Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogeography and species distribution modelling of Cryptocephalusbarii (Coleoptera: Chrysomelidae): is this alpine endemic species close to extinction?

Abstract The alternation of glacial and interglacial cycles of the Quaternary period contributed in shaping the current species distribution. Cold-adapted organisms experienced range expansion and contraction in response to the temperature decrease and increase, respectively. In this study, a fragment of the mitochondrial marker COI was used to investigate the phylogeography of Cryptocephalusbarii, a cold-adapted alpine leaf beetle species endemic of Orobie Alps, northern Italy. The relationships among populations, their divergence time, and the most probable migration model were estimated and are discussed in light of the Pleistocene climate oscillations. Through a species distribution modelling analysis, the current habitat suitability was assessed and the distribution in a future global warming scenario predicted. The main divergence events that led to the actual population structure took place from ~750,000 to ~150,000 years ago, almost following the pattern of the climate oscillations that led to the increase of the connections between the populations during cold periods and the isolation on massifs in warm periods. The most supported migration model suggests that the species survived to past adverse climatic conditions within refugia inside and at the limit of the actual range. The species distribution modelling analysis showed that C.barii is extremely sensitive to air temperature variations, thus the increase of temperature caused by global warming will reduce the suitable areas within the species range, leading to its possible extinction in the next 50 years. Cryptocephalusbarii is a representative case of how cold adapted and limited distributed species have been and could be affected by climate change, that highlights the implementation of conservation actions.
An Autoassociative Memory Model Derived from a Modular Neural Network and a Diversity of the Association Properties

In this paper, we propose a new autoassociative memory model which is derived from CrossCoupled Hopfield Nets (CCHN). The CCHN is a modular neural network in which plural Hopfield networks are mutually connected via feedforward neural networks. The CCHN's architecture is determined by the following structural parameters:the number of modules, the numbers of units in the modules, the contribution of the module information processings and the interactions to the whole network information processing, and the module connectivity. If these parameters are changed, the network dynamics are also changed; therefore, it may be possible to implement a great number of autoassociative memories with different nature. Through some computer simulations, we will discuss a diversity of association properties in the proposed model.
Population-, sex- and individual level divergence in life-history and activity patterns in an annual killifish

Variation in life-history strategies along a slow-fast continuum is largely governed by life-history trade-offs. The pace-of-life syndrome hypothesis (POLS) expands on this idea and suggests coevolution of these traits with personality and physiology at different levels of biological organization. However, it remains unclear to what extent covariation at different levels aligns and if also behavioral patterns such as diurnal activity changes should be incorporated. Here, we investigate variation in life-history traits as well as behavioral variation at the individual, sex and population level in the Turquoise killifish Nothobranchius furzeri. We performed a common garden laboratory experiment with four populations that differ in pond permanence and scored life-history and behavioral (co-) variation at the individual and population level for both males and females. In addition, we focused on diurnal activity change as a behavioral trait that remains understudied in ecology. Our results demonstrate sex-specific variation in adult body size and diurnal activity change among populations that originate from ponds with differences in permanence. However, there was no pond permanence-dependent divergence in maturation time, juvenile growth rate, fecundity and average activity level. With regard to behavior, individuals differed consistently in locomotor activity and diurnal activity change while, in contrast with POLS predictions, we found no indications for life-history and behavioral covariation at any level. Overall, this study illustrates that diurnal activity change differs consistently between individuals, sexes and populations although this variation does not appear to match POLS predictions.
Asymmetric Division Gene Neurl2 Mediates Twist2 Regulation of Self-Renewal of Mouse Lewis Lung Cancer Stem Cells

