Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound-guided laser ablation for local control of neck recurrences of medullary thyroid cancer. A feasibility study

Abstract Background: Surgery is the standard treatment for cervical metastases of medullary thyroid cancer (MTC) diagnosed after initial surgical treatment. Repeated neck dissections, however, carry an elevated risk of complications, have an adverse impact on the quality of life, and sometimes do not achieve cure of the disease Clinical case: In a patient who had undergone two cervical neck dissections complicated by accessory nerve injury, an US-guided laser ablation (LA) of a lymph node metastasis of MTC was performed. LA was performed with two treatments during a five month period. The procedure was carried out with one optical fiber and an energy delivery of 3300 and 360 Joules. Treatments were well tolerated and resulted in complete structural and biochemical cure during a 12 month follow-up. No major complication was registered. Conclusions: LA is a promising tool for the management of relapsing cervical metastases that are localized in non- critical areas and are characterized by low progression rate. Advantages of LA are the outpatient setting, the absence of general anesthesia, the tolerability and the safety of the procedure. Thus, LA may be considered as an alternative approach to surgery or active surveillance for the management of local recurrences of MTC in selected patients.
Total Arterial Off-Pump Coronary Artery Bypass Grafting: A 10-Year Experience

Background: Arterial grafts had better mid-term and long-term patency than saphenous vein grafts in coronary artery bypass grafting (CABG). We summarized our experience with total arterial off-pump coronary artery bypass grafting (OPCAB) and assessed the early clinical results, surgical complications, and follow-up. Methods: From January 2007 to May 2017, 508 coronary artery disease patients undergoing total arterial OPCAB were enrolled. Clinical features, approaches, outcomes of surgical treatments, and follow-up data of these patients were studied retrospectively. A total of 122 patients underwent single left internal mammary artery (IMA)-left anterior descending artery grafts, whereas the other 386 patients underwent multiple vessel grafts. Results: The average distal anastomosis was 2.34 ± 0.97 (range: 1–4). All the patients were discharged from hospital except one died. A total of 457 (90.32%) patients were followed up. In the 4-, 7-, and 10-year follow-up groups, the rate of death from any cause was 1.19%, 6.47%, and 10.67%; rate of cardiac death was 0.60%, 2.88%, and 3.33%; rate of repeat revascularization was 0.00%, 3.60%, and 8.67%; rate of ischemic symptoms was 1.79%, 7.91%, and 11.33%; and incidence of stroke was 2.38%, 4.32%, and 6.67%, respectively. Poor medication adherence was observed in 9.38% of the follow-up population. Conclusions: Total arterial OPCAB with bilateral IMA, radial artery, and right gastroepiploic artery grafting yielded satisfactory early and midterm outcomes in this patient group, without a significant increase in early mortality or morbidity. Moreover, the long-term outcomes are also positive.
A Brief Mindfulness-Based Intervention for Primary Care Physicians: A Pilot Randomized Controlled Trial

Primary care physicians experience high rates of burnout, which results in diminished quality of life, poorer quality of care, and workforce attrition. In this randomized controlled trial, our primary aim was to examine the impact of a brief mindfulness-based intervention (MBI) on burnout, stress, mindfulness, compassion, and resilience among physicians. A total of 33 physicians completed the baseline assessment and were randomized to the Mindful Medicine Curriculum (MMC; n = 17) or waitlist control group (n = 16). Participants completed self-report measures at baseline, post-MBI, and 3-month follow-up. We also analyzed satisfaction with doctor communication (DCC) and overall doctor rating (ODR) data from patients of the physicians in our sample. Participants in the MMC group reported significant improvements in stress (P < .001), mindfulness (P = .05), emotional exhaustion (P = .004), and depersonalization (P = .01) whereas in the control group, there were no improvements on these outcomes. Although the MMC had no impact on patient-reported DCC or ODR, among the entire sample at baseline, DCC and ODR were significantly correlated with several physician outcomes, including resilience and personal achievement. Overall, these findings suggest that a brief MBI can have a positive impact on physician well-being and potentially enhance patient care.
A Novel Metamaterial Inspired High-Temperature Microwave Sensor in Harsh Environments

A high-temperature sensor based on a metamaterial unit cell is proposed in this paper. The wireless passive temperature sensing method is based on the electromagnetic backscatter principle, and thus has the advantages of higher quality, lower environmental interference, and anti-low frequency interference. We developed a finite-element method-based model for the sensor via high-frequency simulation software (HFSS). A double split-ring resonator (SRR) with an outer ring length of 13 mm was designed on alumina ceramic substrate. The sensor was fabricated at 2.42 GHz using micromechanical technology and screen printing technology. When the temperature increased from 28 to 1100 °C, the resonant frequency decreased from 2.417 to 2.320 GHz with an average sensitivity of 95.63 kHz/°C. As the sensor is easily designed and fabricated, it can be used for chipless radio frequency identification (RFID) tags by simply changing the size of rings. Furthermore, emerging 3D printing technology and commercial desktop inkjet printers will be used to realize the rapid low-cost preparation of the sensor, enabling its wide range of applications in aerospace, military, manufacturing, transportation, and other fields.
Operations Guided Neural Networks for High Fidelity Data-To-Text Generation

Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
Bounce: Designing a Physical Activity Intervention for Breast Cancer Survivors

The pervasive use of personal mobile devices provides an advantage for the delivery of a behavioral intervention anywhere and anytime. However, behavioral outcomes are dependent on adoption and sustained use of mobile-based interventions. Design approaches for engagement and feasibility of use in everyday life have been studied among the general population, especially to promote physical activity. In this paper, we discuss a design process that addresses additional barriers to adoption when the target population has symptoms, limitations, or needs resulting from a specific health condition. We designed a smartphone-based physical activity intervention for breast cancer survivors by combining behavior change theory with empirical evidence. In a preliminary evaluation, participants found the application engaging and useful, and continued to use the application briefly after the evaluation ended. We illustrate how our design process was used, and could be applied to other specific health conditions.
Flexible Tongue Housed in a Static Model of the Vocal Tract With Jaws, Lips and Teeth

Physical models of the human vocal tract with a moveable tongue have been reported in past literature. In this study, we developed a new model with a flexible tongue. As with previous models by the author, the flexible tongue is made of gel material. The shape of this model’s tongue is still an abstraction, although it is more realistic than previous models. Apart from the tongue, the model is static and solid; the gel tongue is the main part that can be manipulated. The static portion of the model is an extension of our recent static model with lips, teeth, and tongue. The entire model looks like a sagittal splice taken from an artificial human head. Because the thin, acrylic plates on the outside are transparent, the interior of the oral and pharyngeal cavities are visible. When we feed a glottal sound through a hole in the laryngeal region on the bottom of the model, different vowels are produced, dependent upon the shape of the tongue. This model is the most useful and realistic looking of the models we’ve made for speech science education so far.
A Dependency Parser for Spontaneous Chinese Spoken Language

Dependency analysis is vital for spoken language understanding in spoken dialogue systems. However, existing research has mainly focused on western spoken languages, Japanese, and so on. Little research has been done for spoken Chinese in terms of dependency parsing. Therefore, the new spoken corpus, D-ESCSC (Dependency-Expressive Speech Corpus of Standard Chinese) is built by adding new dependency relations special to spoken Chinese based on a written Chinese annotation scheme. Since spoken Chinese contains typical ill-grammatical phenomena, e.g., translocation, repetition, duplication, and omission, the new atom feature related to punctuation and three feature templates are proposed to improve a graph-based dependency parser. Experimental results on spoken Chinese corpus show that the atom feature and three templates really work and the new parser outperforms the baseline parser. To our best knowledge, it is the first work to report dependency parsing results of spoken Chinese.
Generation Z Goes to College

This group has energy for learning, they are intelligent, motivated to make a change in the world, innovative, driven, responsible, caring and understanding. As opposed to the Millennials (the « Me » generation), Gen.Z is called the « We » generation. Also called the « Digital natives » or the « Net generation », they grew up in a world shaped by Internet (one device can do everything). They are the most racially diverse generation to date. They always lived in a virtual AND physical reality. They describe themselves as loyal (85%), thoughtful, compassionate (73%), open-minded (70%), determined (74% « Hard work is part of life ») and responsible (69%). Gen.Z is predicted to be the last generation in which Caucasian are in the majority (only 55 % of Gen.Z are Caucasian) which help them welcome difference with an open mind.
SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation

This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification. We employ a neural architecture comprising of convolutional and recurrent layers with the addition of an optional CRF layer at the top. This system participated in the open track of the Parseme shared task on automatic identification of verbal MWEs due to the use of pre-trained wikipedia word embeddings. It outperformed all participating systems in both open and closed tracks with the overall macro-average MWE-based F1 score of 58.09 averaged among all languages. A particular strength of the system is its superior performance on unseen data entries.
Experimental Investigation on 3D Graphene-CNT Hybrid Foams with Different Interactions

