Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase Ib/II trial evaluating the safety, tolerability and immunological activity of durvalumab (MEDI4736) (anti-PD-L1) plus tremelimumab (anti-CTLA-4) combined with FOLFOX in patients with metastatic colorectal cancer

Background 5-Fluorouracil plus irinotecan or oxaliplatin alone or in association with target therapy are standard first-line therapy for metastatic colorectal cancer (mCRC). Checkpoint inhibitors targeting PD-1/PD-L1 demonstrated efficacy on mCRC with microsatellite instability but remain ineffective alone in microsatellite stable tumour. 5-Fluorouracil and oxaliplatin were known to present immunogenic properties. Durvalumab (D) is a human monoclonal antibody (mAb) that inhibits binding of programmed cell death ligand 1 (PD-L1) to its receptor. Tremelimumab (T) is a mAb directed against the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). This study is designed to evaluate whether the addition of PD-L1 and CTLA-4 inhibition to oxaliplatin, fluorouracil and leucovorin (FOLFOX) increases treatment efficacy. Methods This phase II study (ClinicalTrials.gov NCT03202758) will assess the efficacy and safety of FOLFOX/D/T association in patients with mCRC (n=48). Good performance status patients (Eastern Cooperative Oncology Group <2) with untreated, RAS mutational status mCRC will be eligible. Prior adjuvant therapy is allowed provided recurrence is >6 months postcompletion. There is a safety lead in nine patients receiving FOLFOX/D/T. Assuming no safety concerns the study will go on to include 39 additional patients. Patients will receive folinic acid (400 mg/m²)/5-fluorouracil (400 mg/m² as bolus followed by 2400 mg/m2 as a 46-hour infusion)/oxaliplatin (85 mg/m2) every 14 days with D (750 mg) D1 every 14 days and T (75 mg) D1 every 28 days. After six cycles of FOLFOX only D/T will continue until disease progression, death, intolerable toxicity, or patient/investigator decision to stop. Primary endpoint is safety and efficacy according to progression-free survival (PFS); secondary endpoints include overall response rate and quality of life. Hypothesis is that a PFS of 50% at 6 months is insufficient and a PFS of 70.7% is expected (with α=10%, β=10%). Blood, plasma and tumour tissue will be collected and assessed for potential prognostic and predictive biomarkers.
Closing With Emotion: The Differential Impact of Male Versus Female Attorneys Expressing Anger in Court

Emotion expression is a key part of trial advocacy. Attorneys are advised to gain credibility with juries by demonstrating conviction through anger expression. In 3 experiments, we tested whether expressing anger in court makes attorneys more effective and whether this depends on their gender. We randomly assigned participants (n = 120 undergraduates) to view a male or female attorney presenting the same closing argument in either a neutral or angry tone (Experiment 1). They reported their impressions of the attorney and how likely they would be to hire the attorney. People used the positive aspects of anger (e.g., conviction, power), to justify hiring an angry male attorney. Yet, they used the negative aspects of anger (e.g., shrill, obnoxious), to justify not hiring a female attorney. We replicated this effect in Experiment 2 with a community sample (n = 294). Experiment 3 (n = 273) demonstrated that the attorney anger by gender interaction generalized to perceptions of effectiveness across a set of additional attorney targets. Finally, a high-powered analysis collapsing across experiments confirmed that when expressing anger relative to when calm, female attorneys were seen as significantly less effective, while angry male attorneys were seen as significantly more effective. Women might not be able to harness the persuasive power of expressing anger in the courtroom, which might prevent female attorneys from advancing in their careers.
Dynamic contraction of tensor with quantum algorithm

In the paper, contraction of a dynamic vector (i.e., both the number and the individuals of elements in the vector are non-deterministic) in a tensor is programmed. It accumulates complete N-combinations of one element of every M-set by recursion algorithm for object-oriented programming. Here, N is dynamic, i.e., uncertain before user's programming. This algorithm has overcome the difficulty of writing loop sentences in uncertain arity, yet the complexity of computation is exponential. So a quantum algorithm is proposed for realizing the computation in an acceptable time.
Parameter-free aggregation of value functions from multiple experts and uncertainty assessment in multi-criteria evaluation

