Miracle-dz/UniFAR-FTUs huggingface dataset:
Net Ecosystem Carbon Balance of a Peat Bog Undergoing Restoration: Integrating CO2 and CH4 Fluxes From Eddy Covariance and Aquatic Evasion With DOC Drainage Fluxes

Peatland ecosystems are generally carbon (C) sinks. However, the role of dissolved organic C (DOC) relative to gaseous fluxes of CO2 and CH4 in the C balance of these ecosystems has not often been studied. Dissolved C fluxes are important for understanding C partitioning within the peatland and the potential C drainage from it. This research was conducted in Burns Bog, a heavily impacted ecosystem near Vancouver, Canada, undergoing ecological restoration efforts by rewetting. Here we present data on (i) ecosystem‐scale fluxes of CO2 (net ecosystem exchange, NEE) and CH4 (FCH4) determined by eddy covariance, (ii) evasion fluxes of CO2 and CH4 from the water surface to estimate the role of open water in ecosystem‐scale fluxes, and (iii) DOC flux (fDOC) in water draining from the peatland. Our results showed that open water areas inside the footprint were a continual C source, emitting 47.0 ± 2.4 g C·m−2·year−1. DOC export (15.6 g C·m−2·year−1) was significant to the net ecosystem C balance, decreasing the magnitude of the eddy covariance‐determined C balance (i.e., NEE + FCH4) of −45.0 ± 16.8 g C·m−2·year−1 by 35%, resulting in a net ecosystem C balance (i.e., NEE + FCH4 + fDOC) of −29.7 ± 17.0 g C·m−2·year−1. Most of this offset occurred during the wetter nongrowing season when gross primary production was low and fDOC was relatively high.
REVIEW Identifying ligand-speciﬁc signalling within biased responses: focus on δ opioid receptor ligands

Opioids activate GPCRs to produce powerful analgesic actions but at the same time induce side effects and generate tolerance, which restrict their clinical use. Reducing this undesired response proﬁle has remained a major goal of opioid research and the notion of ‘biased agonism’ is raising increasing interest as a means of separating therapeutic responses from unwanted side effects. However, to fully exploit this opportunity, it is necessary to conﬁdently identify biased signals and evaluate which type of bias may support analgesia and which may lead to undesired effects. The development of new computational tools has made it possible to quantify ligand-dependent signalling and discriminate this component from confounders that may also yield biased responses. Here, we analyse different approaches to identify and quantify ligand-dependent bias and review different types of confounders. Focus is on δ opioid receptor ligands, which are currently viewed as promising agents
Studying Personality through the Content of Posted and Liked Images on Twitter

Interacting with images through social media has become wide-spread due to ubiquitous Internet access and multimedia enabled devices. Through images, users generally present their daily activities, preferences or interests. This study aims to identify the way and extent to which personality differences, measured using the Big Five model, are related to online image posting and liking. In two experiments, the larger consisting of ∼ 1.5 million Twitter images both posted and liked by ∼ 4,000 users, we extract interpretable semantic concepts using large-scale image content analysis and analyze differences specific of each personality trait. Predictive results show that image content can predict personality traits, and that there can be significant performance gain by fusing the signal from both posted and liked images.
Ant Colony Optimization for Mixed-Variable Optimization Problems

In this paper, we show how ant colony optimization (ACO) may be used for tack-ling mixed-variable optimization problems. We show how a version of ACO extended to continuous domains (ACO R ) may be further used for mixed-variable problems. We present diﬀerent approaches to handling mixed-variable optimization problems and explain their possible uses. We propose a new mixed-variable benchmark problem. Finally, we compare the results obtained to those reported in the literature for various real-world mixed-variable optimization problems.
Using Mixed-Reality to Develop Smart Environments

Smart homes, smart cars, smart classrooms are now a reality as the world becomes increasingly interconnected by ubiquitous computing technology. The next step is to interconnect such environments, however there are a number of significant barriers to advancing research in this area, most notably the lack of available environments, standards and tools etc. A possible solution is the use of simulated spaces, nevertheless as realistic as strive to make them, they are, at best, only approximations to the real spaces, with important differences such as utilising idealised rather than noisy sensor data. In this respect, an improvement to simulation is emulation, which uses specially adapted physical components to imitate real systems and environments. In this paper we present our work-in-progress towards the creation of a development tool for intelligent environments based on the interconnection of simulated, emulated and real intelligent spaces using a distributed model of mixed reality. To do so, we propose the use of physical/virtual components (xReality objects) able to be combined through a 3D graphical user interface, sharing real-time information. We present three scenarios of interconnected real and emulated spaces, used for education, achieving integration between real and virtual worlds.
THE UNIVERSITY OF HULL