Cancer stem cells (CSCs) play an important role in tumor development. While Epithelial-Mesenchymal Transition (EMT) has been shown to promote CSC self-renewal, underlying mechanisms are unclear. Here we identified and characterized the requirement of twist2, the EMT transcription factor, for the regulation of self-renewal thus stemness of mouse Lewis lung CSCs both in vitro and in vivo. Further, we elucidated the role of neurl2, an asymmetric division gene for normal stem cells, in mediating the self-renewal promoting activity of twist2. Moreover, analysis of TCGA showed a positive correlation between the expression of twist2 and the development of lung adenocarcinoma, and a negative correlation between neurl2 and lung adenocarcinoma development. In summary, our study provides a new mechanistic insight of regulation of CSC self-renewal by EMT.
Immunotherapy for hepatocellular carcinoma: Current and future

Hepatocellular carcinoma (HCC) arises on the background of chronic liver disease. Despite the development of effective anti-viral therapeutics HCC is continuing to rise, in part driven by the epidemic of non-alcoholic fatty liver disease. Many patients present with advanced disease out with the criteria for transplant, resection or even locoregional therapy. Currently available therapeutics for HCC are effective in a small minority of individuals. However, there has been a major global interest in immunotherapies for cancer and although HCC has lagged behind other cancers, great opportunities now exist for treating HCC with newer and more sophisticated agents. Whilst checkpoint inhibitors are at the forefront of this revolution, other therapeutics such as inhibitory cytokine blockade, oncolytic viruses, adoptive cellular therapies and vaccines are emerging. Broadly these may be categorized as either boosting existing immune response or stimulating de novo immune response. Although some of these agents have shown promising results as monotherapy in early phase trials it may well be that their future role will be as combination therapy, either in combination with one another or in combination with treatment modalities such as locoregional therapy. Together these agents are likely to generate new and exciting opportunities for treating HCC, which are summarized in this review.
First Clinical Application of Low-Cost OCT

Purpose We present the design of a new low-cost optical coherence tomography (OCT) system and compare its retinal imaging capabilities to a standard commercial system through a clinical study. Methods A spectral-domain OCT system was designed using various cost-reduction techniques to be low-cost, highly portable, and completely stand-alone. Clinical imaging was performed on 120 eyes of 60 patients (60 eyes of normal volunteers and 60 eyes with retinal disease) using both the low-cost OCT and a Heidelberg Engineering Spectralis OCT. Contrast-to-noise ratio (CNR) was measured from resulting images to determine system performance. Results The low-cost OCT system was successfully applied to clinical imaging of the retina. The system offers an axial resolution of 8.0 μm, a lateral resolution of 19.6 μm, and an imaging depth of 2.7 mm for a 6.6-mm field of view in the X and Y directions. Total cost is $5037, a significant size reduction compared to current commercial higher performance systems. Mean CNR value of low-cost OCT images is only 5.6% lower compared to the Heidelberg Spectralis. Conclusions The images captured with the low-cost OCT were of adequate resolution and allowed for clinical diagnostics. It offers comparable performance as a retinal screening tool at a fraction of the cost of current commercial systems. Translational Relevance Low-cost OCT has the potential to increase access to retinal imaging.
Bacterial autoaggregation

Many bacteria, both environmental and pathogenic, exhibit the property of autoaggregation. In autoaggregation (sometimes also called autoagglutination or flocculation), bacteria of the same type form multicellular clumps that eventually settle at the bottom of culture tubes. Autoaggregation is generally mediated by self-recognising surface structures, such as proteins and exopolysaccharides, which we term collectively as autoagglutinins. Although a widespread phenomenon, in most cases the function of autoaggregation is poorly understood, though there is evidence to show that aggregating bacteria are protected from environmental stresses or host responses. Autoaggregation is also often among the first steps in forming biofilms. Here, we review the current knowledge on autoaggregation, the role of autoaggregation in biofilm formation and pathogenesis, and molecular mechanisms leading to aggregation using specific examples.
Project-based learning and assessment of an IT module in an ODL context

