussion Forums Using Social Network Analysis

Distance Education professionals have been constantly coming up with methods and techniques to increase student participation in an environment where learning happens continuously and asynchronously. An online discussion forum (ODF) is one of these mechanisms, but it will only be successful if students are willing to participate. Stimulating students is a challenge many institutions currently face. The objective of this study was to analyze the social interaction among participants in ODFs using Social Network Analysis. Knowing the characteristics of these networks and its participants is important to design actions to improve the use of ODFs. As a case study, data were collected from ODF logs of the majors in Business Administration and Accounting in a Brazilian private university. This study found out that these interaction networks are sparse, which shows that students could be more engaged in interacting and collaborating with others. Students, in general, tend to interact more in the first semester and interaction diminishes as time passes. The number of active ODF participants has been around 45–50%, which shows that students currently do not participate very often in ODFs. Their main incentive seems to exist when they are graded. Popular ODFs were also analyzed.
Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting

This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used to evaluate performance of the hybridized training algorithm and compare them with MAPEs from Backpropagation, GA, and PSO. Yearly average MAPE and the average MAPEs for weekdays, Mondays, weekends, Holidays, and Bridging holidays show that PSO+GA algorithm outperforms other training algorithms for STLF.
Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time.
The Impact of Potential Crowd Behaviours on Emergency Evacuation: An Evolutionary Game-Theoretic Approach

Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game-theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.
Drivers and Barriers for Microservice Adoption - A Survey among Professionals in Germany

Microservices are an architectural style for software which currently receives a lot of attention in both industry and academia. Several companies employ microservice architectures with great success, and there is a wealth of blog posts praising their advantages. Especially so-called Internet-scale systems use them to satisfy their enormous scalability requirements and to rapidly deliver new features to their users. But microservices are not only popular with large, Internet-scale systems. Many traditional companies are also considering whether microservices are a viable option for their applications. However, these companies may have other motivations to employ microservices, and see other barriers which could prevent them from adopting microservices. Furthermore, these drivers and barriers possibly differ among industry sectors. In this article, we present the results of a survey on drivers and barriers for microservice adoption among professionals in Germany. In addition to overall drivers and barriers, we particularly focus on the use of microservices to modernize existing software, with special emphasis on implications for runtime performance and transactionality. We observe interesting differences between early adopters who emphasize scalability of their Internet-scale systems, compared to traditional companies which emphasize maintainability of their IT systems.
Semi-Supervised Learning by Label Gradient Alignment

We present label gradient alignment, a novel algorithm for semi-supervised learning which imputes labels for the unlabeled data and trains on the imputed labels. We define a semantically meaningful distance metric on the input space by mapping a point (x, y) to the gradient of the model at (x, y). We then formulate an optimization problem whose objective is to minimize the distance between the labeled and the unlabeled data in this space, and we solve it by gradient descent on the imputed labels. We evaluate label gradient alignment using the standardized architecture introduced by Oliver et al. (2018) and demonstrate state-of-the-art accuracy in semi-supervised CIFAR-10 classification.
On the Hardness and Inapproximability of Recognizing Wheeler Graphs

In recent years several compressed indexes based on variants of the Burrows-Wheeler transformation have been introduced. Some of these index structures far more complex than a single string, as was originally done with the FM-index [Ferragina and Manzini, J. ACM 2005]. As such, there has been an effort to better understand under which conditions such an indexing scheme is possible. This led to the introduction of Wheeler graphs [Gagie it et al., Theor. Comput. Sci., 2017]. A Wheeler graph is a directed graph with edge labels which satisfies two simple axioms. Wheeler graphs can be indexed in a way which is space efficient and allows for fast traversal. Gagie et al. showed that de Bruijn graphs, generalized compressed suffix arrays, and several other BWT related structures can be represented as Wheeler graphs. Here we answer the open question of whether or not there exists an efficient algorithm for recognizing if a graph is a Wheeler graph. We demonstrate:(i) Recognizing if a graph is a Wheeler graph is NP-complete for any edge label alphabet of size $\sigma \geq 2$, even for DAGs. It can be solved in linear time for $\sigma =1$; (ii) An optimization variant called Wheeler Graph Violation (WGV) which aims to remove the minimum number of edges needed to obtain a Wheeler graph is APX-hard, even for DAGs. Hence, unless P = NP, there exists constant $C > 1$ such that there is no $C$-approximation algorithm. We show conditioned on the Unique Games Conjecture, for every constant $C \geq 1$, it is NP-hard to find a $C$-approximation to WGV; (iii) The Wheeler Subgraph problem (WS) which aims to find the largest Wheeler subgraph is in APX for $\sigma=O(1)$; (iv) For the above problems there exist efficient exponential time exact algorithms, relying on graph isomorphism being computed in strictly sub-exponential time; (v) A class of graphs where the recognition problem is polynomial time solvable.
Age of Information in Multiple Sensing of a Single Source

Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at its destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of age of information. In this study, we consider a network with a single source, a single monitor (destination), and n sensors. In this setting, a single source of information is being sensed independently by different sensors and the data is sent to the monitor. We assume that updates about the source of information arrive at the sensors according to a Poisson random process. Each sensor sends its update to the monitor through a direct link, which is modeled as a queue. The service time to transmit an update is considered to be an exponential random variable. We derive a closed-form expression for the average age of information in our model under a last-come-first-serve (LCFS) queue. Then, we compare different queue setups of first-come-first-serve (FCFS), LCFS, and LCFS with preemption in waiting in terms of the average age of information. It is observed that LCFS is the best queue among the service disciplines.
Toward a Dichotomy for Approximation of $H$-coloring

Given two (di)graphs G, H and a cost function $c:V(G)\times V(H) \to \mathbb{Q}_{\geq 0}\cup\{+\infty\}$, in the minimum cost homomorphism problem, MinHOM(H), goal is finding a homomorphism $f:V(G)\to V(H)$ (a.k.a H-coloring) that minimizes $\sum\limits_{v\in V(G)}c(v,f(v))$. The complexity of exact minimization of this problem is well understood [34], and the class of digraphs H, for which the MinHOM(H) is polynomial time solvable is a small subset of all digraphs. In this paper, we consider the approximation of MinHOM within a constant factor. For digraphs, MinHOM(H) is not approximable if H contains a digraph asteroidal triple (DAT). We take a major step toward a dichotomy classification of approximable cases. We give a dichotomy classification for approximating the MinHOM(H) when H is a graph. For digraphs, we provide constant factor approximation algorithms for two important classes of digraphs, namely bi-arc digraphs (digraphs with a conservative semi-lattice polymorphism or min-ordering), and k-arc digraphs (digraphs with an extended min-ordering). Specifically, we show that: 1. \textbf{Dichotomy for Graphs:} MinHOM(H) has a $2|V(H)|$-approximation algorithm if graph H admits a conservative majority polymorphims (i.e. H is a bi-arc graph), otherwise, it is inapproximable; 2. MinHOM(H) has a $|V(H)|^2$-approximation algorithm if H is a bi-arc digraph; 3. MinHOM(H) has a $|V(H)|^2$-approximation algorithm if H is a k-arc digraph. In conclusion, we show the importance of these results and provide insights for achieving a dichotomy classification of approximable cases. Our constant factors depend on the size of H. However, the implementation of our algorithms provides a much better approximation ratio. It leaves open to investigate a classification of digraphs H, where MinHOM(H) admits a constant factor approximation algorithm that is independent of H.
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models

Acoustic-to-word (A2W) models that allow direct mapping from acoustic signals to word sequences are an appealing approach to end-to-end automatic speech recognition due to their simplicity. However, prior works have shown that modelling A2W typically encounters issues of data sparsity that prevent training such a model directly. So far, pre-training initialization is the only approach proposed to deal with this issue. In this work, we propose to build a shared neural network and optimize A2W and conventional hybrid models in a multi-task manner. Our results show that training an A2W model is much more stable with our multi-task model without pre-training initialization, and results in a significant improvement compared to a baseline model. Experiments also reveal that the performance of a hybrid acoustic model can be further improved when jointly training with a sequence-level optimization criterion such as acoustic-to-word.