Due to the exceptional properties of graphene, numerous possibilities for real applications in various fields have been provided. However, it is a challenge to fabricate bulk graphene materials with properties arising from the nature of individual graphene sheets, and which assemble into monolithic three-dimensional structures. If 3D structured graphene foam were made instead of 2D structured graphene, it is expected that it would be a facile fabrication, with relatively low cost with the possibility of scale-up, and would maintain the intrinsic properties of graphene. To solve the weaknesses of 2D structured graphene, this study aimed to fabricate a 3D graphene-carbon nanotubes (CNT) hybrid foam. In this study, CNT was used to reinforce the graphene foams. In addition, two different surfactants, known as sodium dodecylbenzene sulphonate (SDBS) and cetyltrimethylammonium bromide (CTAB), were applied to help CNT dispersion. The π–π interaction was induced by SDBS/CNT, while ionic interaction was derived from CTAB/CNT. To confirm the charge effect with different surfactants, SEM, Zeta-potential, FT-IR, Raman spectroscopy, and compression tests were performed. When using a cationic surfactant, CTAB, compressive modulus, and strength increased due to the formation of relatively strong ionic bonding.
HBV seroprevalence after 25 years of universal mass vaccination and management of non‐responders to the anti‐Hepatitis B vaccine: An Italian study among medical students

According to international guidelines, healthcare workers and medical students immunized against HBV are periodically tested for anti‐HBs IgG. Subjects who show an anti‐HBs titre <10 mUI/mL must receive additional vaccine doses to induce a measurable antibody response. This study aimed to evaluate the long‐time immunogenicity of anti‐hepatitis B vaccination in a sample of medical students and residents of the University of Bari who attended the Hygiene Department for biological risk assessment (April 2014‐June 2017). The strategy for the management of nonresponder subjects was evaluated. A total of 3676 students and residents were invited for testing according to a standardized protocol. Anti‐HBs IgG was tested for in 3140 (85.4%) subjects: 1174/3140 (37.7%) subjects were negative. 14.6% (128/808) of subjects who received the vaccine during their 12th year of life and 45.8% (1056/2305) of subjects immunized during the first year of life (P < 0.0001) were negative. 1005/1174 (85.6%) seronegative subjects received a booster dose, and 903/1005 (89.9%) were tested for anti‐HBs 1 month after the booster dose: 82/903 (9.1%) subjects were still negative. Of these, 56/82 (68.3%) received 2 additional doses of vaccine and 52/56 (92.9%) were tested 1 month after the third dose: 50/52 subjects (96.2%) developed a positive titre. In conclusion, several medical students, immunized at birth or at young age against HBV, did not develop protective titres against the virus. Our management strategy (booster retest; for negative subjects, 2 doses and retest) seems consistent with the purpose of evidencing immunological memory.
Domain specific approaches to software test automation

In this paper, we describe the contributions to the workshop on domain specific approaches to software test automation. The workshop consists of six accepted papers and a keynote speech. The papers tackle a wide range of topics related to the testing of applications within specific domains including event driven software, synchronous safety critical software, web applications, wireless sensor networks, and mobile phone applications. Each of the papers describes the influence of the domain on the testing process.
Assessment of Metacognition and Reversal Learning in Parkinson’s Disease: Preliminary Results

Reversal learning (RL) has been widely used for assessment of behavioral adaptation, impulsivity, obsession, and compulsion in healthy controls as well as people suffering from psychiatric and neurological disorders such as Parkinson’s disease (PD). Nevertheless, studies addressing high cognitive functions such as metacognition in PD are scarce. Here, we address for the first time the effect of levodopa and PD on metacognition within the framework of a RL paradigm. In agreement with previous reports, PD patients exhibited reversal shifting impairment with respect to healthy controls (CTRL) regardless of medication condition (MED-ON and MED-OFF), which was supported by a well-known model of learning conditioning (Rescorla–Wagner). In spite that we found a significant association between accuracy and decision confidence level for MED-OFF and CTRL, analysis of metacognitive sensitivity assessed by type 2 signal detection theory (SDT) revealed only a significant underperformance for patients without medication (MED-OFF). This finding points toward a non-compromising positive effect of dopaminergic medication on metacognition for PD.
Navigation in Real-World Environments: New Opportunities Afforded by Advances in Mobile Brain Imaging

A central question in neuroscience and psychology is how the mammalian brain represents the outside world and enables interaction with it. Significant progress on this question has been made in the domain of spatial cognition, where a consistent network of brain regions that represent external space has been identified in both humans and rodents. In rodents, much of the work to date has been done in situations where the animal is free to move about naturally. By contrast, the majority of work carried out to date in humans is static, due to limitations imposed by traditional laboratory based imaging techniques. In recent years, significant progress has been made in bridging the gap between animal and human work by employing virtual reality (VR) technology to simulate aspects of real-world navigation. Despite this progress, the VR studies often fail to fully simulate important aspects of real-world navigation, where information derived from self-motion is integrated with representations of environmental features and task goals. In the current review article, we provide a brief overview of animal and human imaging work to date, focusing on commonalties and differences in findings across species. Following on from this we discuss VR studies of spatial cognition, outlining limitations and developments, before introducing mobile brain imaging techniques and describe technical challenges and solutions for real-world recording. Finally, we discuss how these advances in mobile brain imaging technology, provide an unprecedented opportunity to illuminate how the brain represents complex multifaceted information during naturalistic navigation.
Directed test generation using symbolic grammars

