Miracle-dz/UniFAR-FTUs huggingface dataset:
Multidimensional Membership Mixture Models

We present the multidimensional membership mixture (M3) models where every dimension of the membership represents an independent mixture model and each data point is generated from the selected mixture components jointly. This is helpful when the data has a certain shared structure. For example, three unique means and three unique variances can effectively form a Gaussian mixture model with nine components, while requiring only six parameters to fully describe it. In this paper, we present three instantiations of M3 models (together with the learning and inference algorithms): infinite, finite, and hybrid, depending on whether the number of mixtures is fixed or not. They are built upon Dirichlet process mixture models, latent Dirichlet allocation, and a combination respectively. We then consider two applications: topic modeling and learning 3D object arrangements. Our experiments show that our M3 models achieve better performance using fewer topics than many classic topic models. We also observe that topics from the different dimensions of M3 models are meaningful and orthogonal to each other.
X.509 Certificate Error Testing

X.509 Certificates are used by a wide range of technologies to verify identities, while the SSL protocol is used to provide a secure encrypted tunnel through which data can be sent over a public network. Combined both of these technologies provides the basis of the public key infrastructure (PKI). While the concept of PKI is a good idea, the different implementation of the technologies in different operating system and clients often lead to weaknesses. This paper proposes a methodology to automate the testing of SSL clients by generating both bogus and malformed certificates in order to evaluate the client's response and identify potential threats to network infrastructures.
Fuel-Optimal Low-Thrust Trajectory Optimization Using Indirect Method and Successive Convex Programming

Although the robustness of indirect methods is enhanced by the homotopic approach and switching detection technique when applied to fuel-optimal low-thrust trajectory optimization, the bottleneck in adjoint initialization still needs further investigation. This paper overcomes this bottleneck by the adjoint mapping between the Lagrange multipliers of direct methods to the adjoint variables. The nonconvex optimization problem deduced from direct methods is converted into a convex one by lossless convexification and successive convex programming. By combining these techniques, a framework is built to effectively solve the fuel-optimal low-thrust trajectory optimization problem.
Juvenile stress causes reduced locomotor behavior and dendritic spine density in the prefrontal cortex and basolateral amygdala in Sprague–Dawley rats

Little has been investigated about the effects of stress on synaptic communication at prepubertal age, a stage considered as juvenile. This period of development is related to socialization through play. Our group has studied the changes of neuronal morphology in limbic structures caused by stress at prenatal and at early postnatal ages (before weaning) in the rat. In the present study, we assessed the effect of restraint stress at juvenile ages. Male Sprague–Dawley rats from postnatal day (PD) 21 to PD35 were restrained (from movement) for 2 hrs. Locomotor activity in a novel environment was evaluated at three different ages, prepubertal PD38, pubertal PD50, and postpubertal PD68. Using the Golgi–Cox procedure, the dendritic morphology was evaluated in the pyramidal neurons of the prefrontal cortex (PFC), hippocampus, and basolateral amygdala (BLA). Juvenile stress caused a reduced locomotor activity at PD38 and PD68 together with reduction in dendritic spines after puberty in the PFC and at all the studied ages in the BLA. In addition, dendritic length was also reduced in the PFC at PD38 and PD68 and CA1 of the ventral hippocampus at PD50 and PD68. Our results suggest that stress in the juvenile stage can cause changes at the level of behavior and synaptic communication with an effect that remains until adulthood.
Mechanically Tunable Photonic Crystal Cavity with High Quality Factor and Small Mode Volume

A fractured planar photonic crystal microcavity is designed to be mechanically tunable while maintaing a quality factor of >1 × 10<sup>5</sup>, and a mode volume ∼ 0.3(λ/n)<sup>3</sup>. Its optomechanical coefficient is ∼15 GHz/nm, at mid-IR wavelengths.
Social practice, plural lifestyles and health inequalities in the United Kingdom

