Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodetic observation of contemporary deformation in the northern Walker Lane: 1. Semipermanent GPS strategy

As of October 2005, the semipermanent Global Positioning System (GPS) network called MAGNET (Mobile Array of GPS for Nevada Transtension) included 60 stations and spanned 160 km (N-S) × 260 km (E-W) across the northern Walker Lane and central Nevada seismic belt. MAGNET was designed as a cheaper, higher-density alternative to permanent networks in order to deliver high-accuracy velocities more rapidly than campaigns. The mean nearest-neighbor spacing is 19 km (13–31 km range). At each site, the design facilitates equipment installation and pickup within minutes, with the antenna mounted precisely at the same location to mitigate eccentricity error and intersession multipath variation. Each site has been occupied ~50% of the time to sample seasonal signals. Using a custom regional fi ltering technique to process 1.5 yr of intermittent time series, the longest-running sites are assessed to have velocity accuracies of ~1 mm/yr. The mean weekly repeatability is 0.5 mm in longitude, 0.6 mm in latitude, and 2.1 mm in height. Within a few years, MAGNET will characterize strain partitioning in the northern Walker Lane to improve models of (1) geothermal activity, which is largely amagmatic in the Great Basin, (2) seismic hazard, (3) the ways in which northern Walker Lane accommodates strain between the Sierra Nevada block and the extending Basin and Range Province, and (4) Neogene development of the northern Walker Lane and its broader role in the ongoing evolution of the Pacifi c–North America plate-boundary system. MAGNET’s design is generally applicable to regions with an abundance of vehicle-accessible rock outcrops and could be replicated elsewhere.
Experimental specification mining for enterprise applications

Specification mining infers abstractions over a set of program execution traces. Whereas inductive approaches to specification mining rely on a given set of execution traces, experimental approaches systematically generate and execute test cases to infer rich models including uncommon and exceptional behavior. State-of-the-art experimental mining approaches infer low-level models representing the behavior of single classes. This paper proposes an approach for inferring models of built-in processes in enterprise systems based on systematic scenario test generation. The paper motivates the approach, sketches the relevant concepts and challenges, and discusses related work.
Language Learning Histories and Learner Variables in an MA TEFL Programme: A Well-being Perspective

Highlighting the complex and dynamic nature of learning English as a Foreign Language (EFL) from the perspective of the well-being theory and following an intact group design, this study analyzed Iranian MA TEFL students' (N = 37) language learning histories with regard to their language learning strategy use, multiple intelligences, language learning aptitude, and resiliency. T-test revealed a few significant differences in the 24 components of well-being (VIA Institute of Character, 2014) in students' language learning histories between males and females. Qualitative analysis of ten students' coping strategies in negative circumstances showed that their language learning experiences included positive/negative emotions, meaningful engagement, relationships, and accomplishments albeit with some differences. Hierarchical multiple regression analysis revealed that learner variables play significant roles in shaping students' well-being. Detailed results along with discussions and implications for theory and practice are presented in the study.
An Empirical Investigation of the Underlying Behavioral Processes of Trip Chaining

Trip chaining is a phenomenon that has a significant impact on urban transportation and activity systems. This paper argues that an appropriate representation of the underlying behavioral processes in models of trip chaining is crucial to the capability and reliability of the models. To examine the behavioral processes, data on the complete processes of activity scheduling and trip chaining were collected with a computerized survey instrument, REACT! The scheduling horizons of sojourn activities were analyzed with contingency tables. The results of this analysis indicate that some of the decision elements entailed in trip chaining were opportunistically formed within constraints set by previously planned activities. While engaged in earlier activities, individuals might see opportunities of carrying out certain activities at different locations occurring later in the day. The decisions as to whether to take these opportunities or not would depend on their evaluation of scheduling feasibility (e.g., the travel time required to reach these activities). However, the analysis also illustrates that some trip chains were indeed executed as planned, suggesting optimality and potential routine behavior. Based on the empirical evidence, transactional opportunistic planning within a constrained environment is viewed as a potential behavioral model for trip chaining behavior.
Completeness of Chop

