Miracle-dz/UniFAR-FTUs huggingface dataset:
EXPERIENCES AND CONSIDERATIONS IN IMAGE-BASED MODELING OF COMPLEX ARCHITECTURES

Nowadays image-based modeling is receiving much attention and many applications require precise and photo-realistic 3D models. The camera calibration and orientation phases are key steps in the 3D modeling process. If these phases are not accurately performed, there will be some errors in the final model and for some applications low accuracy results are not accepted. The goal of this work is to investigate the influence of wrong camera parameters or bad image configuration in object reconstruction. The analysis is performed with a bundle adjustment solution perturbing the interior camera parameters and using different network configurations. We analyze the effects of wrong focal length and principal point as well as absent distortion parameters with images acquired under typical project configurations. Finally we report some examples of 3D modeling of complex architectures where the theoretical considerations cannot always be fulfilled.
Trustworthy Privacy-Preserving Car-Generated Announcements in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) allow vehicle-to-vehicle communication and, in particular, vehicle-generated announcements. Provided that the trustworthiness of such announcements can be guaranteed, they can greatly increase the safety of driving. A new system for vehicle-generated announcements is presented that is secure against external and internal attackers attempting to send fake messages. Internal attacks are thwarted by using an endorsement mechanism based on threshold signatures. Our system outperforms previous proposals in message length and computational cost. Three different privacy-preserving variants of the system are also described to ensure that vehicles volunteering to generate and/or endorse trustworthy announcements do not have to sacrifice their privacy.
Application of Artificial Neural Network for Fatigue Life Prediction under Interspersed Mode-I Spike Overload

The objective of this study is to design multi-layer perceptron artificial neural network (ANN) architecture in order to predict the fatigue life along with different retardation parameters under constant amplitude loading interspersed with mode-I overload. Fatigue crack growth tests were conducted on two aluminum alloys 7020-T7 and 2024-T3 at various overload ratios using single edge notch tension specimens. The experimental data sets were used to train the proposed ANN model to predict the output for new input data sets (not included in the training sets). The model results were compared with experimental data and also with Wheeler’s model. It was observed that the model slightly over-predicts the fatigue life with maximum error of + 4.0 % under the tested loading conditions
Predicting execution time of machine learning tasks using metalearning

Lately, many academic and industrial fields have shifted research focus from data acquisition to data analysis. This transition has been facilitated by the usage of Machine Learning (ML) techniques to automatically identify patterns and extract non-trivial knowledge from data. The experimental procedures associated with that are usually complex and computationally demanding. Scheduling is a typical method used to decide how to allocate tasks into available resources. An important step for such is to guess how long an application would take to execute. In this paper, we introduce an approach for predicting processing time specifically of ML tasks. It employs a metalearning framework to relate characteristics of datasets and current machine state to actual execution time. An empirical study was conducted using 78 publicly available datasets, 6 ML algorithms and 4 meta-regressors. Experimental results show that our approach outperforms a commonly used baseline method. Statistical tests advise using SVMr as meta-regressor. These achievements indicate the potential of metalearning to tackle the problem and encourage further developments.
An evaluation of multi-modal user interface elements for tablet-based robot teleoperation

For robot teleoperation systems, tablet and smart phone user interfaces provide portability and accessibility to allow anyone anywhere to connect to the system quickly and easily. This can be highly advantageous for disaster-relief robot systems where timing is critical. However, the small screen size and unconventional input methods mean that traditional teleoperation user interface elements, such as multiple visual windows, keyboards and mice, are no longer effective. In this paper we propose and investigate multi-modal user interface design principles as a solution for effective tablet-based teleoperation. We present our tablet user interface that features multiple complementary modalities of control and feedback mechanisms. It allows the operator to perceive the robot's environment and state by exciting their senses of vision, hearing and touch with overlaying visual displays, virtual 3-D audio and vibration feedback. The operator commands actions to the robot through the use of intuitive input methods utilising the touch screen and accelerometer. We analyse these design principles by performing a user study that focuses on finding what aspects of the multi-modal user interface affect the system performance during a navigation task. The study highlights advantages of multimodal user interfaces for robot teleoperation and makes several findings to be considered when developing similar systems.
Can Neural Networks Do Better Than the Vapnik-Chervonenkis Bounds?