Abstract Persistent health inequalities pose a continued research and policy challenge in the United Kingdom and elsewhere. Current approaches to health research and promotion are predicated on a distinction between wider, social structural causes and individual, health‐related behaviours often conceived of as lifestyle choices. Drawing on Bourdieu's theory of social practice, this paper develops an integrated perspective by observing associations between health and structured lifestyle practices. Using the UK Understanding Society household survey, a taxonomy of eight lifestyle clusters is identified, which exhibit significant health inequalities on a number of indicators. But the plurality of practices and subjective orientations inherent in the taxonomy reveals a finer, more complex differentiation of the social gradient in health. In addition, lifestyle appears to at least in parts mediate the relationship between social, material conditions and health. A feature of the taxonomy is that it admits a relational and contextual apprehension of health‐relevant, behavioural aspects within a more holistic notion of lifestyles. Based on this view, strategic approaches can be developed that respond to group‐specific situations and pathways and their varying roots in upstream or downstream domains of policy.
DÜNYA VE TÜRKİYE ÖRNEKLERİNDE METROPOLİTEN ALAN YÖNETİM MODELLERİ VE 6360 SAYILI YASA

2012 yili sonu itibariyla Turkiye yeni bir metropoliten alan yonetim bicimine gecmistir. Bircok ulkede de yeni metropoliten alan yonetimi denemeleri vardir. Calismada metropoliten alan yonetimi ve modelleri incelenmektedir. Bu baglamda, calismada Amerika Birlesik Devletleri’nde (ABD), Fransa’da ve genel olarak dunyada metropoliten yonetim deneyimleri kisaca gozden gecirilerek, 6360 sayili yasayla Turkiye’de kurulan yeni metropoliten alan yonetimi deneyimi ile karsilastirilmaktadir. Ayrica, yeni il buyuksehir modeli, optimal hizmet olcegi, etkinlik, etkililik, ekonomiklik, erisim, hakkaniyet, yerindenlik, cogulculuk, katilim, yerel ozerklik ve demokrasi vb. acisindan degerlendirilmektedir.
The Seednoid Robot Platform: Designing a Multipurpose Compact Robot From Continuous Evaluation and Lessons From Competitions

Robots are designed to tackle specific problems or are designed as a multipurpose platform that achieves a concept. In this letter, we approach the hardware design problem from practice and evaluation through numerous competitions including the Darpa Robotics Challenge, the Amazon Picking Challenge, and other competitions that were held in Japan. The paper aims to answer the question what hardware design would we achieve if we continuously refine designs through competitions? We show that competitions lead to a more compact but capable platform design that is durable and has more end effector solutions. We also point out the limitations and benefits of using competitions for designing robot platforms. Our platform began from a human-inspired design for teleoperation and has evolved to mix both human-like and industrial robot structure.
25 Years of sensory drive: the evidence and its watery bias

Abstract It has been 25 years since the formalization of the Sensory Drive hypothesis was published in the American Naturalist (1992). Since then, there has been an explosion of research identifying its utility in contributing to our understanding of inter- and intra-specific variation in sensory systems and signaling properties. The main tenet of Sensory Drive is that environmental characteristics will influence the evolutionary trajectory of both sensory (detecting capabilities) and signaling (detectable features and behaviors) traits in predictable directions. We review the accumulating evidence in 154 studies addressing these questions and categorized their approach in terms of testing for environmental influence on sensory tuning, signal characteristics, or both. For the subset of studies that examined sensory tuning, there was greater support for Sensory Drive processes shaping visual than auditory tuning, and it was more prevalent in aquatic than terrestrial habitats. Terrestrial habitats and visual traits were the prevalent habitat and sensory modality in the 104 studies showing support for environmental influence on signaling properties. An additional 19 studies that found no supporting evidence for environmental influence on signaling traits were all based in terrestrial ecosystems and almost exclusively involved auditory signals. Only 29 studies examined the complete coevolutionary process between sensory and signaling traits and were dominated by fish visual communication. We discuss biophysical factors that may contribute to the visual and aquatic bias for Sensory Drive evidence, as well as biotic factors that may contribute to the lack of Sensory Drive processes in terrestrial acoustic signaling systems.
Zfat expression in ZsGreen reporter gene knock-in mice: Implications for a novel function of Zfat in definitive erythropoiesis

