Miracle-dz/UniFAR-FTUs huggingface dataset:
MGMT is down-regulated in rats with all-trans retinoic acid-induced spina bifida aperta independently of promoter DNA methylation

The loss of DNA repair genes can cause embryonic teratogenicity and lethality, several DNA repair genes have been reported to be associated with neural tube defects (NTD). O6-methylguanine DNA methyltransferase (MGMT), as one of the DNA repair enzymes has been reported in other congenital malformations, but it is less frequently reported in NTDs. DNA damage was assessed by detecting γ-H2A.X in ATRA-induced SBA rats. Real-time PCR (RT-PCR) was used to examine the mRNA expression of O6-methylguanine DNA methyltransferase (MGMT) in normal and SBA spinal cords. MGMT promoter methylation was analysed using bisulfite sequencing PCR (BSP). Statistical analyses were performed using Student’s t-test and one-way Analysis of Variance (ANOVA). Our data showed that in normal controls, the MGMT mRNA expression was decreased with increasing embryonic days, and decreased dramatically on E11 to E14 (P=0.0483) and reached a minimum on E18 (vs E11, P=0.0031). In SBAs, γ-H2A.X was significantly increased (P=0.0174), and the mRNA expression of MGMT was significantly descreased on E14, E16 and E18 (P=0.0029, 0.0425 and 0.0386, respectively). The BSP results showed that almost all CpG sites in the MGMT promoter remained unmethylated in both SBAs and controls and no significant difference was detected between the two groups in either E14 or E18 embryos. Our results showed that DNA damage occurred in ATRA-induced SBA rat foetuses. The mRNA expression of MGMT is down-regulated in ATRA-induced SBA foetuses, and this down-regulation is independent of promoter DNA methylation.
Under-Keel Clearance of Frigates and Destroyers in Shallow Water

For RAN ships operating in shallow water, it is important to understand the phenomena of squat and wave-induced motions so as to avoid grounding. The British Admiralty Manual of Navigation gives guidelines to predict squat allowances in different conditions. In this article, we discuss current research in this field, and highlight the important phenomenon of transcritical squat and its implications for frigateand destroyer-type ships. What is squat? Squat is a ship’s tendency to sit lower in the water as it travels faster. This is caused by the Bernoulli effect. Water is accelerated along the sides of and beneath the hull, and the free surface must drop downwards to maintain energy conservation. The ship essentially makes its own wave trough in which it sits, as shown in Figure 1. This effect is additional to the trailing wave pattern with which we are all familiar. Figure 1: The squat effect. (Top) Ship at rest. (Bottom) Ship under way. Squat comprises a midship sinkage and a change in trim. High block-coefficient ships such as tankers and bulk carriers tend to trim down by the bow. For medium block-coefficient ships such as containerships and many warships, changes in trim are more subtle, and are sensitive to the exact hull shape. Ideally, a captain should study and understand each ship’s trim behaviour with increasing speed. This may be done with a trim inclinometer, or in the old days, marbles on the deck. For example, the captain of one of the containerships on which we did field trials in Hong Kong knew that his ship had a strong tendency to trim by the bow when under way. He therefore loaded his ship by the stern, so that it would have close to level trim when under way. How do we predict squat? The new PIANC guidelines for approach channel design (PIANC 2014) give a good overview of the state-of-the-art methods in ship squat prediction, and contain several formulae to predict squat. These formulae have mostly been developed for cargo ships, so care should be taken when using them for very slender warships. One simple formula which should be fairly accurate for frigates and destroyers in open water at low speeds is the Tuck formula: ssmidships = ccss ∇ LLPP 2 FFh
Gaze interaction using low-resolution images at 5 FPS

With eye trackers gradually becoming personal wearable devices, gaze-based interaction will become a relevant technique for wearable applications. However, it is a common belief that high-resolution images and high frame rates are desirable to achieve the accuracy and precision required for human interaction. Because of the high computational load, a wearable eye tracker would have their batteries quickly drained out. In this paper we investigate how much processing power can be saved by lowering these requirements, and still maintain the performance adequate for human interaction. We have conducted an experiment using a head-mounted Pupil Labs eye tracker. Our results from 10 participants show that accuracy and precision remain below one degree of error for image resolution of 240 lines, and frame rates as low as 5 frames per second (FPS). Using this minimum setup, we estimate that power consumption can be reduced by 90% compared to the eye tracker camera regular settings (480 lines and 30 FPS). We also propose an algorithm that successfully detects reading behavior in real-time at 5 FPS in order to demonstrate the usefulness of gaze data at such low rates.
Prognostic value of lymph node micrometastasis in patients with colorectal cancer in Dukes stages A and B (T1-T4, N0, M0).