This article introduces project-based learning and assessment in an Information Technology (IT) module offered in an Open Distance Learning (ODL) context. The literature review explains the main arguments relating to project-based learning and assessment in terms of the module researched. The methodology used (an e-mail survey) to investigate the influence of an intervention to enhance student success is described. The findings presented show that the majority of respondents experienced an increase in the effectiveness of teaching for successful learning in an ODL context. Most students believe that sufficient information had been provided to complete their projects successfully, which also helped improve their self-confidence. This article also includes recommendations and implications for the possible future use of project-based learning and assessment in similar modules. In conclusion, the way in which project-based learning and assessment assists in solving problems experienced in a vocational IT module offered in an ODL context is summarised.
New results in nearest neighbor and range searching

In this thesis, we tackle challenging open questions in the area of nearest neighbor and range searching. Nearest neighbor and range searching are among the most fundamental problems in computational geometry, with numerous applications in areas such as pattern recognition, machine learning, and information retrieval. In these problems, we have to preprocess the given point set in some space into a data structure according to the query type, so that queries can be answered efficiently. In our research, we have obtained the following three results. Our first two results concern approximate nearest neighbor searching in the context of metric spaces. We show that the concept of approximate Voronoi diagram (AVD), which is known to provide the most efficient solution in Euclidean spaces, can be generalized to doubling spaces as well as to hyperbolic spaces. This enables us to achieve much faster query times than previously possible, and also achieve space-time tradeoffs for the first time. Our third result is concerned with the complexity of halfspace range searching. We consider this problem in a weighted setting, where each point is associated with a weight from a commutative semigroup, and the output of the query is the semigroup sum of the weights of the points lying within the halfspace. We establish two new lower bounds for this problem in the semigroup arithmetic model. Neglecting logarithmic factors, our result for integral semigroups matches the best known upper bound due to Matoušek. Our result for general semigroups improves upon the best known lower bound due to Brönnimann, Chazelle, and Pach. Moreover, it also resolves the computational complexity of halfspace range searching over idempotent semigroups in the important special case of uniformly distributed point sets.
Improving three aspects of a content based image retrieval system

A content based image retrieval system directly accepts image(s) as input, and returns similar images to the user. The key step in the searching process for a content based image retrieval system is calculating the similarities between the query image and the images in the database, follow by generating a ranked list based on the similarities. In this thesis work, we give a framework to solve the above problem, which contains three parts: image representation, feature combination, and ranking. In detail, firstly some representations are extracted from the query image and the images in the database. Then some similarities are calculated for a query-image pair. At last, the ranking algorithm is used to get a final rank from the similarities vector. For each part of the framework, we propose some candidate methods, and their characteristics are studied in detail. By applying the framework, a simple way of combining multiple image representations are introduced, and its performance is evaluated in the experiment. The results indicates that their is a notable improvement by combining multiple image representations than using them individually. Besides, this work also studies how to treat the ranking problem as a classification problem. Several asymmetric classification losses are proposed and compared. The comparison results give some instructions of how to design a good classification loss for the ranking problem. At last, we build an end-to-end trainable content based image retrieval system based on the convolutional neural network (CNN). The experimental results for the end-to-end system are not satisfactory, where the reasons are analyzed and some potential solutions are proposed.
Content-Centric Networking (CCNx) Messages in TLV Format

This document specifies the encoding of CCNx messages in a TLV packet format, including the TLV types used by each message element and the encoding of each value. The semantics of CCNx messages follow the encoding-independent CCNx Semantics specification. This document is a product of the Information Centric Networking research group (ICNRG).
A 10Gb/s compact low-power serial I/O with DFE-IIR equalization in 65nm CMOS

