Miracle-dz/UniFAR-FTUs huggingface dataset:
Active inference and agency

Abstract I greatly enjoyed Seth’s compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)—which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth’s key argument.
Using visual attention to extract regions of interest in the context of image retrieval

Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.
Energy efficiency in a steel plant using optimization-simulation

The recent years showed a significant increase both in energy costs and social awareness for environmental concerns. In the industrial sector, these aspects affected revenues and sustainability of the most intensive energy plants, such as steel mills. The present paper describes how simulation may powerfully support production planning and the related decisions. The application proposed refers to a steel plant feeding its Continuous Casting line by an electric Arc Furnace. The optimization of the production plan is pursued by an objective function which takes into account both traditional targets (e.g., lead times and due dates) and the need for an efficient use of energy. To this end, a discrete/continuous simulation model was developed: stochastic laws, suitable for temperature variations modelling, were implemented to forecast energy requirements at the Ladle Furnaces and/or the Vacuum Degasser. Final results showed how the simulation‐based Decision Support System may lead to a significant reduction (more than 20%) of the daily consumption of electric energy.
Reducing test time of embedded SRAMs

Compared with traditional functional fault models, fault model obtained from an Inductive Fault Analysis (IFA) test flow can provide an attractive basis for obtaining a good estimate of the overall test quality in terms of defect level and yield. However, the associated surging test time due to increased SRAM capacity is becoming a major challenge when testing either standalone or embedded SRAMs. This paper refines the functional fault models translated from defect simulations for embedded SRAMs with IFA proposed and described. Reconsidering the defect causes of the functional faults allows us to simplify the functional fault model FFM2 and formulate the test time required for detecting Data Retention Faults. We combine this simplification with the consideration of specific memory redundancy elements to develop a new March 6N Test algorithm. Simulation results reveal that our proposed fault modeling and test generation algorithm can reduce total test time to one half or less of that required by the methodology, while maintaining the same defect and fault coverage.
Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing

This paper analyzes and compares different incentive mechanisms for a client to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. Data acquisition from a large number of users is essential to build a rich database and support emerging location-based services. We propose a reward-based collaboration mechanism, where the client announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users willing to collaborate. We show that if the client knows the users' collaboration costs, then he can choose to involve only users with the lowest costs by offering a small total reward. However, if the client does not know users' private cost information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Distributed computing aims to solve computational intensive problems in a distributed and inexpensive fashion. We study how the client can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the client will involve a user type as long as the client's preference for that type outweighs the corresponding cost. All collaborators achieve a zero payoff in this case. But if the client does not know users' private cost information, he will conservatively target at a smaller group of efficient users with small costs. He has to give most benefits to the collaborators, and a collaborator's payoff increases in his computing efficiency.
Unscented External Force Estimation for Quadrotors and Experiments

In this paper, we describe an algorithm, based on the well-known Unscented Quaternion Estimator, to estimate external forces and torques acting on a quadrotor. This formulation uses a non-linear model for the quadrotor dynamics, naturally incorporates process and measurement noise, requires only a few parameters to be tuned manually, and uses singularity-free unit quaternions to represent attitude. We demonstrate in simulation how the proposed algorithm outperforms existing methods, namely a non-linear observer, and how our approach can be used to generate force and torque profiles from experimental data that are useful in controller design. Finally, we show how the resulting controllers enable a quadrotor to track the center of wind induced by a fan, and avoid catastrophic loss of lift due to downwash.
TAKING ADVANTAGE OF UNLABELED DATA WITH THE ORDERED CLASSIFICATION ALGORITHM

We introduce a new method for improving poor performance of classi£ers due to a small training set. The Ordered Classi£cation algorithm presented here incrementally increases the training set by adding unlabeled examples. These unlabeled examples are selected by the algorithm accordingly to the con£dence level of the predictions made by an ensemble of classi£ers. The use of this con£dence level measurement, which was inspired by the Query By Committee approach within the Active Learning setting, ensures that the algorithm incorporates the examples which are more likely to have the right classi£cation label assigned by the ensemble. Experimental results show that this algorithm effectively takes advantage of the unlabeled data yielding an error reduction of up to 78%. Giving that a very common scenario in classi£cation problems is the lack of a large enough training set, this algorithm provides a practical solution.
Indirect effects of domestic and wild herbivores on butterflies in an African savanna