Interval-based temporal logics are a natural framework for modeling a number of problems from various areas of computer science such as artificial intelligence, natural language processing, temporal databases and formal specification. Some interval-based temporal logics became popular in recent years, such as Venema’s CDT logic, Halpern and Shoham’s HS logic, Moszkowski’s ITL and its propositional version, and Goranko, Montanari, and Sciavicco’s PNL. Although completeness results are available for some of these, to the best of our knowledge such a result was missing for propositional ITL, which can be also viewed as the proper fragment of CDT with only chop plus the modal constant for point-intervals. In this paper we tackle this problem, and we show that the logic C is sound and complete.
Recovery of Current through Mutated TASK3 Potassium Channels Underlying Birk Barel Syndrome

TASK3 (TWIK-related acid-sensitive K+ channel 3) potassium channels are members of the two-pore–domain potassium channel family. They are responsible for background leak potassium currents found in many cell types. TASK3 channels are genetically imprinted, and a mutation in TASK3 (G236R) is responsible for Birk Barel mental retardation dysmorphism syndrome, a maternally transmitted developmental disorder. This syndrome may arise from a neuronal migration defect during development caused by dysfunctional TASK3 channels. Through the use of whole-cell electrophysiologic recordings, we have found that, although G236R mutated TASK3 channels give rise to a functional current, this current is significantly smaller in an outward direction when compared with wild-type (WT) TASK3 channels. In contrast to WT TASK3 channels, the current is inwardly rectifying. Furthermore, the current through mutated channels is differentially sensitive to a number of regulators, such as extracellular acidification, extracellular zinc, and activation of Gαq-coupled muscarinic (M3) receptors, compared with WT TASK3 channels. The reduced outward current through mutated TASK3_G236R channels can be overcome, at least in part, by both a gain-of-function additional mutation of TASK3 channels (A237T) or by application of the nonsteroidal anti-inflammatory drug flufenamic acid (FFA; 2-{[3-(trifluoromethyl)phenyl]amino}benzoic acid). FFA produces a significantly greater enhancement of current through mutated channels than through WT TASK3 channels. We propose that pharmacologic enhancement of mutated TASK3 channel current during development may, therefore, provide a potentially useful therapeutic strategy in the treatment of Birk Barel syndrome.
Overcoming Resolution Limits in MDL Community Detection

A popular approach to community detection in networks is to search for partitions that maximize modularity [7]. However, recent work has identified two important limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Compression-based approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two compressionbased algorithms are themselves subject to a resolution limit, identifies the aspect of each approach that is responsible for the resolution limit, proposes a variant, SGE (Sparse Graph Encoding), that addresses this limitation, and demonstrates on three artificial data sets that (1) SGE does not exhibit a resolution limit on graphs in which other approaches do, and that (2) modularity and the compression-based algorithms, including SGE, behave similarly on graphs not subject to the resolution limit.
Metformin increases antitumor activity of MEK inhibitors through GLI1 downregulation in LKB1 positive human NSCLC cancer cells

Purpose Metformin, widely used as antidiabetic drug, showed antitumoral effects expecially in combination with chemotherapy. Our group recently has demonstrated that metformin and gefitinib are synergistic in LKB1-wild-type NSCLC cells. In these models, metformin as single agent induced an activation and phosphorylation of mitogen-activated-protein-kinase (MAPK) through an increased C-RAF/B-RAF heterodimerization. Experimental design Since single agent metformin enhances proliferating signals through the RAS/RAF/MAPK pathway, and several MEK inhibitors (MEK-I) demonstrated clinical efficacy in combination with other agents in NSCLC, we tested the effects of metformin plus MEK-I (selumetinib or pimasertib) on proliferation, invasiveness, migration abilities in vitro and in vivo in LKB1 positive NSCLC models harboring KRAS wild type and mutated gene. Results The combination of metformin with MEK-I showed a strong anti-proliferative and proapoptotic effect in Calu-3, H1299, H358 and H1975 human NSCLC cell lines, independently from the KRAS mutational status. The combination reduced the metastatic behaviour of NSCLC cells, via a downregulation of GLI1 trascritional activity, thus affecting the transition from an epithelial to a mesenchymal phenotype. Metformin and MEK-Is combinations also decreased the production and activity of MMP-2 and MMP-9 by reducing the NF-jB (p65) binding to MMP-2 and MMP-9 promoters. Conclusions Metformin potentiates the antitumor activity of MEK-Is in human LKB1-wild-type NSCLC cell lines, independently from the KRAS mutational status, through GLI1 downregulation and by reducing the NF-jB (p65)-mediated transcription of MMP-2 and MMP-9.
Within-Person Variability in Social Evaluation