This paper makes a threefold contribution to spatial multi-criteria evaluation (MCE): firstly by presenting a new method concerning value functions, secondly by comparing different approaches to assess the uncertainty of a MCE outcome, and thirdly by presenting a case-study on land-use change. Even though MCE is a well-known methodology in GIScience, there is a lack of practicable approaches to incorporate the potentially diverse views of multiple experts in defining and standardizing the values used to implement input criteria. We propose a new method that allows generating and aggregating non-monotonic value functions, integrating the views of multiple experts. The new approach only requires the experts to provide up to four values, making it easy to be included in questionnaires. We applied the proposed method in a case study that uses MCE to assess the potential of future loss of vineyards in a wine-growing area in Switzerland, involving 13 experts from research, consultancy, government, and practice. To assess the uncertainty of the outcome three different approaches were used: firstly, a complete Monte Carlo simulation with the bootstrapped inputs, secondly a one-factor-at-a-time variation, and thirdly bootstrapping of the 13 inputs with subsequent analytical error propagation. The complete Monte Carlo simulation has shown the most detailed distribution of the uncertainty. However, all three methods indicate a general trend of areas with lower likelihood of future cultivation to show a higher degree of relative uncertainty.
Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring

We use dynamic time warping (DTW) as supervision for training a convolutional neural network (CNN) based keyword spotting system using a small set of spoken isolated keywords. The aim is to allow rapid deployment of a keyword spotting system in a new language to support urgent United Nations (UN) relief programmes in parts of Africa where languages are extremely under-resourced and the development of annotated speech resources is infeasible. First, we use 1920 recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a DTW-based template matching system and apply it to untranscribed broadcast speech. Then, we use the resulting DTW scores as targets to train a CNN on the same unlabelled speech. In this way we use just 34 minutes of labelled speech, but leverage a large amount of unlabelled data for training. While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our CNN system is several orders of magnitude faster at runtime than the DTW system, it represents the most viable keyword spotter on this extremely limited dataset.
Rectification of Images Distorted by Microlens Array Errors in Plenoptic Cameras

A plenoptic cameras is a sensor that records the 4D light-field distribution of target scenes. The surface errors of a microlens array (MLA) can cause the degradation and distortion of the raw image captured by a plenoptic camera, resulting in the confusion or loss of light-field information. To address this issue, we propose a method for the local rectification of distorted images using white light-field images. The method consists of microlens center calibration, geometric rectification, and grayscale rectification. The scope of its application to different sized errors and the rectification accuracy of three basic surface errors, including the overall accuracy and the local accuracy, are analyzed through simulation of imaging experiments. The rectified images have a significant improvement in quality, demonstrating the provision of precise light-field data for reconstruction of real objects.
A Deeper Look at Power Normalizations

Power Normalizations (PN) are very useful non-linear operators in the context of Bag-of-Words data representations as they tackle problems such as feature imbalance. In this paper, we reconsider these operators in the deep learning setup by introducing a novel layer that implements PN for non-linear pooling of feature maps. Specifically, by using a kernel formulation, our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN. Linearization of such a kernel results in a positive definite matrix capturing the second-order statistics of the feature vectors, to which PN operators are applied. We study two types of PN functions, namely (i) MaxExp and (ii) Gamma, addressing their role and meaning in the context of nonlinear pooling. We also provide a probabilistic interpretation of these operators and derive their surrogates with well-behaved gradients for end-to-end CNN learning. We apply our theory to practice by implementing the PN layer on a ResNet-50 model and showcase experiments on four benchmarks for fine-grained recognition, scene recognition, and material classification. Our results demonstrate state-of-the-part performance across all these tasks.
European In-Situ Snow Measurements: Practices and Purposes

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction”. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.
How Do Crowdworker Communities and Microtask Markets Influence Each Other? A Data-Driven Study on Amazon Mechanical Turk

Crowdworker online communities — operating in fora like mTurkForum and TurkerNation — are an important actor in microwork markets. Albeit central to market dynamics, how the behavior of crowdworker communities and the dynamics of online marketplaces influence each other is yet to be understood. To provide quantitative evidence of such influence, we performed an analysis on 6-years worth of mTurk market activities and community discussions in six fora. We investigated the nature of the relationships that exist between activities in fora, tasks published in mTurk, requesters for such tasks, and task completion speed. We validate -- and expand upon — results from previous work by showing that (i) there are differences between market demand and community activities that are specific to fora and task types; (ii) the temporal progression of HIT availability in the market is predictive of the upcoming amount of crowdworker discussions, with significant differences across fora and discussion categories; (iii) activities in fora can have a significant positive impact on the completion speed of tasks available in the market.
Kernel Deep Regression Network for Touch-Stroke Dynamics Authentication

Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a classifier separately. In this letter, we propose a stacking-based deep learning network that performs feature extraction and classification, collectively dubbed Kernel Deep Regression Network (KDRN). The KDRN is built on multiple kernel ridge regressions (KRR) hierarchically, where each is trained analytically and independently. In principal, KDRN does not mean to learn directly from the raw touch-stroke data like other deep learning models, but it relearns from the pre-extracted features to yield a richer and a relatively more discriminative feature set. Subsequent to that, the authentication is carried out by KRR. Overall, KDRN achieves an equal error rate of 0.013% for intrasession authentication, 0.023% for intersession authentication, and 0.121% for interweek authentication on the Touchlaytics dataset.
Grounding Natural Language to Goals for Abstraction , Generalization , and Interpretability

Language is universal and powerful: it allows people to communicate their ideas, telegraph their intents, and interact with the world around them in an incredibly natural way. Therefore, as new technology is developed and integrated into everyday life, it is necessary to factor in language, and build interfaces that bridge the gap between human users and arbitrarily complex systems. Language grounding is an instrumental part of this process, defined as the problem of mapping natural language to behavior on an underlying system or environment. These systems and environments can take many forms, including physical systems like robots (mapping language to low-level actuators), software systems like databases and constraint solvers (translating language to programs and structured queries), or environments like real-world landscapes and photographic images (bridging language and perception). In this thesis, we examine the general problem of language grounding as it relates to two separate and important application areas: the first is the problem of grounding in the context of human-robot interaction, while the second is the problem of grounding in the context of question-answering on short stories. We use these two application areas to guide our exploration of three different themes in language as a whole, and how they manifest themselves in the problem of language grounding: 1) abstraction, or how to interpret language of varying complexities, 2) generalization, or how to extrapolate and map language to new behaviors, and 3) interpretability, or how to reason about language in an understandable way. Each chapter of this thesis explores one of the aforementioned themes in greater detail and motivates its utility before surveying related approaches and evaluating a proposed, novel approach that better addresses the theme in question. Building on techniques from deep learning, natural language processing, reinforcement learning, and planning, we introduce a full suite of diverse models for language grounding, each demonstrating state-of-the-art performance on tasks spanning human-robot interaction and question-answering. Finally, we discuss next steps, describing the limitations of the current work in language grounding before outlining several important open problems that motivate future research directions.
Digital hardware implementation of the forward/inverse kinematics for a SCARA robot manipulator

In this work, the development of a forward/inverse kinematics IP (intellectual property) based on FPGA (Field Programmable Gate Array) technology is proposed which expects to complete the overall computation of the forward/inverse kinematics within few microseconds. However, FPGA is a suitable solution in this issue because it has the ability of the synchronous parallel processing and the high-speed operation. Firstly, the forward/inverse kinematics for SCARA robot is derived in this paper. Secondly the circuit behavior using VHDL (Very-High-Speed-IC Hardware Description Language) is described. The parameterized function is applied in the designed VHDL to increase the code flexible and the FSM (Finite state machine) is adopted to reduce the hardware resource usage in FPGA. Finally, the developed forward/inverse kinematics IP will be downloaded to FPGA to confirm its effectiveness and correctness.
Effects of Dietary Grape Pomace Supplementation on Performance, Carcass Traits and Meat Quality of Lambs

Background/Aim: A previous study revealed that the inclusion of grape pomace (GP) in the diet for growing lambs had beneficial effects on the redox status and fecal microbiota. Herein, we investigated the effect of GP inclusion on performance, carcass traits and fatty acid composition of meat. Materials and Methods: In the experimental trial of 55 days, lambs were fed with standard or diet supplemented with GP. Performance, carcass traits and fatty acid profile of quadriceps muscle were assessed. Results: GP inclusion in the diet improved growth performance, since the average daily gain was significantly increased by 2-fold in GP group. Regarding the fatty acid composition of meat, GP inclusion significantly increased the content of long chain n-3 fatty acids, eicosapentaenoic acid and docosahexaenoic acid, and reduced the n-6/n-3 ratio compared to the control group. Conclusion: GP supplementation in lamb diet may improve performance and may have beneficial effects on meat quality.
Breakthrough of drain current capability and on-resistance limits by gate-connected superjunction MOSFET

