Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensional tectonics in the vicinity of a transform fault in north Iceland

At the junction between an oceanic transform fault and the adjacent ridge segments, there is normally a curved fabric. The Trollaskagi peninsula is located just south of the junction between the Kolbeinsey ridge and the Tjornes fracture zone and offers a rare opportunity for studying the three-dimensional tectonics of the associated curved fabric. For the purpose of elucidating its infrastructure, we measured the attitudes of tens of lava flows and several hundred dykes, faults, and mineral veins in 28 profiles in the northern half of the Trollaskagi peninsula. At sea level the general dip of the lava pile is 6°–14°, but in the northernmost part of the peninsula the dip is 10°–36°, with an average of 22°. Most dykes are basaltic and strike north or NNE. They are exceptional as regards thickness, frequency of multiple dykes and crustal dilation. The average thickness, 5.9 m, and a crustal dilation of 28% in a 4.5-km-long profile are the highest figures reported from regional dyke swarms in Iceland. Most faults are normal faults. The curved fabric is limited to the northernmost part of the Trollaskagi peninsula and consists of two main sets of fractures. One set includes north and NNE striking faults, mineral veins, and dykes. The other set consists of NW striking (oblique) faults, mineral veins, and tilted lava flows. A boundary element model indicates that the abnormal lava dip and the NW striking (oblique) extensional structures in the northernmost part of the peninsula are attributable to the stress field associated with the junction between the Kolbeinsey ridge and the Tjornes fracture zone.
A Model for Detecting Cost-Prone Classes Based on Mahalanobis-Taguchi Method

In software development, comprehensive software reviews and testings are important activities to preserve high quality and to control maintenance cost. However it would be actually difficult to perform comprehensive software reviews and testings because of a lot of components, a lack of manpower and other realistic restrictions. To improve performances of reviews and testings in object-oriented software, this paper proposes a novel model for detecting cost-prone classes; the model is based on Mahalanobis-Taguchi method---an extended statistical discriminant method merging with a pattern recognition approach. Experimental results using a lot of Java software are provided to statistically demonstrate that the proposed model has a high ability for detecting cost-prone classes.
A unified survey on treatment effect heterogeneity modeling and uplift modeling

A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a.k.a treatment effect). In recent years, a need for estimating the heterogeneous treatment effects conditioning on the different characteristics of individuals has emerged from research fields such as personalized healthcare, social science, and online marketing. To meet the need, researchers and practitioners from different communities have developed algorithms by taking the treatment effect heterogeneity modeling approach and the uplift modeling approach, respectively. In this paper, we provide a unified survey of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We then provide a structured survey of existing methods by emphasizing on their inherent connections with a set of unified notations to make comparisons of the different methods easy. We then review the main applications of the surveyed methods in personalized marketing, personalized medicine, and social studies. Finally, we summarize the existing software packages and present discussions based on the use of methods on synthetic, semi-synthetic and real world data sets and provide some general guidelines for choosing methods.
Aggregation effects of surface heterogeneity in land surface processes

Abstract. In order to investigate the aggregation effects of surface heterogeneity in land surface processes we have adapted a theory of aggregation. Two strategies have been adopted: 1) Aggregation of radiative fluxes. The aggregated radiative fluxes are used to derive input parameters that are then used to calculate the aerodynamic fluxes at different aggregation levels. This is equivalent to observing the same area at different resolutions using a certain remote sensor, and then calculating the aerodynamic fluxes correspondingly. 2) Aggregation of aerodynamic fluxes calculated at the original observation scale to different aggregation levels. A case study has been conducted to identify the effects of aggregation on areal estimates of sensible and latent heat fluxes. The length scales of surface variables in heterogeneous landscapes are estimated by means of wavelet analysis.
A Descriptive Literature Review and Classification of Cloud Computing Research

We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.
Exploring evolution-based & -free protein language models as protein function predictors

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particular, AlphaFold [20], a ground-breaking AI system, could potentially reshape structural biology. However, the utility of the PLM module in AlphaFold, Evoformer, has not been explored beyond structure prediction. In this paper, we investigate the representation ability of three popular PLMs: ESM-1b (single sequence) [32], MSA-Transformer (multiple sequence alignment) [27] and Evoformer (structural), with a special focus on Evoformer. Speciﬁcally, we aim to answer the following key questions: (i) Does the Evoformer trained as part of AlphaFold produce representations amenable to predicting protein function? (ii) If yes, can Evoformer replace ESM-1b and MSA-Transformer? (iii) How much do these PLMs rely on evolution-related protein data? In this regard, are they complementary to each other? We compare these models by empirical study along with new insights and conclusions. Finally, we release code and datasets for reproducibility.
The effect of user characteristics on the efficiency of visual querying