The portfolio presented functions as an artistic response to the soundscape of Scarborough. The thesis presents a set of soundscape research projects which seek out the core elements that make up Scarborough's individual acoustic identity. The research projects form the source material for each of the compositions, and the data derived from them informs their use. The core intention being to understand how soundscape studies can create informative compositions which in turn improve our understanding of a locations acoustic ecology.
Fast Feature subset selection algorithm based on clustering for high dimensional data

A Feature selection algorithm employ for removing irrelevant, redundant information from the data. Amongst feature subset selection algorithm filter methods are used because of its generality and are usually good choice when numbers of features are large. In cluster analysis, graph-theoretic clustering methods to features are used. In particular, the minimum spanning tree (MST)- based clustering algorithms are adopted. A Fast clustering bAsed feature Selection algoriThm (FAST) is based on MST method. In the FAST algorithm, features are divided into clusters by using graph-theoretic clustering methods and then, the most representative feature that is strongly related to target classes is selected. Features in different clusters are relatively independent. A feature subset selection algorithm (FAST) is used to test high dimensional available image, microarray, and text data sets. Traditionally, feature subset selection research has focused on searching for relevant features. The clustering-based strategy of FAST having a high probability of producing a subset of useful and independent features.
Tool to Enhance Probability Estimates

We propose a general method to assess the reliability of two-class probabilities in an instance-wise manner. This is relevant, for instance, for obtaining calibrated multi-class probabilities from two-class probability scores. The LS-ECOC method approaches this by performing least-squares ﬁtting over a suitable error-correcting output code matrix, where the optimisation resolves potential conﬂicts in the input probabilities. While this gives all input probabilities equal weight, we would like to spend less effort ﬁtting unreliable probability estimates. We introduce the concept of a reliability map to accompany the more conventional notion of calibration map; and LS-ECOC-R which modiﬁes LS-ECOC to take reliability into account. We demonstrate on synthetic data that this gets us closer to the Bayes-optimal classiﬁer, even if the base classiﬁers are linear and hence have high bias. Results on UCI data sets demonstrate that multi-class accuracy also improves.
A deep learning framework for inference of single-trial neural population activity from calcium imaging 1 with sub-frame temporal resolution 2

: 21 In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-22 moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale 23 computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample 24 from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging 25 because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning 26 method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking
A Flexible Uncore Infrastructure for RISC-V Core Development

The openness, flexibility, and modularity of RISC-V opens the door for new developments in computer architecture research. The same characteristics also make it highly attractive for teaching and computer architecture education, as they lower the entry barrier for students in the implementation of their own core designs, especially in combination with FPGA-based rapid prototyping. However, one major issue remains: the development of the surrounding environment, including but not limited to caches, the memory consistency model as well as debugging and control protocols, remain complex and often detract from the ability to design the cores themselves. To address this challenge, we propose a flexible uncore infrastructure — targeted at FPGAs — that can be used to implement, test and use the full range of RISC-V core design possible. Our uncore infrastructure contains a configurable cache and memory hierarchy for multi-core support matching the RISC-V memory semantics and features an easily adaptable communication protocol to simplify the collection of performance metrics and debugging information. We show that our approach allows for the development of both performance-oriented cores as well as cache-coherent multicore systems, while only requiring minimal resources on the targeted FPGA. It enables core designers to focus on the essential part of their design, which makes the framework attractive for researchers and students alike. We use this framework successfully in freshman bachelor courses with positive results.
Moderate Deviations for Particle Filtering

Consider the state space model (Xt, Yt) where (Xt) is a Markov chain and (Yt) are the observations. In order to solve the so-called filtering problem, one 1 n n 1 δx i,t. In this paper, we establish the Moderate Deviation Principle for the empirical mean 1 n n 1 ψ(xi,t) (centered and properly rescaled) when the number of particles grows to infinity, enhancing the Central Limit theorem. Several extensions and examples are also studied.
AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision

Motivation: Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is an important problem, as it is a prerequisite for classifying SVs, evaluating their functional impact and reconstructing personal genome sequences. Given approximate breakpoint locations and a bridging assembly or split read, the problem essentially reduces to finding a correct sequence alignment. Classical algorithms for alignment and their generalizations guarantee finding the optimal (in terms of scoring) global or local alignment of two sequences. However, they cannot generally be applied to finding the biologically correct alignment of genomic sequences containing SVs because of the need to simultaneously span the SV (e.g. make a large gap) and perform precise local alignments at the flanking ends. Results: Here, we formulate the computations involved in this problem and describe a dynamic-programming algorithm for its solution. Specifically, our algorithm, called AGE for Alignment with Gap Excision, finds the optimal solution by simultaneously aligning the 5′ and 3′ ends of two given sequences and introducing a ‘large-gap jump’ between the local end alignments to maximize the total alignment score. We also describe extensions allowing the application of AGE to tandem duplications, inversions and complex events involving two large gaps. We develop a memory-efficient implementation of AGE (allowing application to long contigs) and make it available as a downloadable software package. Finally, we applied AGE for breakpoint determination and standardization in the 1000 Genomes Project by aligning locally assembled contigs to the human genome. Availability and Implementation: AGE is freely available at http://sv.gersteinlab.org/age. Contact: pi@gersteinlab.org Supplementary information: Supplementary data are available at Bioinformatics online.
Bayesian extension to the language model for ad hoc information retrieval

We propose a Bayesian extension to the ad-hoc Language Model. Many smoothed estimators used for the multinomial query model in ad-hoc Language Models (including Laplace and Bayes-smoothing) are approximations to the Bayesian predictive distribution. In this paper we derive the full predictive distribution in a form amenable to implementation by classical IR models, and then compare it to other currently used estimators. In our experiments the proposed model outperforms Bayes-smoothing, and its combination with linear interpolation smoothing outperforms all other estimators.
AIS Electronic

Microfinance based institutions have emerged as a potential solution to the financial exclusion problem in developing economies around the world. A key challenge facing such micro-lending firms is assessing the credit risk of borrowers, owing to the lack of formal financial histories and collaterals. A number of micro-lending companies have, therefore, started leveraging social media and digital communication data from applicants to assess their ability and willingness to repay loans. In our study, we demonstrate a novel approach of leveraging online and offline behavior sequences, as captured from the borrowers’ browsing logs and mobility traces to accurately predict the borrowers’ creditworthiness. Our preliminary results show that using such sequence data, we can provide micro-lending firms with a cheap and reliable strategy for assessing credit risk of borrowers at the time of loan creation. We contend that such big-data based strategies are critical to the sustainability of micro-lending institutions
Computationally Efficient Standard-Cell FEM-based Thermal Analysis

Thermal analysis is a high performance computing problem because the microscale spatial and time discretization of the heat equation translates into repeatedly solving large linear systems of equations. In previous works, compact models of integrated circuits (IC) were introduced to speed up this process. However, such methods are limited in their accuracy as they approximate the underlying physics. The solution methodologies for such models are also ill-suited to simulate the thermal characteristics of an IC at cell-level. The ﬁnite element method (FEM) is an appropriate computational technique for providing both fast and accurate thermal analyses. Considering that the number of cells in modern ICs is in the order of millions, thermal analysis at this abstraction level is a formidable task. Consequently, handling the computational meshes and computing thermal proﬁles of an IC at the cell-level requires intensive computing power. In order to provide accurate cell-level thermal simulations at a lower computational cost, this work introduces an advanced mesh generator for cell-level ﬂoorplans. This mesh generator applies a cell-homogenization technique based on the initial cell-level ﬂoorplan and the power trace to create reduced order meshes for e � cient cell-level thermal simulations. The e ↵ ects of using di ↵ erent homogenization algorithms are explored to illustrate the tradeo ↵ between the simulation speed and the solution accuracy. Results show that the proposed technique can achieve a 90% reduction in the number of nodes in the mesh with less than 5% error to the temperature of the full scale mesh. In addition, the simulation time is reduced by an order of magnitude.
Towards the Study of Human Emotions Through Social Media Contents

Emotions are present in the everyday life of human-beings, and are therefore reflected in the way humans communicate and interact with each other. Gestures, prosody, and the words we choose among a given context, are also good reflectors of the emotional state a given producer of content wishes to impress on a message. In addition to this, social media networks have been arising as an ethereal space where countless people interact with each other and produce a hugeness of contents every minute. These contents, as cheap and easy to obtain, sustain a good base of research to study human behaviour, and in the case of this work, the human behaviour regarding emotions. Hereupon, this work aims at studying human emotions leveraging on social media contents. Having this goal in mind, this dissertation proposes on building a method for inferring the emotional semantics, in terms of scores of valence, arousal, and dominance for textual contents (i.e., words and short texts). Later on, leveraging on the aforementioned method and social media contents from Twitter, are proposed two applications: the first one tries to assess well-being levels for populations across continental USA (excluding Alaska), and the second one aims at studying the relationship between the emotional content of a given message in Twitter and how this message spreads in the referred social network, e.g., trying to see whether markedly negative messages reach a larger number of users than neutral messages.
Supplementary Material for Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis

In this section, we describe the details about the structure of our neural networks and the hyperparameters we use in the experiments. The main differences among the network structures we use on different datasets depend on: (i) the size of the image we are dealing with: the larger image needs more blocks; (ii) the complexity of the scenes. For 7Scenes and Gibson rooms dataset, the scenes are highly complex. Therefore we apply instance normalization to multiple layers, which is dependent on scenes, besides the vector representation of the scene at the top layer. For the GQN rooms dataset, which includes a huge amount of scenes, we employ an encoder to calculate the scene representations online. We use Adam[5] as optimizer for all the experiments with β1 = 0.9 and β2 = 0.999. The learning rate for each setting is introduced in each later section.
Computational Intelligence: Engineering of Hybrid Systems

Intelligent Techniques and Computational Intelligence. - Neuro-Fuzzy based Intelligent Hybrid Systems for Fault Diagnosis. - Neuro-Fuzzy Integration in Intelligent Hybrid Systems. - Fuzzy Rules Extraction from ANNs. - Integration of Explicit and Implicit Knowledge in Intelligent Hybrid Systems. - Practical Implementation Aspects Regarding Real-World Applications of Intelligent Hybrid Systems (NEIKeS, WITNEeSS and other original applications). - New Trends of Developing Intelligent Hybrid Systems - AIS Hybridization and DNA Hybridization. - Genetic Algorithms Based Intelligent Hybrid Systems.
Supplementary Material for Unsupervised Layered Image Decomposition into Object Prototypes

We provide a quantitative evaluation of our approach in an unsupervised semantic segmentation setting. We do not compare to state-of-the-art approaches as none of them explicitly model nor output categories for objects. We argue that modeling categories for discovered objects is crucial to analyse and understand scenes, and thus advocate such quantitative semantic evaluation to assess the quality of any object-based image decomposition algorithm.
Pinstripe: a suite of programs for integrating transcriptomic and proteomic datasets identifies novel proteins and improves differentiation of protein-coding and non-coding genes

MOTIVATION Comparing transcriptomic data with proteomic data to identify protein-coding sequences is a long-standing challenge in molecular biology, one that is exacerbated by the increasing size of high-throughput datasets. To address this challenge, and thereby to improve the quality of genome annotation and understanding of genome biology, we have developed an integrated suite of programs, called Pinstripe. We demonstrate its application, utility and discovery power using transcriptomic and proteomic data from publicly available datasets. RESULTS To demonstrate the efficacy of Pinstripe for large-scale analysis, we applied Pinstripe's reverse peptide mapping pipeline to a transcript library including de novo assembled transcriptomes from the human Illumina Body Atlas (IBA2) and GENCODE v10 gene annotations, and the EBI Proteomics Identifications Database (PRIDE) peptide database. This analysis identified 736 canonical open reading frames (ORFs) supported by three or more PRIDE peptide fragments that are positioned outside any known coding DNA sequence (CDS). Because of the unfiltered nature of the PRIDE database and high probability of false discovery, we further refined this list using independent evidence for translation, including the presence of a Kozak sequence or functional domains, synonymous/non-synonymous substitution ratios and ORF length. Using this integrative approach, we observed evidence of translation from a previously unknown let7e primary transcript, the archetypical lncRNA H19, and a homolog of RD3. Reciprocally, by exclusion of transcripts with mapped peptides or significant ORFs (>80 codon), we identify 32 187 loci with RNAs longer than 2000 nt that are unlikely to encode proteins. AVAILABILITY AND IMPLEMENTATION Pinstripe (pinstripe.matticklab.com) is freely available as source code or a Mono binary. Pinstripe is written in C# and runs under the Mono framework on Linux or Mac OS X, and both under Mono and . Net under Windows. CONTACT m.dinger@garvan.org. au or j.mattick@garvan.org. au SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Proceedings of the Annual Meeting of the Cognitive Science Society

In eye-movement experiments using gaze-contingent windows, the stimulus display is continuously updated in response to the participant’s current gaze position. Usually, a window is centered at the participant’s gaze position and follows it wherever the participant looks. Within the window, all stimulus information is visible, while outside of the window at least part of the information is masked. In the present paper, we apply this technique to a face recognition task. By varying the size of the window, we gain insight into face recognition processes in humans and characterize the visual information on which face recognition relies. The results also motivate the use of gaze-contingent windows to study visual perception.