This paper reports a new structure of Gate-connected Superjunction (GS) MOSFET to cope with both high drain current density and low on-resistance. The conventional superjunction (SJ) structure is attractive to reduce the specific on-resistance dramatically due to the charge compensation concept. The drain saturation current density, however, is limited by JFET depletion at the bottom region of the SJ structure even if the on-resistance can be reduced by the lateral SJ pitch narrowing. The accumulation-mode operation is effective not only for low on-resistance but also for suppressing the depletion at the SJ bottom due to the accumulation carriers. This paper reports the potential of the GS-MOSFET for high drain current density and low on-resistance based on the simulation results. Dynamic characteristics are also compared with the conventional SJ-MOSFET.
Web Services in Document Image Analysis - Recent Developments on DIVAServices and the Importance of Building an Ecosystem

Web Services are being adapted into the workflows of many Document Image Analysis researchers. However, so far, there is no common platform for providing access to algorithms in the community. DIVAServices aims to become this by providing a platform that is open to the whole community to provide their own methods as Web Services. In this paper we present updates and enhancements made to the existing DIVAServices platform. This includes a new computational backend, a revamped execution workflow based on asynchronous communication, and the possibility for methods to specify their outputs. Furthermore, we discuss the importance of an ecosystem for such platforms. We argue that only providing a RESTful API is not enough. Users need tools and services around the framework that support them in adapting the Web Services and we introduce some of the tools that we built around DIVAServices.
Vitamin D supplement use and associated demographic, dietary and lifestyle factors in 8024 South Asians aged 40–69 years: analysis of the UK Biobank cohort

Abstract Objective Vitamin D deficiency (serum 25-hydroxyvitamin D<25 nmol/l) is extremely common in Western-dwelling South Asians but evidence regarding vitamin D supplement usage in this group is very limited. This work identifies demographic, dietary and lifestyle predictors associated with vitamin D supplement use. Design Cross-sectional analysis of baseline vitamin D supplement use data. Setting UK Biobank cohort. Subjects In total, 8024 South Asians (Bangladeshi, Indian, Pakistani), aged 40–69 years. Results Twenty-three per cent of men and 39 % of women (P<0·001) (22 % of Bangladeshis, 32 % of Indians, 25 % of Pakistanis (P<0·001)) took a vitamin-D-containing supplement. Median vitamin D intakes from diet were low at 1·0–3·0 µg/d, being highest in Bangladeshis and lowest in Indians (P<0·001). Logistic regression modelling showed that females had a higher odds of vitamin D supplement use than males (OR=2·02; 95 % CI 1·79, 2·28). A lower supplement usage was seen in younger persons (40–60 years v. >60 years: OR=0·75; 95 % CI 0·65, 0·86) and in those living outside Greater London (OR=0·53 to 0·77), with borderline trends for a lower BMI, higher oily fish intake and higher household income associated with increased odds of vitamin D supplement use. Conclusions Vitamin D supplements were not used by most South Asians and intakes from diet alone are likely to be insufficient to maintain adequate vitamin D status. Public health strategies are now urgently required to promote the use of vitamin D supplements in these specific UK South Asian subgroups.
Enhancement of surface bioactivity on carbon fiber-reinforced polyether ether ketone via graphene modification

Background and objective The modulus of carbon fiber-reinforced polyether ether ketone (CFR-PEEK), a composite containing layers of carbon fiber sheets, can be precisely controlled to match bone. However, CFR-PEEK is biologically inert and cannot promote bone apposition. The objective of this study was to investigate whether graphene modification could enhance the bioactivity of CFR-PEEK. Methods and results In vitro, the proliferation and differentiation of rat bone marrow stromal cells on scaffolds were quantified via cell-counting kit-8 assay and Western blotting analysis of osteoblast-specific proteins. Graphene modification significantly promoted bone marrow stromal cell proliferation and accelerated induced differentiation into osteogenic lineages compared to cells seeded onto nongraphene-coated CFR-PEEK. An in vivo rabbit extraarticular graft-to-bone healing model was established. At 4, 8, and 12 weeks after surgery, microcomputed tomography analyses and histological observations revealed significantly better microstructural parameters and higher average mineral apposition rates for graphene-modified CFR-PEEK implants than CFR-PEEK implants (P<0.05). van Gieson staining indicated more new bone was formed around graphene-modified CFR-PEEK implants than CFR-PEEK implants. Conclusion Graphene may have considerable potential to enhance the bioactivity and osseointegration of CFR-PEEK implants for clinical applications.