We describe a series of careful numerical experiments which measure the average generalization capability of neural networks trained on a variety of simple functions. These experiments are designed to test whether average generalization performance can surpass the worst-case bounds obtained from formal learning theory using the Vapnik-Chervonenkis dimension (Blumer et al., 1989). We indeed find that, in some cases, the average generalization is significantly better than the VC bound: the approach to perfect performance is exponential in the number of examples m, rather than the 1/m result of the bound. In other cases, we do find the 1/m behavior of the VC bound, and in these cases, the numerical prefactor is closely related to prefactor contained in the bound.
Identification of an Upstream Enhancer in the Mouse Lamininα1 Gene Defining Its High Level of Expression in Parietal Endoderm Cells*

Laminin-1 is the major component of the embryonic basement membrane and consists of α1, β1, and γ1 chains. The expression of laminin-1 is induced in mouse F9 embryonal carcinoma cells upon differentiation into parietal endoderm through transcriptional up-regulation of the genes encoding these subunits. Here, we identified a 435-bp enhancer in the 5′-flanking region of the mouse laminin α1 (LAMA1) gene that activated its transcription in a differentiation-dependent manner. This enhancer was also active in PYS-2 parietal yolk sac-derived cells but not in NIH/3T3 fibroblasts, indicating that it was a parietal endoderm-specific enhancer. This enhancer was also active in Engelbreth-Holm-Swarm (EHS) tumor-derived cells characterized by excessive production of laminin-1 and other basement membrane components, suggesting that EHS tumors have a transcriptional control mechanism similar to that of parietal endoderm cells. Electrophoretic mobility shift analyses revealed four protein binding sites (PBS1-PBS4) in the 435-bp region. However, these DNA-binding proteins were detected not only in parietal endoderm cells (i.e. differentiated F9 cells, PYS-2 cells, and EHS tumor-derived cells) but also in undifferentiated F9 cells and NIH/3T3 cells. Mutational analyses revealed that three of these binding sites (PBS2, PBS3, and PBS4) function synergistically to confer the parietal endoderm-specific enhancer activity. The proteins binding to PBS2 and PBS4 were identified as the Sp1/Sp3 family of transcription factors and YY1, respectively.
A Unified Viscoelasto-Plastic Damage Model for Long-Term Performance of Prestressed Concrete Box Girders

The prediction of long-term deflection of large-span prestressed concrete bridges is a serious challenge to the current progress towards sustainable transportation system, which requires for a longer service lifetime. Although a number of concrete models and numerical formulations were proposed, the accuracy of prediction is not satisfactory and significant underestimate happens in structural analysis. In order to overcome this obstacle, a unified viscoelasto-plastic damage model is proposed for the prediction of long-term performance of large-span prestressed concrete bridges carrying heavy traffic flow. In this unified concrete model, concrete cracking, plasticity and history-dependent behaviors (e.g. static creep, cyclic creep and shrinkage) are coupled. The isotropic damage model developed by Tao and Phillips enriched with the plastic yield surface is used in this study. For the static creep and shrinkage models, the rate-type formulation is applied so as to 1) save the computational cost and 2) make it admissible to couple memory-dependent and -independent processes. Cyclic creep, which is frequently ignored in structural analysis, is found to contribute substantially to the deflections of bridges with heavy traffic loads. The model is embedded in the general FEM program ABAQUS and a case study is carried out on the Humen Bridge Auxiliary Bridge. The simulation results are compared to the inspection reports, and the effectiveness of the proposed model is supported by the good agreement between the simulation and in-situ measurements.
TraV: A Genome Context Sensitive Transcriptome Browser

Next-generation sequencing (NGS) technologies like Illumina and ABI Solid enable the investigation of transcriptional activities of genomes. While read mapping tools have been continually improved to enable the processing of the increasing number of reads generated by NGS technologies, analysis and visualization tools are struggling with the amount of data they are presented with. Current tools are capable of handling at most two to three datasets simultaneously before they are limited by available memory or due to processing overhead. In order to process fifteen transcriptome sequencing experiments of Bacillus licheniformis DSM13 obtained in a previous study, we developed TraV, a RNA-Seq analysis and visualization tool. The analytical methods are designed for prokaryotic RNA-seq experiments. TraV calculates single nucleotide activities from the mapping information to visualize and analyze multiple transcriptome sequencing experiments. The use of nucleotide activities instead of single read mapping information is highly memory efficient without incurring a processing overhead. TraV is available at http://appmibio.uni-goettingen.de/index.php? sec=serv.
Metric learning on biological sequence embeddings

Embedding techniques such as word2vec [1] have gained popularity due to their ability to represent words and their semantic variants as real valued vectors. Biological sequence analysis may also leverage unsupervised feature representations, augmented with supervised learning techniques for tasks like retrieval and classification. Algorithms that rely on distance metrics are computationally efficient and can handle large datasets, however, default distances in the embedded space often yield inadequate accuracy. In this paper, we use class labels to learn a Mahalanobis distance in the embedded feature vector space and show performance improvements over the default Euclidean metric in both retrieval and classification tasks. The approach may be readily generalised, and is and applicable to a wide range of problems in sequence analysis and others involving discrete entities or segmented data streams.
A trans-membrane segment inside the ribosome exit tunnel triggers RAMP4 recruitment to the Sec61p translocase