Zinc finger and AT-hook domain containing (Zfat) is a transcriptional regulator harboring an AT-hook domain and 18 repeats of a C2H2 zinc-finger motif, which binds directly to the proximal region of transcription start sites in Zfat-target genes. It was previously reported that deletion of the Zfat gene in mice yields embryonic lethality by embryonic day 8.5 and impairs primitive hematopoiesis in yolk sac blood islands. In addition, Zfat has been reported to be involved in thymic T-cell development and peripheral T-cell homeostasis. In the present study, in order to obtain a precise understanding of the expression and function of Zfat, a knock-in mouse strain (ZfatZsG/+ mice), which expressed ZsGreen in the Zfat locus, was established. ZsGreen signals in tissues and cells of ZfatZsG/+ mice were examined by flow cytometric and histological analyses. Consistent with our previous studies, ZsGreen signals in ZfatZsG/+ mice were detected in the embryo and yolk sac blood islands, as well as in thymocytes, B and T cells. In the ZfatZsG/+ thymus, ZsGreen+ cells were identified not only in T-cell populations but also in thymic epithelial cells, suggesting the role of Zfat in antigen-presenting cells during thymic T-cell development. ZsGreen signals were observed in definitive erythroid progenitor cells in the fetal liver and adult bone marrow of ZfatZsG/+ mice. The proportion of ZsGreen+ cells in these tissues was highest at the early stage of erythroid differentiation, suggesting that Zfat serves particular roles in definitive erythropoiesis. Histological studies demonstrated that ZsGreen signals were detected in the pyramidal cells in the hippocampal CA1 region and the Purkinje cells in the cerebellum, suggesting novel functions of Zfat in nervous tissues. Taken together, these results indicated that the ZfatZsG/+ reporter mouse may be considered a useful tool for elucidating the expression and function of Zfat.
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives

This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems. Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions. See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8
Lip-Reading via Deep Neural Network Using Appearance-Based Visual Features

Lip-reading, is visually interpreting lips movements in order to understand speech, when there is no access to the normal sound. Image processing techniques for lip-reading recognition has been widely applied in various kinds of applications. As an application, computer-based video system developed to provide lip-reading instruction to hearing-impaired adults and teenagers. Taking a step toward automating the process, challenges such as coarticulation phenomenon, homophone effect, insufficient training data per class, choice of features and speaker-dependency are faced. Finding a method to overcome these challenges is desirable. This paper describes a lip-reading model, highlighting the feature extraction and recognition parts. Certain arrangement of blocks are considered in a way to achieve optimal appearance-based features for feature extraction part, while a properly structured Deep Belief Network (DBN) is used for the recognition part. The challenging dataset of CUAVE is used in this study, and visual phone recognition (VPR) accuracies are reported on the phone-level. Proposed lip-reading recognizer is unique in its usage for all speakers. Our suggested method outperforms the conventional Hidden Markov Model (HMM)-based recognizer, and the best VPR accuracy of %45.63 is achieved, using the best DBN.
Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease

In last years, several approaches to develop an effective Computer-Aided-Diagnosis (CAD) system for Parkinson's Disease (PD) have been proposed. Most of these methods have focused almost exclusively on brain images through the use of Machine-Learning algorithms suitable to characterize structural or functional patterns. Those patterns provide enough information about the status and/or the progression at intermediate and advanced stages of Parkinson's Disease. Nevertheless this information could be insufficient at early stages of the pathology. The Parkinson's Progression Markers Initiative (PPMI) database includes neurological images along with multiple biomedical tests. This information opens up the possibility of comparing different biomarker classification results. As data come from heterogeneous sources, it is expected that we could include some of these biomarkers in order to obtain new information about the pathology. Based on that idea, this work presents an Ensemble Classification model with Performance Weighting. This proposal has been tested comparing Healthy Control subjects (HC) vs. patients with PD (considering both PD and SWEDD labeled subjects as the same class). This model combines several Support-Vector-Machine (SVM) with linear kernel classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF), RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features and a list of defined Morphological Features) from PPMI database subjects. The proposed methodology makes use of all data sources and selects the most discriminant features (mainly from neuroimages). Using this performance-weighted ensemble classification model, classification results up to 96% were obtained.
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices’ battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.
Unsupervised Coupled Metric Similarity for Non-IID Categorical Data