Information systems increasingly provide options for visually inspecting data during the process of information discovery and exploration. Little research has dealt so far with user interactions with these systems, and specifically with the effects of characteristics of the displayed data and the user on performance with such systems. The study reports an experiment on users' performance with a visual exploration system. Participants had to identify target graphs within a large set of candidate graphs by using visual filtering criteria that differed in their efficiency for reducing the number of candidate graphs. A pay-off matrix and a time limit served to motivate users to select filter criteria efficiently. Performance was measured as the number of correct identifications of target graphs within a time-limit, and the number, type and position of filter criteria selected for the search. Efficiency was somewhat biased by users' preference to select filter criteria sequentially, starting from the left to the right. Rational and experiential cognitive styles affected performance, and they interacted with learning and the types of filter criteria chosen. The study shows that not only visual search tools can be used effectively but also that data and user characteristics affect task performance with such systems.
Liquefaction analysis of Duncan Dam using strength ratios

B.C. Hydro performed extensive seismic safety studies for Duncan Dam in the late 1980s and early 1990s. The results of these studies indicated that Duncan Dam was reasonably safe from liquefaction-induced failure. A recently proposed liquefaction analysis procedure that uses strength ratios to assess liquefaction triggering and post-triggering stability was used to revisit the liquefaction analysis of Duncan Dam. When compared to the detailed field and analytical studies performed by B.C. Hydro, the proposed liquefaction analysis procedure predicts: (i) similar zones of liquefaction within a contractive unit of foundation sand; (ii) similar shear strengths at the triggering of liquefaction (yield shear strength) and at large strain (liquefied shear strength); and (iii) similar post-triggering factors of safety for slope stability. The good agreement suggests that the strength ratio based liquefaction analysis approach is relatively robust and is suitable for screening level studies for large projects or m.
A 150MS/s 133μW 7b ADC in 90nm digital CMOS Using a Comparator-Based Asynchronous Binary-Search sub-ADC

In this paper, a 2-step 7b ADC consists of a TVH, followed by a 1b comparison and D/A conversion, and a 6b comparator-based asynchronous binary-search (CABS) conversion. The 7b ADC operates as follows: the passive T/H samples the input signal on a capacitance, the 1b comparator determines the sign of the input and steers a capacitive DAC. The DAC subtracts 1/4 of the full-scale range in charge from one of the input nodes, changing simultaneously differential signal and common-mode level to be in range of the 6b CABS converter. The clock buffer generates the 1b coarse A/D clock signal and starts the 6b fine conversion after the 1b D/A conversion has finished.
USENIX Association Proceedings of USITS ’ 03 : 4 th USENIX Symposium on Internet Technologies and Systems

We present NPS, a novel non-intrusive web prefetching system that (1) utilizes only spare resources to avoid interference between prefetch and demand requests at the server as well as in the network , and (2) is deployable without any modi cations to servers, browsers, network or the HTTP protocol. NPS's self-tuning architecture eliminates the need for traditional \thresholds" or magic numbers typically used to limit interference caused by prefetching, thereby allowing applications to improve bene ts and reduce the risk of aggressive prefetching. NPS avoids interference with demand requests by monitoring the responsiveness of the server and accordingly throttling the prefetch aggressiveness, and by using TCP-Nice, a congestion control protocol suitable for low priority transfers. NPS avoids the need to modify existing infrastructure by modifying HTML pages to include Javascript code that issues prefetch requests and by wrapping the server infrastructure with several simple external modules that require no knowledge of or no modi cations to the internals of existing servers. Our measurements of the prototype under a web trace indicate that NPS is both non-interfering and eÆcient under di erent network load and server load conditions. For example, in our experiments with a loaded server with little spare capacity, we observe that a threshold-based prefetching scheme causes response times to increase by a factor of 2 due to interference, whereas prefetching using NPS decreases response times by 25%.
York University at TREC 2007: Genomics Track

Our Genomics experiments in this year mainly focus on improving the passage retrieval performance in the biomedical domain. We address this problem by constructing difierent indexes. In particular, we propose a method to build word-based index and sentence-based index for our experiments. The passage mean average precision (passage MAP) for our flrst run \york07ga1" using the word-based index was 0.095 and the passage MAP for our second run \york07ga2" using the sentence-based index was 0.086. However, the passage MAP for our third run \york07ga3" using both the word-based index and UMLS for query expansion degraded to 0.060. All these three o‐cial runs are automatic. The evaluation results show that using the word-based index is more efiective than using the sentence-based index for improving the passage retrieval performance. We flnd that pseudo-relevance feedback can make a positive contribution to the retrieval performance. However, we also flnd that query expansion using UMLS and Entrez Gene does not improve the retrieval performance, and in some cases it makes a negative contribution to the retrieval performance.
Deep Latent Variable Models for Semi-supervised Paraphrase Generation