Membrane protein integration occurs predominantly at the endoplasmic reticulum and is mediated by the translocon, which is formed by the Sec61p complex. The translocon binds to the ribosome at the polypeptide exit site such that integration occurs in a cotranslational manner. Ribosomal protein Rpl17 is positioned such that it contacts both the ribosome exit tunnel and the surface of the ribosome near the exit site, where it is intimately associated with the translocon. The presence of a trans-membrane (TM) segment inside the ribosomal exit tunnel leads to the recruitment of RAMP4 to the translocon at a site adjacent to Rpl17. This suggests a signaling function for Rpl17 such that it can recognize a TM segment inside the ribosome and triggers rearrangements of the translocon, priming it for subsequent TM segment integration.
Detecting Image Forgery Based on Noise Estimation

With the advent of the Internet and low-price digital cameras, as well as powerful image editing software, the authenticity of digital images can no longer be taken for granted. Image noises are often introduced into the tampered region during image manipulation process. In this paper, we propose a detection method to locate image forgeries based on noise estimation on HSV color space and hybrid clustering method combined with unsupervised clustering and supervised clustering. A suspicious image is first converted into HSV color space and segmented into non-overlapping image blocks. Then the noise variance at each local image block is estimated as input of unsupervised clustering. Finally, a supervised clustering method based on SVM is used to further improve the detection accuracy. Our experimental results demonstrate that the proposed method can effectively expose tampered regions from tampered images.
Microstructural Characterization of the New Tool Ni-Based Alloy with High Carbon and Chromium Content

The concept of new tool materials, based on Ni alloys strengthened by intermetallic compounds, intended for applications at high temperatures is presented in the paper. The chemical composition of this new material was designed in such a way as to obtain the matrix strengthening by means of the precipitation of intermetallic compounds rich in Ni and to have the carbide fraction above 25%. Carbides should remain stable in the microstructure, regardless of the heat treatment, since they favourably influence an abrasion resistance. The results of microstructure investigations in the as-cast condition, are given. The type of phases appearing in the microstructure was determined and their morphology described. The main microstructure components of the investigated Ni-based alloy with high carbon and chromium content are: the γ phase, which constitutes a matrix, the γ’ phase, which occurs as fine globular precipitates and the primary Ta and Cr carbides (of MC and M7C3 type – respectively).
A Comparison with two semantic sensor data storages in total data transmission

The creation of small and cheap sensors promoted the emergence of large scale sensor networks. Sensor networks allow monitoring a variety of physical phenomena, like weather conditions (temperature, humidity, atmospheric pressure .), traffic levels on highways or rooms occupancy in public buildings. Some of the sensors produce large volume of data such as weather temperature. These data should be stored somewhere for user queries. In this paper two known sensor data storage methods that store data semantically has been compared and it has been shown that storing data in ontology form consumes more energy so the lifetime of sensor network would decreases. The reason we choose them is that they are useful and popular.
Mechanics of biomimetic materials for tissue engineering of the intervertebral disc

Tissue engineering o ers a paradigm shift in the treatment of back pain. Engineered intervertebral discs could replace degenerated tissue and overcome the limitations of current treatments that disrupt the biomechanics of the spine. New materials, which exhibit sophisticated mechanical responses, are needed to provide templates for tissue regeneration. These behaviours include timedependent deformation facilitating shock absorption and nutrient transfer and strong material anisotropy and tensile-compressive nonlinearities providing exibility in controlled directions. In this work, frameworks for the design of materials with controllable structure-property relationships are developed. The time-dependent mechanical properties of composites of agar, alginate and gelatin hydrogels are investigated. It is shown that the time-dependent responses of the composites can be tuned over a wide range. It is then demonstrated that materials mimicking the bre-reinforced nature of natural tissues can be developed by in ltrating thick electrospun bre networks with alginate. These bre-reinforced hydrogels have tensile and compressive properties that can be separately altered. To better understand the mechanical behaviour of these hydrogel-based materials, improved methods for characterising poroelastic and poroviscoelastic timedependent material properties using indentation are developed. It is shown that poroviscoelastic relaxation is the product of separate poroelastic and viscoelastic relaxation responses. The techniques developed here provide a methodology to rapidly characterise the properties of time-dependent materials and to create materials with complex structure-property relationships similar to those found in natural tissues; they present a framework for biomimetic materials design. The work in this thesis can be used to inform the design of clinically relevant tissue engineering treatments and help the quarter of a million people each year who undergo spinal surgery to reduce back pain.
On the Design of Effective Online Market Interfaces: Focusing on Massive Scale Online Auctions

