Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of the conduction band offsets in Si/Si1−x−yGexCy and Si/Si1−yCy heterostructures using metal-oxide-semiconductor capacitors

Metal-oxide-semiconductor (MOS) capacitors fabricated on in situ doped n-type Si/Si1−x−yGexCy and Si/Si1−yCy epitaxial layers were used to study the conduction band offsets in these heterojunctions. The heterostructures were grown epitaxially in a rapid thermal chemical vapor deposition reactor. Si/Si1−x−yGexCy samples with a nominal Ge concentration of 20 at. % and carbon fractions up to 1.3 at. % were studied. Carbon fractions up to 1.6 at. % were studied for the Si/Si1−yCy samples. Gate oxides were formed by thermal oxidation of the Si cap at 750 °C. X-ray diffraction measurements confirm that the processing did not affect the strain in the layers. Devices exhibit well-behaved high frequency and quasistatic capacitance–voltage (C–V) characteristics indicating the high electronic quality of the material. Capacitance–voltage measurements performed over a range of temperatures were used to extract the band offsets. Confinement of electrons at the heterointerface is apparent in the C–V curves of the Si/Si1.
Operational Impacts of IEEE 802.1Qbv Scheduling on a Collaborative Robotic Scenario

Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications. TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs). TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities. TSN has since expanded its scope and applicability to other applications such as those in industrial environments and automotive applications. Industrial examples include machine-machine communications for robot control, end-effector actuation, real-time sensing, and safety integrated systems. Applications utilizing a wireless local area network (WLAN) can also benefit from scheduling and traffic shaping as defined in the 802.1Qbv standard; however, factors such as clock stability, synchronization, resource requirements and protocol options come into play when selecting a schedule to support multiple application types on the same network. In this article, we present a scenario for a collaborative robot heavy lift operation, in which, two robots communicate over an IEEE 802.11 WLAN with TSN capabilities to lift a rigid body in three dimensions. Scheduling is performed using 802.1Qbv over WLAN with the robot operating system (ROS) used as the software middleware utilizing the transport control protocol (TCP). As a part of the research, we describe our process for schedule selection to accommodate the time-sensitive traffic of the robotic scenario while allowing an industrial internet of things (IIoT) high data rate traffic to coexist. We then provide an analysis of the impacts of TSN schedule selection on the operational performance of the collaborative robot application.
PHENOTYPIC AND GENETIC VARIABILITY AND GENETIC DIVERGENCE IN LENTIL (Lens culinaris Medik.) GERMPLASM

: Lentil is a leguminous crop which plays important role in both human health and agriculture. Lack of genotypic and phenotypic variability limits the scope of breeding for developing high yielding lentil varieties. In order to know the genotypic and phenotypic variability and diversity in lentil, research was carried out with 30 lentil genotypes following a randomized complete block design using three replications. Highly significant variability was found for all of the traits among the genotypes under study. The genotype ILL 4127 showed superior performance whereas ILL 2894 showed poor performance. The environment had great impact on the genotypes as the PCV (phenotypic coefficient of variation) was greater than the GCV (genotypic coefficient of variation). High PCV coupled with high GCV, heritability, genetic advance and genetic advance in percentage of mean was found for number of pods plant -1 and number of seeds plant -1 . Thirty genotypes were divided into 5 clusters where cluster I and cluster V had the maximum inter-cluster distance, specifying the existence of wider genetic diversity among the genotypes of these clusters. Hence, suitable transgressive segregants might be found from crosses between genotypes of those clusters. Genetic diversity at molecular level was found 0.861 to 1.112 which indicated the existence of genetic variation within the studied genotypes. Microsatellite profiling revealed that SSR19 was the highly informative and detectable polymorphic marker followed by SSR48, SSR156, SSR33, SSR13 and SSR130. The results showed the consistency both in molecular and morphological clustering. The genotypes ILL 2894, ILL 3823, ILL 2764, ILL 3201, BM 680, ILL 2083, ILL 5103, ILL 4355, ILL 4707 and ILL 5844 were found as stable considering genetic variability and diversity under study. Therefore, these genotypes might be used as suitable breeding materials for developing advanced lentil varieties.
Late-Holocene maize cultivation, fire, and forest change at Lake Ayauchi, Amazonian Ecuador