The design of compact low-power I/O transceivers continues to be a challenge for both chip-to-chip and backplane applications. The introduction of dense fine-pitch silicon packaging technologies, that in principle are capable of supporting tens of thousands of high-data-rate I/O for local chip-to-chip interconnect, will make I/O area and power requirements even more stringent. Figure 10.2. 1 depicts two chips mounted on a silicon (Si) carrier [1], an example of such a dense packaging technology, and connected by fine-pitch interconnects. Characteristics of an example 20mm Si carrier channel are shown in Fig. 10.2. 1, showing significant (∼6dB) DC attenuation as well as 17dB of loss at 5GHz. In the time domain, the response to an isolated ‘1’ applied at 10Gb/s shows many postcursors. A DFE would require many taps to be effective, however, the power and area penalty would be prohibitive in this context. This paper describes an alternative low-power compact I/O transceiver with RX equalization that achieves the required multi-bit postcursor cancellation without a high tap-count DFE. While this work targets data transmission over Si carrier links at rates up to 10Gb/s, it is also relevant to backplane channels.
Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.
Intelligent Adaptation of Ensemble Size in Data Streams Using Online Bagging

In this era of the Internet of Things and Big Data, a proliferation of connected devices continuously produce massive amounts of fast evolving streaming data. There is a need to study the relationships in such streams for analytic applications, such as network intrusion detection, fraud detection and financial forecasting, amongst other. In this setting, it is crucial to create data mining algorithms that are able to seamlessly adapt to temporal changes in data characteristics that occur in data streams. These changes are called concept drifts. The resultant models produced by such algorithms should not only be highly accurate and be able to swiftly adapt to changes. Rather, the data mining techniques should also be fast, scalable, and efficient in terms of resource allocation. It then becomes important to consider issues such as storage space needs and memory utilization. This is especially relevant when we aim to build personalized, near-instant models in a Big Data setting. This research work focuses on mining in a data stream with concept drift, using an online bagging method, with consideration to the memory utilization. Our aim is to take an adaptive approach to resource allocation during the mining process. Specifically, we consider metalearning, where the models of multiple classifiers are combined into an ensemble, has been very successful when building accurate models against data streams. However, little work has been done to explore the interplay between accuracy, efficiency and utility. This research focuses on this issue. We introduce an adaptive metalearning algorithm that takes advantage of the memory utilization cost of concept drift, in order to vary the ensemble size during the data mining process. We aim to minimize the memory usage, while maintaining highly accurate models with a high utility. We evaluated our method against a number of benchmarking datasets and compare our results against the state-of-the art. Return on Investment (ROI) was used to evaluate the gain in performance in terms of accuracy, in contrast to the time and memory invested. We aimed to achieve high ROI without compromising on the accuracy of the result. Our experimental results indicate that we achieved this goal.
Multitier Modules

Multitier programming languages address the complexity of developing distributed systems abstracting over low level implementation details such as data representation, serialization and network protocols. Since the functionalities of different peers can be defined in the same compilation unit, multitier languages do not force developers to modularize software along network boundaries. Unfortunately, combining the code for all tiers into the same compilation unit poses a scalability challenge or forces developers to resort to traditional modularization abstractions that are agnostic to the multitier nature of the language. In this paper, we address this issue with a module system for multitier languages. Our module system supports encapsulating each (cross-peer) functionality and defining it over abstract peer types. As a result, we disentangle modularization and distribution and we enable the definition of a distributed system as a composition of multitier modules, each representing a subsystem. Our case studies on distributed algorithms, distributed data structures, as well as on the Apache Flink task distribution system, show that multitier modules allow the definition of reusable (abstract) patterns of interaction in distributed software and enable separating the modularization and distribution concerns, properly separating functionalities in distributed systems. 2012 ACM Subject Classification Computing methodologies → Distributed programming languages; Software and its engineering → Modules / packages
Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels.
AskStrider: What Has Changed on My Machine Lately?

As computer programs grow more complex, extensible, and connected, it becomes increasingly difficult for users to understand what has changed on their machines and what impact those changes have. In this paper, we describe a tool, called AskStrider, that answers those questions by correlating volatile process information with persistent-state context information and change history. AskStrider automatically scans a system for active components, matches them against a change log to identify recently updated and hence more interesting state, and searches for context information to help users understand the changes. We use several real-world cases to demonstrate the effectiveness of using AskStrider to quickly identify the presence of unwanted software, to determine if a software patch is potentially breaking an application, and to detect lingering components left over from an unclean uninstallation.