BACKGROUND 30% of patients with colorectal cancer (CRC) in Dukes stages A and B (T1-T4, N0, M0) present tumor recurrence and die after 5 years follow up. This unexpectedly poor evolution might be attributable to the presence of lymph node micrometastasis undetected in routine examination with haematoxilin-eosine (H&E). OBJECTIVE To assess the presence of undetected micrometastasis. PATIENTS AND METHODS we conducted a retrospective study of the locoregional lymph nodes in 85 patients operated for CRC in Dukes stages A and B (T1-T4, N0, M0), using immunohistochemistry with anticytokeratin antibodies AE1/AE3. In this descriptive, inferential bivariant and survival study, we analyzed different risk factors, including local infiltration T1/T4, Dukes A/B, number of dissected lymph nodes, vascular invasion, micrometastasis, tumor recurrence and death in the context of the presence or absence of micrometastases. RESULTS Dukes stage and neoplastic angioinvasion are influential in patient prognosis; however, lymph node micrometastases were not associated with a poorer outcome of CRC. CONCLUSIONS Locorregional lymph node micrometastases detected with anticytokeratine antibodies AE1/AE3 in Dukes A and B CRC patients are not associated with reduced survival.
Image Classi cation Based on CodeBook on CodeBooks

The Bag of Features (BoF) method is one of the most popular methods to provide the state-of-the-art in image classi cation performance. One of the important issues that a ect the performance of image classi cation is the quality of the codebook and coding scheme. The simplest codebook generation and coding is a hard vector quantization (HVQ) method[1]. In HVQ, codebook is leaned by K-means (each centroid is a visual word) and each descriptor is encoded to the closest visual word (only one non-zero element per code). Wang[3] (LLC) successfully improved the image classi cation performance by incorporating locality constraint in coding. Even though, some local descriptors are more informative to represent the characteristics of a particular concept of an image/object, the unsupervised way of generating codebook does not take such class information into account with the codebook. Futhermore, There are no clear standards of how many local descriptors per class/image should be sampled to generate a codebook. In this paper, we propose a novel image classi cation approach, Locality-constrained Linear Coding with codebook on codebooks. The ow of our proposed method is, i) generate a class codebook from each class using local descriptors of the class, ii) generate a global codebook based on class codebooks, and iii) encode local descriptors to codes with LLC based on the global codebook. Since the algorithm does not go beyond the Euclidean distance based BoF approach, it is simple and easy to re-implement. Moreover, there no longer requires to try di erent sampling strategy to tune a codebook.
The Dynamic Geometric Network Model for Representing Verticalized Urban Environment and its Generation

Against the background of urbanization and the fast growth of population in big cities, there will be more and more high-rises emerged in the future. In some big cities, the various layers of public transpiration networks such as metro systems also played an essential role in the urban life. In the verticalized urban environment, the complexity of the multi-layers space system connected by various horizontal and vertical connections have been beyond people's cognition. The boundaries between private space and public space, outdoor-space and indoor-space have already blurred. The graph theory based urban spatial analysis approaches are adopted in urban studies to tackle with the urban complexity issues. However, at present, most of the methods proposed are specializing in open urban spaces, and they cannot describe the three-dimensional completely and accurately. Therefore, in this paper, a new graph theory based representation method, the Dynamic Geometric Network Model, which adapted to the verticalized urban environment will be proposed. And the approach of how to automatically generate such a representation model according to the urban layout will also be introduced.
Response of sunflower to sulphur and sulphur oxidizing biofertilizer on quality and sulphur use efficiency