A high-resolution paleoecological record provides a 2690 year-long fossil pollen and charcoal history from Lake Ayauchi, Ecuador, in lowland Amazonia. The record begins with a landscape that is already partially deforested and in which maize is being grown. Dated charcoal fragments from local soils coincide with fire events and peaks of land clearance seen in the lake sediment record. After c. AD 550 grass pollen becomes less abundant, as a broad array of forest types show small increases in abundance. Between c. AD 750 and 1280, Zea mays pollen was at its most abundant. Although maize cultivation continued until the AD 1700s, forest pollen abundance showed a significant increase at c. AD 1260. Another transition at c. AD 1420, which saw a transition from dominance by early successional taxa and an increase in mid-successional elements, suggests the onset of reduced human activity at the site. Fossil maize is found in a lower proportion of samples, disappearing altogether for a century in the late 1700s. Forest taxa increase in abundance and charcoal disappears from the record at c. AD 1790. These data suggest a complex social history prior to and following European arrival with phases of forest clearing and episodes of apparent regrowth at c. AD 500, 950, and 1260. Increased forest pollen after c. AD 1260 and a reduction in maize pollen abundance suggests some abandonment, with a second, relatively late, depopulation following European Conquest (c. AD 1790). Evidence is not found supporting reforestation associated with European arrival.
Economic Benefits from the Use of Mass Trapping in the Management of Diamondback Moth, Plutella xylostella, in Central America

Simple Summary Introduction of monitoring of male diamondback moth using lures emitting female sex pheromone allowed farmers to determine the populations of adult DBM in cabbages in several cabbage farms in Costa Rica and Nicaragua. Farmers were encouraged to use trap capture data to trigger insecticide applications as an alternative to their normal practice of calendarized insecticide applications. Those farmers with the highest insecticide application rates against DBM were the most receptive to the idea of using pheromone-baited traps and reduced their spray regime by the largest proportion. Reduced spray schedules do not increase damage but can increase profits for farmers. Abstract The diamondback moth, Plutella xylostella (L.), is a worldwide pest of brassica crops, resistant to a large number of insecticides. As an alternative, the use of pheromone-baited traps has been proposed but farmers are yet to be convinced. In the present study, we aimed to validate the benefits of the use of pheromone-baited traps for monitoring and mass trapping in cabbage production in Central America as means of Integrated Pest Management (IPM) in comparison to calendarized insecticide sprays, which are the farmers’ current practices (FCP). Mass trapping was established in nine selected plots of cabbage in Costa Rica and Nicaragua. Average captures of males/trap/night, plant damage and net profits of these IPM plots were compared to simultaneously evaluated or historically reported FCP plots. The results indicate that in Costa Rica, trap captures did not justify the application of insecticides and average net profits increased by more than 11% when the trapping methods were implemented. In Nicaragua, IPM plots were able to reduce insecticide applications to one third of those in FCP plots. These results confirm the economic and environmental benefits of pheromone-based management of DBM in Central America.
The gradient flow of Higgs pairs

In this paper, we consider the gradient flow of the Yang-Mills-Higgs functional of Higgs pairs on a Hermitian vector bundle (E,H0) over a Kahler surface (M,? ), and study the asymptotic behavior of the heat flow for Higgs pairs at infinity. The main result is that the gradient flow with initial condition (A0,? 0) converges, in an appropriate sense which takes into account bubbling phenomena, to a critical points (A? ,? ) of this functional. We also prove that the limiting Higgs pair (A? ,? ) can be extended smoothly to a vector bundle E? over (M,? ) , and the isomorphism class of the limiting Higgs bundle (E? ,A? ,? ) is given by the double dual of the graded Higgs sheaves associate to Harder-Narasimhan-Seshadri filtration of the initial Higgs bundle (E,A0,? 0).
Importance of Facts for Natural Language Generation