The Internet has dramatically changed how people sell and buy goods. In recent years we have seen the emergence of electronic marketplaces that leverage information technology to create more efficient markets such as online auctions to bring together buyers and sellers with greater effectiveness and at a massive scale. A potential threat to the effectiveness of massive scale online marketplaces is information overload – buyers have to put in extensive effort in searching through an enormous amount of products and sellers have to compete with a great number of sellers in order to effectively reach the potential buyers. The purpose of this paper is to propose a framework for the design of massive scale online marketplaces focusing on the effectiveness of bringing buyers and sellers together. An exploratory analysis of a currently operational online auction market is conducted to provide insights into how the various design aspects may affect how the online auction marketplace is used. The results have various implications for the design of current and perhaps future online marketplaces to come.
Mining semantically related terms from biomedical literature

Discovering links and relationships is one of the main challenges in biomedical research, as scientists are interested in uncovering entities that have similar functions, take part in the same processes, or are coregulated. This article discusses the extraction of such semantically related entities (represented by domain terms) from biomedical literature. The method combines various text-based aspects, such as lexical, syntactic, and contextual similarities between terms. Lexical similarities are based on the level of sharing of word constituents. Syntactic similarities rely on expressions (such as term enumerations and conjunctions) in which a sequence of terms appears as a single syntactic unit. Finally, contextual similarities are based on automatic discovery of relevant contexts shared among terms. The approach is evaluated using the Genia resources, and the results of experiments are presented. Lexical and syntactic links have shown high precision and low recall, while contextual similarities have resulted in significantly higher recall with moderate precision. By combining the three metrics, we achieved F measures of 68% for semantically related terms and 37% for highly related entities.
BAGE Hypomethylation Is an Early Event in Colon Transformation and Is Frequent in Histologically Advanced Adenomas

We showed earlier that BAGE (B melanoma antigen) loci are hypermethylated in normal tissues and hypomethylated in 98% of human cancers. More recently, we provided evidence that hypomethylation of BAGE loci represents an informative marker for colon cancer detection. In this study, we show that hypomethylation of BAGE loci was an early event that occurred in 43% of colorectal adenomas. Interestingly, hypomethylation of BAGE loci was frequent (50%) in tubulo-villous and villous adenomas, these adenomas having a high probability of being transformed into colorectal cancers.
Adaptive Arrival Price Robert Almgren

Arrival price algorithms determine optimal trade schedules by balancing the market impact cost of rapid execution against the volatility risk of slow execution. In the standard formulation, meanvariance optimal strategies are static: they do not modify the execution speed in response to price motions observed during trading. We show that with a more realistic formulation of the meanvariance tradeoff, and even with no momentum or mean reversion in the price process, substantial improvements are possible for adaptive strategies that spend trading gains to reduce risk, by accelerating execution when the price moves in the trader’s favor. The improvement is larger for large initial positions. ∗Electronic Trading Services, Banc of America Securities LLC, New York; Robert.Almgren@bofasecurities.com. ∗∗Institute of Theoretical Computer Science, ETH Zürich; jlorenz@inf.ethz. ch. Partially supported by UBS AG.
Collembolan Transcriptomes Highlight Molecular Evolution of Hexapods and Provide Clues on the Adaptation to Terrestrial Life

Background Collembola (springtails) represent a soil-living lineage of hexapods in between insects and crustaceans. Consequently, their genomes may hold key information on the early processes leading to evolution of Hexapoda from a crustacean ancestor. Method We assembled and annotated transcriptomes of the Collembola Folsomia candida and Orchesella cincta, and performed comparative analysis with protein-coding gene sequences of three crustaceans and three insects to identify adaptive signatures associated with the evolution of hexapods within the pancrustacean clade. Results Assembly of the springtail transcriptomes resulted in 37,730 transcripts with predicted open reading frames for F. candida and 32,154 for O. cincta, of which 34.2% were functionally annotated for F. candida and 38.4% for O. cincta. Subsequently, we predicted orthologous clusters among eight species and applied the branch-site test to detect episodic positive selection in the Hexapoda and Collembola lineages. A subset of 250 genes showed significant positive selection along the Hexapoda branch and 57 in the Collembola lineage. Gene Ontology categories enriched in these genes include metabolism, stress response (i.e. DNA repair, immune response), ion transport, ATP metabolism, regulation and development-related processes (i.e. eye development, neurological development). Conclusions We suggest that the identified gene families represent processes that have played a key role in the divergence of hexapods within the pancrustacean clade that eventually evolved into the most species-rich group of all animals, the hexapods. Furthermore, some adaptive signatures in collembolans may provide valuable clues to understand evolution of hexapods on land.
Arylglycine-derivative synthesis via oxidative sp3 C–H functionalization of α-amino esters

An efficient method for the synthesis of arylglycine derivatives is described. The oxidative coupling reactions of naphthols and phenols with α-amino esters proceeded smoothly in the presence of meta-chloroperoxybenzoic acid as an oxidant under ambient conditions, to produce arylglycine derivatives in satisfactory yields.