Globally, sunflower ranks second to soybean among annual field crops grown for edible oil. Sunflower contains 48-53 per cent oil and 14 to 19 per cent protein. Functionally, sulphur significantly influences the yield and quality of crops, improves odour, flavors and imparts resistance to cold; and hence it is generally considered a “quality nutrient”. A field experiment was conducted at University of Agricultural Sciences, Dharwad, India to study the effect of different levels of sulphur alone or in combination with sulphur oxidizing biofertilizer in sunflower during kharif 2013. Results of the study indicated that application of 30 kg S ha with sulphur oxidizing biofertilizer resulted in higher seed oil content (39.00%) and oil yield (783 kg ha) over control (34.33%) and (482 kg ha), respectively. However, oil content in sunflower was on par with 30 kg S ha alone (38.93%) and 20 kg S ha with sulphur oxidizing biofertilizer (38.43%). Application of 10 kg S ha with sulphur oxidizing biofertilizer recorded significantly a higher (5.73%) sulphur use efficiency. Lower sulphur use efficiency was observed under control.
Oral Glucose Tolerance of Traditional Medicines in a Diabetes Induced Rat Model

Background: The purpose of this study was to investigate the oral glucose tolerance activity of aqueous extracts of Icacina tracantha (tuber) (fam Icacinaceae,) Ananas cosmos (fam. Bromeliaceae), and Uraria picta (leaves) (fam leguminosae) on a high calorie diet fed animal model to induce type II diabetes. These plants have a long history of use as anti-diabetic agents in western Nigeria. 120 male Sprague-Dawley rats were assigned into two major groups. One group was fed on normal rat chow with the other group fed on a high calorie diet for four months. The plant crude extracts were prepared according to the traditional healer method of boiling, filtering, drying and reconstituting. A nontreated group as well as a metformin dosed group of rats were used as the control and comparator respectively. Over a 3 week period, all the animals were orally dosed with the different doses of plant extracts daily. Blood was collected from of each rat prior to dosing and thereafter weekly, and analysed for glucose concentration. During this period, the animals were weighed weekly and food intake was measured every three days. An oral glucose tolerance test (OGTT) was performed after the dosing period and fasting, 0, 30, 60 and 120 minute blood samples were taken and assayed for glucose concentration. Only UP showed a significant difference in its effect on the plasma glucose lowering in the normal chow fed rats. The effect of the plant extracts on the weight, and food consumed, was minimal and in most of the groups was not significant.The administered doses of IC and UP in the normal diet fed animals significantly improved the glucose clearance rates. None of the different plant extracts had any significant effect on the glucose clearance rate in the high calorie diet fed animals. Higher doses need to be checked in future studies as well as the insulin sensitivity of the plant extracts.
Quick-Start TCP : From Theory to Practice

Router-assisted congestion control schemes are considered to be one potential solution to improve the transport protocol performance in high-speed wide-area networks. The experimental Quick-Start TCP extension uses explicit router feedback to avoid the standard Slow-Start mechanism. This allows a TCP connection to almost immediately utilize a highspeed path and can significantly speed up broadband interactive applications. Similar to other recently proposed new congestion control approaches, Quick-Start requires modifications both in TCP connection endpoints as well as in routers. While the theoretical performance benefit of Quick-Start is evident, its implementation and usage in practice has rarely been addressed so far. This paper gives an overview of our QuickStart implementation efforts and real-world experiments. First, we report from our experiences with an implementation in the Linux protocol stack, which has been extended to offer full QuickStart functionality. Another implementation of the new router functions on a network processor shows that fast path processing of Quick-Start is technically feasible at very high line speeds. Second, our measurements show how Quick-Start performs in practice, and they quantify the benefit compared to the existing TCP congestion control. Finally, we determine the performance costs of using Quick-Start. Our measurements suggest that the Quick-Start processing overhead is very moderate.
Multihop clustering algorithm for load balancing in wireless sensor networks

The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach.
Tüpa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing

Our submission for Task 1 ‘Cross-lingual Semantic Parsing with UCCA’ at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.
A compiler-based approach for GPGPU performance calibration using TLP modulation (WIP paper)