Appropriate similarity measures always play a critical role in data analytics, learning, and processing. Measuring the intrinsic similarity of categorical data for unsupervised learning has not been substantially addressed, and even less effort has been made for the similarity analysis of categorical data that is not independent and identically distributed (non-IID). In this work, a Coupled Metric Similarity (CMS) is defined for unsupervised learning which flexibly captures the value-to-attribute-to-object heterogeneous coupling relationships. CMS learns the similarities in terms of intrinsic heterogeneous intra- and inter-attribute couplings and attribute-to-object couplings in categorical data. The CMS validity is guaranteed by satisfying metric properties and conditions, and CMS can flexibly adapt to IID to non-IID data. CMS is incorporated into spectral clustering and k-modes clustering and compared with relevant state-of-the-art similarity measures that are not necessarily metrics. The experimental results and theoretical analysis show the CMS effectiveness of capturing independent and coupled data characteristics, which significantly outperforms other similarity measures on most datasets.
Genome Sequencing and Carbohydrate-Active Enzyme (CAZyme) Repertoire of the White Rot Fungus Flammulina elastica

Next-generation sequencing (NGS) of the Flammulina elastica (wood-rotting basidiomycete) genome was performed to identify carbohydrate-active enzymes (CAZymes). The resulting assembly (31 kmer) revealed a total length of 35,045,521 bp (49.7% GC content). Using the AUGUSTUS tool, 12,536 total gene structures were predicted by ab initio gene prediction. An analysis of orthologs revealed that 6806 groups contained at least one F. elastica protein. Among the 12,536 predicted genes, F. elastica contained 24 species-specific genes, of which 17 genes were paralogous. CAZymes are divided into five classes: glycoside hydrolases (GHs), carbohydrate esterases (CEs), polysaccharide lyases (PLs), glycosyltransferases (GTs), and auxiliary activities (AA). In the present study, annotation of the predicted amino acid sequences from F. elastica genes using the dbCAN CAZyme database revealed 508 CAZymes, including 82 AAs, 218 GHs, 89 GTs, 18 PLs, 59 CEs, and 42 carbohydrate binding modules in the F. elastica genome. Although the CAZyme repertoire of F. elastica was similar to those of other fungal species, the total number of GTs in F. elastica was larger than those of other basidiomycetes. This genome information elucidates newly identified wood-degrading machinery in F. elastica, offers opportunities to better understand this fungus, and presents possibilities for more detailed studies on lignocellulosic biomass degradation that may lead to future biotechnological and industrial applications.
Splay trees, Davenport-Schinzel sequences, and the deque conjecture

We introduce a new technique to bound the asymptotic performance of splay trees. The basic idea is to transcribe, in an indirect fashion, the rotations performed by the splay tree as a Davenport-Schinzel sequence, none of whose subsequences are isomorphic to a fixed <i>forbidden subsequence</i>. We direct this technique towards Tarjan's <i>deque conjecture</i> and prove that <i>n</i> deque operations take only <i>O</i>(<i>n</i>α*<i>(n)</i>) time, where α* <i>(n)</i> is the minimum number of applications of the inverse-Ackermann function mapping <i>n</i> to a constant. We are optimistic that this approach could be directed towards other open conjectures on splay trees such as the <i>traversal</i> and <i>split</i> conjectures.
Techno-Economic Analysis of Forest Residue Conversion to Sugar Using Three-Stage Milling as Pretreatment

This study quantifies the cost of cellulosic sugar production using a fully-mechanical pretreatment process and fuel pellets as a co-product. The pretreatment reduces softwood forest harvest residuals to micron-sized amorphous particles. Energy consumption is minimized using a three-stage milling process. A techno-economic analysis was completed for a milling facility with saccharification and wood pellet manufacture. For the base case, concentrated sugar syrup can be produced for $0.496/kg of sugar. Sensitivity analyses were used to determine cost controlling variable, optimize the sugar cost and found that siting for this technology needs to strongly consider electricity cost and to a lesser extent local feedstock availability. If the sugar produced in this process is used to generate biofuel and is qualified for RIN credits through a life-cycle analysis, the effective cost could be reduced by $0.04-$0.06/kg of sugar. An additional $0.067/kg savings is possible if the biofuel facility is located adjacent or on-site; the finished sugar syrup would not have to be concentrated for transportation. An optimized scenario, including the RIN credit, dilute sugar syrup, and favorable energy costs and consumption, could reduce the cost to $0.34/kg sugar compared to $0.496/kg for the base case.