Indirect interactions driven by livestock and wild herbivores are increasingly recognized as important aspects of community dynamics in savannas and rangelands. Large ungulate herbivores can both directly and indirectly impact the reproductive structures of plants, which in turn can affect the pollinators of those plants. We examined how wild herbivores and cattle each indirectly affect the abundance of a common pollinator butterfly taxon, Colotis spp. , at a set of long-term, large herbivore exclosure plots in a semiarid savanna in central Kenya. We also examined effects of herbivore exclusion on the main food plant of Colotis spp. , which was also the most common flowering species in our plots: the shrub Cadaba farinosa. The study was conducted in four types of experimental plots: cattle-only, wildlife-only, cattle and wildlife (all large herbivores), and no large herbivores. Across all plots, Colotis spp. abundances were positively correlated with both Cadaba flower numbers (adult food resources) and total Cadaba canopy area (larval food resources). Structural equation modeling (SEM) revealed that floral resources drove the abundance of Colotis butterflies. Excluding browsing wildlife increased the abundances of both Cadaba flowers and Colotis butterflies. However, flower numbers and Colotis spp. abundances were greater in plots with cattle herbivory than in plots that excluded all large herbivores. Our results suggest that wild browsing herbivores can suppress pollinator species whereas well-managed cattle use may benefit important pollinators and the plants that depend on them. This study documents a novel set of ecological interactions that demonstrate how both conservation and livelihood goals can be met in a working landscape with abundant wildlife and livestock.
Accurate prediction of power consumption in sensor networks

Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are commonly battery-driven. Although recent research focuses strongly on energy-aware applications and operating systems, energy consumption is still a limiting factor. Once sensor nodes are deployed, it is challenging and sometimes even impossible to change batteries. As a result, erroneous lifetime prediction causes high costs and may render a sensor network, useless before its purpose is fulfilled. In this paper, we present AEON (accurate prediction of power consumption), a novel evaluation tool to quantitatively predict energy consumption of sensor nodes and whole sensor networks. Our energy model, based on measurements of node current draw and the execution of real code, enables accurate prediction of the actual energy consumption of sensor nodes. Consequently, it prevents erroneous assumptions on node and network lifetime. Moreover, our detailed energy model allows us to compare different low power and energy aware approaches in terms of energy efficiency. Thus, it enables a highly precise estimation of the overall lifetime of a sensor network.
Multilineage Differentiation Potential of Bone and Cartilage Cells Derived from Explant Culture

To date, mesenchymal stem cells (MSCs) from various tissues have been reported, but the yield and differentia- tion potential of different tissue-derived MSCs is still not clear. This study was undertaken in an attempt to investigate the multilineage stem cell potential of bone and cartilage explant cultures in comparison with bone marrow derived mesen- chymal stem cells (BMSCs). The results showed that the surface antigen expression of tissue-derived cells was consistent with that of mesenchymal stem cells, such as lacking the hematopoietic and common leukocyte markers (CD34, CD45) while expressing markers related to adhesion (CD29, CD166) and stem cells (CD90, CD105). The tissue-derived cells were able to differentiate into osteoblast, chondrocyte and adipocyte lineage pathways when stimulated in the appropriate differentiating conditions. However, compared with BMSCs, tissue-derived cells showed less capacity for multilineage differentiation when the level of differentiation was assessed in monolayer culture by analysing the expression of tissue- specific genes by reverse transcription polymerase chain reaction (RT-PCR) and histology. In high density pellet cultures, tissue-derived cells were able to differentiate into chondrocytes, expressing chondrocyte markers such as proteoglycans, type II collagen and aggrecan. Taken together, these results indicate that cells derived from tissue explant cultures re- served certain degree of differentiation properties of MSCs in vitro.
Saving the public from the private? Incentives and outcomes in dual practice

We consider a setting of dual practice, where a physician offers free public treatment and, if allowed, a private treatment for which patients have to pay out of pocket. Private treatment is superior in terms of health outcomes but more costly and time intensive. For the latter reason it generates waiting costs. As patients differ in their propensity to benefit from private treatment and in their costs of waiting for treatment, we study the physician's incentives to supply private care and to allocate waiting time to public and private sectors and contrast it with the first-best allocation. The physician shifts waiting costs to public patients in order to increase the willingness-topay for private treatment. While this waiting time allocation turns out to be socially optimal, the resulting positive network effect leads to an over-provision of private care if and only if waiting costs are sufficiently high. A second-best allocation arises when the health authority selects physician reimbursement in the public segment but has no control over private provision. Depending on the welfare weight the health authority attaches to physician profits a ban of dual practice may improve on the second-best allocation. Due to patient heterogeneity, such a ban would affect patients differently.
Enhancing literacy practices in science classrooms through a professional development program for Canadian minority-language teachers