When meeting someone for the first time, we not only extract a wealth of information about their age, gender, ethnicity, or mood, but we also evaluate them on social dimensions such as attractiveness, trustworthiness, or dominance. What makes these social inferences important and interesting is the fact that people agree with each other’s evaluations and that they can influence our attitudes and behaviours, even if evidence for their accuracy is only limited. Existing face evaluation models focus on the identity level, arguing that a person is either, say, trustworthy or untrustworthy, regardless of the many different ways they might look. Recent evidence, however, suggests that images of the same person can vary just as much as images of different people, i.e. people rather have trustworthy- or untrustworthy-looking images of themselves. Here, I explore the spread and magnitude of such within-person variability in social evaluation. This is accomplished by sampling natural face variability and using images with different pose, emotional expression, lighting, etc. that are representative of real life social interactions. In addition to idiosyncratic variability, experiments described here aim to examine social evaluation across gender and familiarity as well as investigate the implications of trait inferences for face recognition. I then address social evaluation across modality, integrating visual information from the face and acoustic information from the voice. My findings show comparable within- and between-person variability in social ratings and demonstrate that idiosyncratic variability alone can bring about significant changes in trait attribution. This suggests that social evaluation depends on both identity and image properties. Finally, I demonstrate the automaticity of audiovisual integration in social evaluation and show that the relative contribution of face and voice cues is different for the two fundamental social dimensions. Ultimately, this brings us a step closer to understanding integrated person perception.
Model-based reinforcement learning as cognitive search : Neurocomputational theories

One oft-envisioned function of search is planning actions, e.g. by exploring routes through a cognitive map. Yet, among the most prominent and quantitatively successful neuroscentific theories of the brain’s systems for action choice is the temporal difference account of the phasic dopamine response. Surprisingly, this theory envisions that action sequences are learned without any search at all, but instead wholly through a process of reinforcement and chaining. This chapter considers recent proposals that a related family of algorithms, called model-based reinforcement learning, may provide a similarly quantitative account for action choice by cognitive search. It reviews behavioral phenomena demonstrating the insufficiency of temporal difference-like mechanisms alone, then details the many questions that arise in considering how model-based action valuation might be implemented in the brain and in what respects it differs from other ideas about search for planning.
Last-Mile Navigation Using Smartphones

Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FollowMe, a "last-mile" navigation system to enable plug-and-play navigation in indoor and semi-outdoor environments. FollowMe exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FollowMe is infrastructure-free, energy-efficient and cost-saving. We implemented FollowMe on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m2. Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.
The Magnitude, Destinations, and Determinants of Mathematics and Science Teacher Turnover

This study examines the magnitude, destinations, and determinants of mathematics and science teacher turnover. The data are from the nationally representative Schools and Staffing Survey and the Teacher Follow-Up Survey. Over the past two decades, rates of mathematics and science teacher turnover have increased but, contrary to conventional wisdom, have not been consistently different than those of other teachers. Also, contrary to conventional wisdom, mathematics and science teachers were also no more likely than other teachers to take noneducation jobs, such as in technological fields or to be working for private business or industry. The data also show there are large school-to-school differences in mathematics and science turnover; high-poverty, high-minority, and urban public schools have among the highest rates. In the case of cross-school migration, the data show there is an annual asymmetric reshuffling of a significant portion of the mathematics and science teaching force from poor to not-poor schools, from high-minority to low-minority schools, and from urban to suburban schools. A number of key organizational characteristics and conditions of schools accounted for these school differences. The strongest factor for mathematics teachers was the degree of individual classroom autonomy held by teachers. Net of other factors such as salaries, schools with less classroom autonomy lose math teachers at a far higher rate than other teachers. In contrast, for science teachers salary was the strongest factor, while classroom autonomy was not strongly related to their turnover.
Prototizer: Agile on Steroids