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction ( VSAR ), which performs latent sequence inference given an observed text. To leverage information from text pairs, we introduce a supervised model named dual directional learning ( DDL ). Combining VSAR with DDL ( DDL+VSAR ) enables us to conduct semi-supervised learning; however, the combined model suffers from a cold-start problem. To combat this issue, we propose to deal with better weight initialisation, leading to a two-stage training scheme named knowledge reinforced training. Our empirical evaluations suggest that the combined model yields competitive performance against the state-of-the-art supervised baselines on complete data. Furthermore, in scenarios where only a fraction of the labelled pairs are available, our combined model consistently outperforms the strong supervised model baseline ( DDL ) by a signiﬁcant margin.
Comparing Multiple Regression, Principal Componant Analysis, Partial Least Square Regression and Ridge Regression in Predicting Rangeland Biomass in the Semi Steppe Rangeland of Iran

In this paper, the prediction of rangeland biomass using different methods including Multiple regression, Principal Component Analysis, Partial Least Square regression and Ridge regression were compared. For this goal, environmental factors such as elevation (m), rainfall (mm), slope (٪), caco3 (٪), Sand (٪), Sand (٪), Clay (٪), pH, EC (ds.m-1), Saturation (SP (٪)) were used to determine a relationship between environmental factors and the forage yield. The results showed that PLS, and ridge regressions were among the best models to predict rangeland biomass followed by multiple regressions and Principal component Analysis. PLS and ridge regression had a higher predicted accuracy than other calculation methods. It was shown that, the Partial Least Square regression values to R, RMSE and MAE were 0.83, 34.9 and 26.23, respectively.
Multivariate multi-way analysis of multi-source data

Motivation: Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently increasingly important experiments where data is obtained from multiple sources. Common examples of such settings include integrated analysis of metabolic and gene expression profiles, or metabolic profiles from several tissues in our case, in a controlled multi-way experimental setup where disease status, medical treatment, gender and time-series are usual covariates. Results: We extend the applicability area of multivariate, multi-way ANOVA-type methods to multi-source cases by introducing a novel Bayesian model. The method is capable of finding covariate-related dependencies between the sources. It assumes the measurements consist of groups of similarly behaving variables, and estimates the multivariate covariate effects and their interaction effects for the discovered groups of variables. In particular, the method partitions the effects to those shared between the sources and to source-specific ones. The method is specifically designed for datasets with small sample sizes and high dimensionality. We apply the method to a lipidomics dataset from a lung cancer study with two-way experimental setup, where measurements from several tissues with mostly distinct lipids have been taken. The method is also directly applicable to gene expression and proteomics. Availability: An R-implementation is available at http://www.cis. hut.fi/projects/mi/software/multiWayCCA/ Contact: ilkka.huopaniemi@tkk.fi; samuel.kaski@tkk.fi
Seeing Is Slicing: Observation Based Slicing of Picture Description Languages

Program slicing has seen a plethora of applications and variations since its introduction over thirty years ago. The dominant method for computing slices involves significant complex source-code analysis to model the dependences in the code. A recently introduced alternative, Observation-Based Slicing (ORBS), sidesteps this complexity by observing the behavior of candidate slices. ORBS has several other strengths, including the ability to slice multi-language systems. However, ORBS remains rooted in tradition as it captures semantics by comparing sequences of values. This raises the question of whether it is possible to extend slicing beyond its traditional semantic roots. A few existing projects have attempted this, but the extension requires considerable effort. If it is possible to build on the ORBS platform to more easily generalize slicing to languages with non-traditional semantics, then there is the potential to vastly increase the range of programming languages to which slicing can be applied. ORBS supports this by reducing the problem to that of generalizing how semantics are captured. Taking Picture Description Languages as a case study, the challenges and effectiveness of such a generalization are considered. The results show that not only is it possible to generalize the ORBS algorithm, but the resulting slicer is quite effective removing from 27% to 98% of the original source code with an average of 85%. Finally a qualitative look at the slices finds the technique very effective, at times producing minimal slices.
Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction

Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently, different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper, we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.
Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded with Thin-Film Intrafascicular Electrodes

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback.
Scalable Safe Exploration for Global Optimization of Dynamical Systems

Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for complex systems while giving safety and optimality guarantees. Our experiments on a robot arm that would be prohibitive for GoSafe demonstrate that GoSafeOpt safely finds remarkably better policies than competing safe learning methods for high-dimensional domains.
Enhancing Information Security Risk Management with Security Analytics: A Dynamic Capabilities Perspective

The importance of information security risk management (ISRM) and its potential strategic role in protecting organisational information assets is widely studied in literature. Less attention is given to how ISRM can be enhanced using security analytics to contribute to a competitive advantage. This paper proposes a model showing that security analytics capabilities (the ability to effectively use security data for informed security related decision making) and ISRM capabilities (the ability to effectively identify and protect organizational information assets) indirectly influence competitive advantage in ISRM through two key mediating links: analytics-enabled ISRM capabilities (the ability to effectively leverage insights gleaned from security data to make informed ISRM decisions) and ISRM dynamic capabilities (the ability to reconfigure analytics-enabled ISRM capabilities to address turbulent environments). Environmental turbulence moderates the process by which security analytics and ISRM capabilities influence competitive advantage. The paper concludes by calling for evaluation and refinement of the research model.