Natural Language Generation is the task of producing understandable human text from variety of input sources. With the advent of pretrained language models (PLMs), text generation capabilities of current systems have achieved unprecedented heights. Pretrained language models have become the new normal and naturally serve as a backbone architecture for numerous tasks. It has been observed that these models learn various intricacies involved in language understanding and generation during pretraining step. These models are pretrained over large corpus; they also discover the world knowledge through text, some of which are absorbed in model parameters. Although, during finetuning on certain knowledge-intensive tasks (like text coherence, data-to-text, summarization, translation, etc.), it fails to utilize the intrinsic knowledge stored in it’s parameters effectively. In this thesis, we will tackle this problem by incorporating external facts to improve results on two downstream tasks: multilingual facts-to-text generation and text coherence modeling. We observe close association of facts in improving text generation by directly focusing on fact-to-text generation task. The fact-to-text generation is a variant of data-to-text where structured input data is knowledge graph triples. Data-to-text generative system consumes structured input data like tables, databases, knowledge bases, time-series data etc., and produces human-readable text summaries. The first part of the thesis addresses the multilingual fact-to-text generation, where facts are used to generate sentences in multiple languages. The fact-to-text generation requires a dataset where knowledge graph triples are well-aligned with semantically equivalent textual information. Manual creation of such a highquality fact-to-text dataset requires human supervision and is quite challenging to scale. Unsupervised alignment has recently emerged as an active area of research to overcome lack of labelled data and difficulty in domain adaptation. However, not much work has been done for low-resource languages that provide two significant challenges: (1) unavailability of pair of triples and native text with same content distribution and (2) limited Natural language Processing resources in low-resource languages. Hence, we rigorously investigate cross-lingual fact-to-text problem of aligning English structured data with sentences in multiple low-resource languages and develop a new dataset called XALIGN consisting of 0.45M pairs across seven low-resource languages. We propose two different methods of cross-lingual fact-to-text alignment: (a) Non-parametric approaches and (b) Parametric approaches. Additionally, we experimented with strong baseline results by adapting popular natural language generation methods for the cross-lingual fact-to-text task.
Memory in Social Interactions: The Effects of Introspection on Destination Memory in Traumatic Brain Injury

Destination memory, which is the ability to remember to whom one has sent information, is intimately associated with social cognition. We assessed whether processing attributes of destinations would improve destination memory in patients with traumatic brain injury (TBI). In this cross-sectional study, we tested the destination memory of 24 patients with TBI and 25 control participants in two conditions. On the first one (control condition), we invited participants to tell proverbs to celebrities’ faces in order to decide, on a subsequent recognition test, whether they previously told that proverb to that celebrity or not. On the second condition (experimental introspection condition), the same procedures were repeated. However, after telling the proverbs, we invited participants to introspect about what the destination might believe about the proverbs (e.g., “What do you think that the celebrities would think about the proverbs? ”). Group comparisons demonstrated better destination memory after the introspection than when no introspection was implemented in control participants, but there were no significant differences between the two conditions in patients with TBI. However, analyses of individual profiles demonstrated that more than half (n = 13) of the patients with TBI demonstrated better destination memory after introspection. While these results demonstrate a beneficial effect of introspection on destination memory for some cases of patients with TBI, more research is needed to reveal how introspection may influence patients’ memory in social interactions.
Efficacy and Contextual Dimensions of Task-Based Language Teaching: A Study on Bangladeshi Learners'

This scholarly investigation, titled "Assessing the Pedagogical Efficacy of Task-Oriented Language Didactics: A Comparative Inquiry into English Proficiency Augmentation Among Bangladeshi Learners," dissects the multi-layered implications and efficacies of task-based language teaching (TBLT) in contrast to traditional pedagogical modalities within the Bangladeshi educational context. Employing a meticulous secondary source analysis, this research scrutinizes the pedagogical pluralism in TBLT and evaluates its quantitative and qualitative effectiveness, particularly focusing on English proficiency among Bangladeshi students. The study employs a rigorous methodological framework, incorporating criteria for source selection and methods for comparative analysis. The research explicates the synthesis of analyses and extrapolates both theoretical and practical implications. The task-type effectiveness in TBLT is thoroughly interrogated, providing nuanced insights into the types of tasks that significantly contribute to language acquisition. Additionally, the paper offers an in-depth contextual analysis to elucidate the cultural and educational factors that may influence the efficacy of TBLT in Bangladesh. The conclusion aggregates the key findings, delineates limitations, and furnishes recommendations for future empirical endeavors in this realm. This paper serves as a seminal work for educators, policymakers, and researchers aiming to comprehend the complexities and efficacies of TBLT in fostering English language proficiency in non-native speakers, particularly in a Bangladeshi milieu.
On Modeling Query Refinement by Capturing User Intent through Feedback