The model-driven software development (MDSD) vision has booked significant advances in the past decades. MDSD was said to be very promising in tackling the “wicked” problems of software engineering in general. However, a decade later MDSD is still far from becoming widely recognized within the mainstream software development. At the same time Agile software development methodologies are widely considered as the way to go. This seems mysterious as MDSD seems to be the right methodology to boost Agile approaches. From a MDSD perspective, the design models are nothing but a waste within an Agile software development process. In this experience report, we present Prototizer a tool based on modeldriven software engineering that could boost the Agile vision. We present a validation of Prototizer on a recent case study and discuss the main lessons learned throughout the past years.
SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity

We propose a general information-theoretic approach called Seraph (SEmi-supervised metRic leArning Paradigm with Hyper-sparsity) for metric learning that does not rely upon the manifold assumption. Given the probability parameterized by a Mahalanobis distance, we maximize the entropy of that probability on labeled data and minimize it on unlabeled data following entropy regularization, which allows the supervised and unsupervised parts to be integrated in a natural and meaningful way. Furthermore, Seraph is regularized by encouraging a low-rank projection induced from the metric. The optimization of Seraph is solved efficiently and stably by an EM-like scheme with the analytical E-Step and convex M-Step. Experiments demonstrate that Seraph compares favorably with many well-known global and local metric learning methods.
Intelligence in Future NASA Swarm-based Missions

NASA is researching advanced technologies for future exploration missions using intelligent swarms of robotic vehicles. One of these missions is the Autonomous Nano Technology Swarm (ANTS) mission that will explore the asteroid belt using 1,000 cooperative autonomous spacecraft. The emergent properties of intelligent swarms make it a potentially powerful concept, but at the same time more difficult to design and implement. In addition, a reliance on intelligence in each of the spacecraft and collective intelligence in teams of spacecraft will be needed to successfully execute maneuvers in and around asteroids. Designing, verifying and validating this collective intelligence will be one of the major challenges of the
MAV Stabilization using Machine Learning and Onboard Sensors

In many situations, Miniature Aerial Vehicles (MAVs) are limited to using only on-board sensors for navigation. This limits the data available to algorithms used for stabilization and localization, and current control methods are often insufficient to allow reliable hovering in place or trajectory following. In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path. This predicted drift will allow the MAV to adjust it's flightpath to maintain a desired course.
Content-based music structure analysis with applications to music semantics understanding

In this paper, we present a novel approach for music structure analysis. A new segmentation method, beat space segmentation, is proposed and used for music chord detection and vocal/instrumental boundary detection. The wrongly detected chords in the chord pattern sequence and the misclassified vocal/instrumental frames are corrected using heuristics derived from the domain knowledge of music composition. Melody-based similarity regions are detected by matching sub-chord patterns using dynamic programming. The vocal content of the melody-based similarity regions is further analyzed to detect the content-based similarity regions. Based on melody-based and content-based similarity regions, the music structure is identified. Experimental results are encouraging and indicate that the performance of the proposed approach is superior to that of the existing methods. We believe that music structure analysis can greatly help music semantics understanding which can aid music transcription, summarization, retrieval and streaming.
Low Density Parity Check Codes Based on Finite Geometries: A Rediscovery and More

Low density parity check (LDPC) codes with iterative decoding based on belief propagation achieve astonishing error performance close to Shannon limit. No algebraic or geometric method for constructing these codes has been reported and they are largely generated by computer search. As a result, encoding of long LDPC codes is in general very complex. This paper presents two classes of high rate LDPC codes whose constructions are based on finite Euclidean and projective geometries, respectively. These classes of codes a.re cyclic and have good constraint parameters and minimum distances. Cyclic structure adows the use of linear feedback shift registers for encoding. These finite geometry LDPC codes achieve very good error performance with either soft-decision iterative decoding based on belief propagation or Gallager's hard-decision bit flipping algorithm. These codes can be punctured or extended to obtain other good LDPC codes. A generalization of these codes is also presented.
Scale-Space Random Walks

The Random Walks image segmentation algorithm provides a fast and effective method for supervised image segmentation. However, Random Walks does not work very well in the presence of noise or texture. Therefore, we propose an augmented version of Random Walks known as “Scale-Space Random Walks” (SSRW) that addresses these problems. Through a minor, though non-trivial, modification to the Random Walks algorithm, we show that the SSRW can produce more accurate segmentations in the presence of noise and texture then the original Random Walks can.