ABSTRACT Literacy in the Science Classroom Project was a three-year professional development (PD) program supporting minority-language secondary teachers’ use of effective language-based instructional strategies for teaching science. Our primary objective was to determine how teacher beliefs and practices changed over time and how these were enacted in different classrooms. We also wanted to identify the challenges and enablers to implementing these literacy strategies and practices at the classroom, school, and district levels. Data collection involved both qualitative and quantitative methodologies: student questionnaires; interviews with teachers, principals, and mentor; and focus groups with students. The findings suggest that the program had an impact on beliefs and practices commensurate with the workshop participation of individual teachers. These language-enhanced teacher practices also had a positive impact on the use of talking, reading and writing by students in the science classroom. Finally, continuing PD support may be needed in certain jurisdictions for strengthening minority-language programs given the high teacher mobility in content-area classrooms evident in this study.
A new image denoising algorithm based on nonsubsampled contourlet transform

A new postprocessing method based on nonsubsampled contourlet transform is introduced in this paper for suppressing additive Gaussian Noise (AWGN) in images. By transforming the noised image into nonsubsampled contourlet domain, the lowest-frequency subband is filtered by wiener filter firstly. According to the adaptive thresholds, the contourlet coefficients in different subbands are filtered. Experimental results show that our image denoising algorithm achieves the better performance than the traditional wiener filter method and the denoising algorithms based on wavelet or contourlet.
The Stor-e-Motion Visualization for Topic Evolution Tracking in Text Data Streams

Nowadays, there are plenty of sources generating massive amounts of text data streams in a continuous way. For example, the increasing popularity and the active use of social networks result in voluminous and fastflowing text data streams containing a large amount of user-generated data about almost any topic around the world. However, the observation and tracking of the ongoing evolution of topics in these unevenly distributed text data streams is a challenging task for analysts, news reporters, or other users. This paper presents “Store-Motion” a shape-based visualization to track the ongoing evolution of topics’ frequency (i.e., importance), sentiment (i.e., emotion), and context (i.e., story) in user-defined topic channels over continuous flowing text data streams. The visualization supports the user in keeping the overview over vast amounts of streaming data and guides the perception of the user to unexpected and interesting points or periods in the text data stream. In this work, we mainly focus on the visualization of text streams from the social microblogging service Twitter, for which we present a series of case studies (e.g., the observation of cities, movies, or natural disasters) applied on real-world data streams collected from the public timeline. However, to further evaluate our visualization, we also present a baseline case study applied on the text stream of a fantasy book series.
Influence of cultural factors on leisure sports behavior of city dwellers in Shenzhen

This study was designed to investigate the relationships among cultural factors and leisure sports behavior of city dwellers which based on Karahanna's behavior theory. Structural equation modeling techniques were used to test the hypothesized relations in a sample of 383 dwellers in Shenzhen, China. SPSS 16.0 and AMOS16.0 were employed to analyze survey data to consider the interactive influences of cultural value, social support, cognition, skills and behavior of leisure sports among city dwellers. The results show that four cultural factors are affecting dwellers' leisure sport in varying degrees, and then according to the results,some rational suggestions are proposed.
Cortical Source Multivariate EEG Synchronization Analysis on Amnestic Mild Cognitive Impairment in Type 2 Diabetes

Is synchronization altered in amnestic mild cognitive impairment (aMCI) and normal cognitive functions subjects in type 2 diabetes mellitus (T2DM)? Resting eye-closed EEG data were recorded in 8 aMCI subjects and 11 age-matched controls in T2DM. Three multivariate synchronization algorithms (S-estimator (S), synchronization index (SI), and global synchronization index (GSI)) were used to measure the synchronization in five ROIs of sLORETA sources for seven bands. Results showed that aMCI group had lower synchronization values than control groups in parietal delta and beta2 bands, temporal delta and beta2 bands, and occipital theta and beta2 bands significantly. Temporal (r = 0.629; P = 0.004) and occipital (r = 0.648; P = 0.003) theta S values were significantly positive correlated with Boston Name Testing. In sum, each of methods reflected that the cortical source synchronization was significantly different between aMCI and control group, and these difference correlated with cognitive functions.
Formal Refinement of Software Architectures Based on Rewriting Logic