Modern GPUs are the most successful accelerators as they provide outstanding performance gain by using CUDA or OpenCL programming models. For maximum performance, programmers typically try to maximize the number of thread blocks of target programs, and GPUs also generally attempt to allocate the maximum number of thread blocks to their GPU cores. However, many recent studies have pointed out that simply allocating the maximum number of thread blocks to GPU cores does not always guarantee the best performance, and identifying proper number of thread blocks per GPU core is a major challenge. Despite these studies, most existing architectural techniques cannot be directly applied to current GPU hardware, and the optimal number of thread blocks can vary significantly depending on the target GPU and application characteristics. To solve these problems, this study proposes a just-in-time thread block number adjustment system using CUDA binary modification upon an LLVM compiler framework, referred to as the CTA-Limiter, in order to dynamically maximize GPU performance on real GPUs without reprogramming. The framework gradually reduces the number of concurrent thread blocks of target CUDA workloads using extra shared memory allocation, and compares the execution time with the previous version to automatically identify the optimal number of co-running thread blocks per GPU Core. The results showed meaningful performance improvements, averaging at 30%, 40%, and 44%, in GTX 960, GTX 1050, and GTX 1080 Ti, respectively.
UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs

We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3% accuracy) on the ‘labels-by-publisher’ sub-task and top 24 out of 44 teams (68.3% accuracy) on the ‘labels-by-article’ sub-task (65.3% accuracy). We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the ‘labels-by-article’ sub-task.
How do Startups Develop Internet-of-Things Systems - A Multiple Exploratory Case Study

Internet-of-Things applications are not only the new opportunity for digital businesses but also a major driving force for the modification and creation of software systems in all industries and businesses. Compared to other types of software-intensive products, the development of Internet-of-Things applications lacks a systematic approach and guidelines. This paper aims at understanding the methodological commonalities among startups who are developing Internet-of-Things products. Using the SEMAT Essence framework, we captured common team compositions, common types of Minimum Viable Products and common way of working in early stage Internet-of-Things startups. We found that startups include various engineering and business competence, but do not cover all of what is needed. The development of Internet-of-Things applications adopts certain speed-favor approaches, i.e. rapid prototyping, iterative development and outsourcing. The finding implies some recommendations for both researchers and practitioners in the area of Internet-of-Things development.
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers

Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., image recognition) and natural language processing (e.g., text classification). One of the benefits of transfer learning is to learn effectively and efficiently from limited labeled data with a pre-trained model. In the shared task of identifying and categorizing offensive language in social media, we preprocess the dataset according to the language behaviors on social media, and then adapt and fine-tune the Bidirectional Encoder Representation from Transformer (BERT) pre-trained by Google AI Language team. Our team NULI wins the first place (1st) in Sub-task A - Offensive Language Identification and is ranked 4th and 18th in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offense Target Identification respectively.
Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate

Hate speech occurs more often than ever and polarizes society. To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate. The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish. In the second task (B), the system is also asked to classify the following sub-tasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic. We evaluate multiple models, and finally combine them in an ensemble setting. This ensemble setting is built of five and three submodels for the English and Spanish task respectively. In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets. Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.
Design and analysis of field-logging write barriers

Write barriers are a fundamental mechanism that most production garbage collection algorithms depend on. They inform the collector of mutations to the object graph, enabling partial heap collections, concurrent collection, and reference counting. While in principle, write barriers remember only the pointers within the object graph that were changed and do so just once, widely-used write barriers are less precise, sacrificing temporal and/or spatial precision to performance. The idea of precisely remembering just the pointers that were changed is not new, however implementing performant field-precise barriers has proved elusive. We describe a technique for efficiently implementing field-logging barriers. We implement a number of barriers and evaluate them on a range of x86 hardware. A generational field-logging barrier performs with 0.1% to 1% mutator overhead compared to a highly tuned object-logging barrier, while a preliminary implementation of a reference counting field-logging barrier performs with around 1% to 2% overhead compared to a highly tuned object-logging reference counting barrier. These results suggest that garbage collection algorithms that require the precision of exactly remembering field mutations without sacrificing performance may now be possible, adding a new mechanism to the design toolkit available to garbage collection researchers.