SQL queries in relational data model implement the binary satisfaction of tuples. Tuples are generally filtered out from the result set if they miss the constraints expressed in the predicates of the given query. For naive or inexperienced users posing precise queries in the first place is very difficult as they lack of knowledge of the underlying dataset. Therefore, imprecise queries are commonplace for them. In connection with it, users are interested to have explanation of the missing answers. Even for unexpected tuples present in the result set advanced users may also want to know why a particular piece of information is present in the result set. This paper presents a simple model for generating explanations for both unexpected and missing answers. Further, we show how these explanations can be used to capture the user intent via feedback specifically for refining initial imprecise queries. The presented framework can also be thought as a natural extension for the existing SQL queries where support of explanation of expected and unexpected results are required to enhance the usability of relational database management systems. Finally, we summarize future research directions and challenges that need to be addressed in this endeavour.
SparSNP — Supplementary Information

where yi ∈ {−1,+1}. SparSNP uses the squared hinge loss as the classification model. We use the squared-hinge loss rather than the standard hinge loss since the former is twicedifferentiable whereas the latter is not. Twice-differentiability means that the Newton stepsize used in coordinate descent is the second derivative of the loss wrt βj , whereas to use coordinate descent with the hinge loss requires some pre-chosen step size, which must be found through manual tuning or a line search procedure. The disadvantage of the squaredhinge loss is that it is potentially more sensitive to outliers in the data, with the loss increasing quadratically for mis-classified samples, whereas the hinge loss increases the loss only linearly.
Meet-and-go: finding optimal single connecting points considering companionship preferences

Consider the problem of a group of friends who want to depart from their current locations (e.g., workplaces) towards a common destination (e.g., a restaurant) but also want to meet at a single intermediary point before continuing traveling together from there. The goal is to minimize an aggregate perceived travel cost by taking advantage of companionship, e.g., a pre-defined mutual affinity factor, motivated by the common impression that "time flies when in good company". This is an instance of what we name as the Optimal Single Connecting Point (OSCP) query. In this paper we present Meet-and-Go, a prototype application with a simple interface for solving instances of the (top) k-OSCP query, thus allowing the user to also explore alternative solutions as well.
Correction to "Regression Segmentation for M3 Spinal Images"

Manuscript received January 01, 1899. Date of current version July 29, 2015. Z. Wang is with the GE Healthcare, London, ON, N5Y 0A3 Canada (e-mail: zhijie.wang@ge.com). X. Zhen is with the Department of Biophisics, Western University, London, ON, N6A 3L7 Canada (e-mail: zhenxt@gmail.com). K. Tay is with the London Health Sciences Centre, London, ON, N6A 5W9 Canada (e-mail: kengyeow.tay@lhsc.on.ca). S. Osman and W. Romano are with the Department of Medical Imaging, Western University, London, ON, N6A 3K7 Canada (e-mail: said.osman@sjhc. london.on.ca; wmromano@rogers.com). S. Li is with the GE Healthcare, London, ON, N5Y 0A3 Canada (e-mail: shuo.li@ge.com). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee. org. Digital Object Identifier 10.1109/TMI.2015. 2472836 REFERENCES
A switching median filter with boundary discriminative noise detection for extremely corrupted images

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups--lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted. " For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy--in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.
An Online Algorithm for Programmatic Advertisement Placement in Supply Side Platform of Mobile Advertisement

Smartphone applications are emerging as popular media for promoting one’s products through in-app advertisements. Today, there are a number of organizations, known as supply-side-platforms (SSP), who aggregate and auction these ad-spaces from different suppliers/publishers. Advertisers (or their intermediaries) place bid for these spaces based on different relevance criteria (e.g., the location and device of the app-user, the app’s IAB category etc.), the impression value, clickthrough value, and the conversion value. After the received ads are filtered based on relevance, the SSP is often still faced with a number of options for ad-placement, each having different revenues owing to differences in clickthrough rates etc. Moreover, the SSP has to decide on the ad-placement in real-time. In this paper, we consider the SSP’s ad-placement problem in the aforementioned situation. We propose an optimization model to maximize the SSP’s revenues. Based on computational experience with this model, we develop a rule-based online algorithm that appears to be viable as a real-time solution.
Robust principal curvatures using feature adapted integral invariants

Principal curvatures and principal directions are fundamental local geometric properties. They are well defined on smooth surfaces. However, due to the nature as higher order differential quantities, they are known to be sensitive to noise. A recent work by Yang et al. combines principal component analysis with integral invariants and computes robust principal curvatures on multiple scales. Although the freedom of choosing the radius r gives results on different scales, in practice it is not an easy task to choose the most appropriate r for an arbitrary given model. Worse still, if the model contains features of different scales, a single r does not work well at all. In this work, we propose a scheme to automatically assign appropriate radii across the surface based on local surface characteristics. The radius r is not constant and adapts to the scale of local features. An efficient, iterative algorithm is used to approach the optimal assignment and the partition of unity is incorporated to smoothly combine the results with different radii. In this way, we can achieve a better balance between the robustness and the accuracy of feature locations. We demonstrate the effectiveness of our approach with robust principal direction field computation and feature extraction.