As the end-users’ expectations increase, software systems grow in size and complexity. To avoid design errors that are expensive when detected during the implementation phase, a high level model of the system is needed, i.e. the software architecture. Any architectural design is aiming to produce an executable software, but the manual application code generation may introduce errors. However, a few software architecture description languages propose refinement mechanisms leading from an abstract architectural specification to a more complete, more detailed description of the system. This paper presents a new and formal approach of architecture refinement facilitating software development, using transformation patterns based on rewriting logic.
A data mining application on moving object data

With the wide availability of GPS devices in our lives, massive amounts of object movement data have been collected from various moving object targets, such as mobile devices, animals, and vehicles. In the last decade, Moving Object Databases (MOD) have attracted many researchers. Analyzing such data has deep implications in many areas, such as ecological study and traffic control. In this study, we focus on moving object data (moving points) analysis and retrieve valuable information for knowledge discovery. In this research, a moving object data model is implemented in the object-relational database system, additionally some special queries and data mining techniques are performed. Retrieving information directly from unorganized spatial-temporal data is almost impossible. However, not only a vast amount of spatial-temporal data sets organized into MOD data model but also the discovery of valuable knowledge from spatial-temporal data to help decision support processes is possible now owing to this research implementation.
Multiple Alignment of Genomic Sequences

Genomic sequence is currently available from ENTREZ for more than 40 eukaryotic and 157 prokaryotic organisms. As part of the ongoing NIH Intramural Sequencing Center’s, Comparative Vertebrate Sequencing project, genomic sequences will soon be available from 50 vertebrates for regions orthologous to defined regions of the human genome. Managing and interpreting these sequence data requires new computational tools, including programs designed to align multiple genomic sequences. Biologists can use such alignments to identify functional elements (coding regions and transcription factor binding sites, as well as highly conserved elements whose exact function(s) remain to be determined, e.g. the recently described “ultraconserved” elements (Bejerano et al., 2004)), to understand the evolution of genome sequence and structure, and for phylogenetic analysis (for reviews see (Boffelli et al., 2004; Dubchak and Frazer, 2003; Frazer et al., 2003; Ureta-Vidal et al., 2003).
SONGBIRD PREMOTOR NUCLEUS HVC AUDITORY RESPONSES ARE ROBUST, DYNAMIC, AND REFLECT LEARNED PREDICTIVE VALUES IN AWAKE ZEBRA FINCHES By EFE SOYMAN

Premotor and motor areas of the human brain are known to take part in various perceptual phenomena, the most significant of which is speech perception. The present study tests the involvement of a premotor vocal structure in perception of various acoustic signals, using the zebra finch (Taeniopygia guttata), a songbird species that provides the best-studied model organism for vocal learning. Water-deprived zebra finches were trained in a differential classical conditioning task in which CS+ stimuli predicted a water reward while CS-stimuli did not result in any outcome. When stimulus discrimination reached criterion, a set of novel auditory stimuli was passively presented in the absence of water-deprivation outside of the training apparatus ~20 hours before the neural recordings. In awake, restrained birds, electrophysiological activity in response to bird's own song (BOS), CS+, CS-, passively familiar (PasFam), and novel stimuli was recorded from multiple sites in the vocal premotor nucleus HVC and the HVC Shelf region bilaterally. Multi-unit iii responses in HVC were found to be highest for BOS, followed by CS+ and Novel, which were higher than CS-stimuli. Furthermore, responses decreased in magnitude with stimulus repetition (adapted) for all stimuli except BOS. BOS-bias over Novel stimuli was found to be higher in right than in left HVC. In HVC Shelf, there was no BOS-biased activity and responses to all stimuli decreased with repeated presentation. Responsive single neurons in HVC showed the same pattern as multi-unit activity among stimulus categories, but, as a population, their responses did not adapt to stimulus repetition. Overall, the results suggest that the premotor nucleus HVC has robust and dynamic auditory responses that also reflect learned predictive values in awake zebra finches. These neural mechanisms may potentially reveal general principles that can be applied to understand the role of human speech production brain regions in recognition and discrimination of speech sounds.