We present CESI, an algorithm that combines exhaustive enumeration of test inputs from a structured domain with symbolic execution driven test generation. We target programs whose valid inputs are determined by some context free grammar. We introduce symbolic grammars, where the original tokens are replaced with symbolic constants, that link enumerative grammar-based input generation with symbolic directed testing. Symbolic grammars abstract the concrete input syntax, thus reducing the set of input strings that must be enumerated exhaustively. For each enumerated input string, which may contain symbolic constants, symbolic execution based test generation instantiates the constants based on program execution paths. The "template" generated by enumerating valid strings reduces the burden on the symbolic execution to generate syntactically valid inputs and hence exercise interesting code paths. Together, symbolic grammars provide a link between exhaustive enumeration of valid inputs and execution-directed symbolic test generation. In preliminary experiments, CESI is better than if both enumerative and symbolic techniques are used alone.
A Three-Category Face Detector with Contextual Information on Finding Tiny Faces

Great progresses have been achieved on object detection in the wild. However, it still remains a challenging problem due to tiny objects. In this paper, we present a Three-category Classification Neural Network to find tiny faces under complex environments by leveraging contextual information around faces. Tiny faces (within 20×20 pixels) are so fuzzy that the facial patterns are not clear or even ambiguous for detection. To solve this problem, instead of formulating the face detection as a two-category classification task, a novel face detection network is proposed for three-category classification, i.e., normal face, tiny face and background. Moreover, we take full advantage of contextual information around faces and pick good prior anchors to predict good detection on tiny faces. Extensive experiments on two challenging face detection benchmarks, FDDB and WIDER FACE, demonstrate the effectiveness of our method.
Scale Mapping and Dynamic Re-Detecting in Dense Head Detection

Convolutional neural networks (CNNs) have demonstrated a strong ability to extract semantics from images during object detection; however, the extracted semantics are typically have strong scale priors for a specific circumstance. In this paper, we investigate the influence of head scale and contextual information, and then propose a scale-invariant method for head detection. Our method can dynamically detect heads depending on the complexity of the image. It uses an extra feature map to represent the scale information of the spatial relationship, and then uses this feature map for auxiliary detection. Particularly, we exploit several new techniques, including contextual information, scale invariance, and hard example mining. We evaluated our method on three head datasets and achieved state-of-the-art results for the Brainwash dataset, HollywoodHeads dataset, and SCUT-HEAD dataset.
Coding Trajectory: Enable Video Coding for Video Denoising

We introduce a novel video denoising approach which can produce a clean video by utilizing redundant image patches existed in the video frames. Previous multi-frame video denosing approaches either require image registration or employ Patch Match algorithms for the discovery of the patch redundancy. However, these computations are time-consuming and prone to errors. On the other hand, nearly all captured videos have been compressed. Such a compression can produce a rich set of block-based motion vectors that can be utilized for the redundant patch extraction, leading to the efficient video denosing. To be specific, the motion vectors and frame references can be obtained from the video coding. Given a noised frame block, we follow its motion vectors from the coding to form a trajectory and gather a set of block candidates along the routes from its nearby frames. The trajectory is referred to as Coding Trajectory. Then, the corresponding denoised block is generated by weighted fusing the block candidates with outlier rejections. A denoised frame is consisted of all the denoised blocks. We compare our method with several state-of-the-art approaches, such as VBM3D and VB-M4D, in terms of PSNR and SSIM. The experiments show that our method can achieve high quality results while runs much faster then the other approaches.
Articulation-to-Speech Synthesis Using Articulatory Flesh Point Sensors' Orientation Information

Articulation-to-speech (ATS) synthesis generates audio wave-form directly from articulatory information. Current works in ATS used articulatory movement information (spatial coordinates) only. The orientation information of articulatory ﬂesh points has rarely been used, although some devices (e.g., electromagnetic articulography) provide that. Previous work indicated that orientation information contains signiﬁcant information for speech production. In this paper, we explored the performance of applying orientation information of ﬂesh points on articulators (i.e., tongue, lips and jaw) in ATS. Experiments us-ing articulators’ movement information with or without orientation information were conducted using standard deep neural networks (DNNs) and long-short term memory-recurrent neural networks (LSTM-RNNs). Both objective and subjective evaluations indicated that adding orientation information of ﬂesh points on articulators in addition to movement information generated higher quality speech output than using movement information only.