Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPACT MICROSTRIP UWB BANDPASS FILTER WITH TRIPLE-NOTCHED BANDS

A new microstrip ultra-wideband (UWB) bandpass fllter (BPF) with triple-notched bands is presented in this paper. The circuit topology and its corresponding electrical parameters of the initial microstrip UWB BPF are desired by a variation of genetic algorithm (VGA). Then, triple-notched bands inside the UWB passband are implemented by coupling a square ring short stub loaded resonator (SRSSLR) to the main transmission line of the initial microstrip UWB BPF. The triple-notched bands can be easily generated and set at any desired frequencies by varying the designed parameters of SRSSLR. For veriflcation, a microstrip UWB BPF with triple-notched bands respectively centered at frequencies of 4.3GHz, 5.8GHz, and 8.1GHz is designed and fabricated. Simulated and experimental results are in good agreement.
PSG: Prompt-based Sequence Generation for Acronym Extraction

Acronym extraction aims to find acronyms (i.e., short-forms) and their meanings (i.e., long-forms) from the documents, which is important for scientific document understanding (SDU@AAAI-22) tasks. Previous works are devoted to modeling this task as a paragraph-level sequence labeling problem. However, it lacks the effective use of the external knowledge, especially when the datasets are in a low-resource setting. Recently, the prompt-based method with the vast pre-trained language model can significantly enhance the performance of the low-resourced downstream tasks. In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task. Specifically, we design a template for prompting the extracted acronym texts with auto-regression. A position extraction algorithm is designed for extracting the position of the generated answers. The results on the acronym extraction of Vietnamese and Persian in a low-resource setting show that the proposed method outperforms all other competitive state-of-the-art (SOTA) methods.
Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU

With the fast development of deep neural networks (DNNs), many real-world applications are adopting multiple models to conduct compound tasks, such as co-running classification, detection, and segmentation models on autonomous vehicles. Such multi-tenant DNN inference cases greatly exacerbate the computational complexity and call for comprehensive collaboration for graph-level operator scheduling, runtime-level resource awareness, as well as hardware scheduler support. However, the current scheduling support for such multi-tenant inference is still relatively backward. In this work, we propose a resource-aware scheduling framework for efficient multi-tenant DNN inference on GPU, which automatically coordinates DNN computing in different execution levels. Leveraging the unified scheduling intermediate representation and the automated ML-based searching algorithm, optimal schedules could be generated to wisely adjust model concurrency and interleave DNN model operators, maintaining a continuously balanced resource utilization across the entire inference process, and eventually improving the runtime efficiency. Experiments show that we could consistently achieve $1.3\times\sim 1.7\times$ speed-up, comparing to regular DNN runtime libraries (e.g., CuDNN, TVM) and particular concurrent scheduling methods (e.g., NVIDIA Multi-Stream).
Stability of Quantized Conductance Levels in Memristors with Copper Filaments: Toward Understanding the Mechanisms of Resistive Switching

Memristors are among the most promising elements for modern microelectronics, having unique properties such as quasi-continuous change of conductance and long-term storage of resistive states. However, identifying the physical mechanisms of resistive switching and evolution of conductive ﬁlaments in such structures still remains a major challenge. In this work, aiming at a better understanding of these phenomena, we experimentally investigate an unusual eﬀect of enhanced conductive ﬁlament stability in memristors with copper ﬁlaments under the applied voltage and present a simpliﬁed theoretical model of the eﬀect of a quantum current through a ﬁlament on its shape. Our semi-quantitative, continuous model predicts, indeed, that for a thin ﬁlament, the “quantum pressure” exerted on its walls by the recoil of charge carriers can well compete with the surface tension and crucially aﬀect the evolution of the ﬁlament proﬁle at the voltages around 1 V. At lower voltages, the quantum pressure is expected to provide extra stability to the ﬁlaments supporting quantized conductance, which we also reveal experimentally using a novel methodology focusing on retention statistics. Our results indicate that the recoil eﬀects could potentially be important for resistive switching in memristive devices with metallic ﬁlaments and that taking them into account in rational design of memristors could help achieve their better retention and plasticity characteristics.
Local Edge Dynamics and Opinion Polarization

The proliferation of social media platforms, recommender systems, and their joint societal impacts have prompted significant interest in opinion formation and evolution within social networks. We study how local edge dynamics can drive opinion polarization. In particular, we introduce a variant of the classic Friedkin-Johnsen opinion dynamics, augmented with a simple time-evolving network model. Edges are iteratively added or deleted according to simple rules, modeling decisions based on individual preferences and network recommendations. Via simulations on synthetic and real-world graphs, we find that the combined presence of two dynamics gives rise to high polarization: 1) confirmation bias -- i.e., the preference for nodes to connect to other nodes with similar expressed opinions and 2) friend-of-friend link recommendations, which encourage new connections between closely connected nodes. We show that our model is tractable to theoretical analysis, which helps explain how these local dynamics erode connectivity across opinion groups, affecting polarization and a related measure of disagreement across edges. Finally, we validate our model against real-world data, showing that our edge dynamics drive the structure of arbitrary graphs, including random graphs, to more closely resemble real social networks. Our code and supplemental materials are available at https://github.com/adamlechowicz/opinion-polarization/.
Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Autonomous driving technologies are expected to not only improve mobility and road safety but also bring energy e ciency benefits. In the foreseeable future, autonomous vehicles (AVs) will operate on roads shared with human-driven vehicles. To maintain safety and liveness while simultaneously minimizing energy consumption, the AV planning and decision-making process should account for interactions between the autonomous ego vehicle and surrounding human-driven vehicles. In this chapter, we describe a framework for developing energy-e cient autonomous driving policies on shared roads by exploiting human-driver behavior modeling based on cognitive hierarchy theory and reinforcement learning.
Transdiagnostic profiles of behaviour and communication relate to academic and socioemotional functioning and neural white matter organisation

Behavioural and language difficulties co-occur in multiple neurodevelopmental conditions. Our understanding of these problems has arguably been slowed by an overreliance on case-control designs, which limit the conclusions we can draw because they fail to capture the overlap across different neurodevelopmental disorders and the heterogeneity within them. In this study, we recruited a large transdiagnostic cohort of children with complex diagnosed and undiagnosed needs (N = 805) to identify distinct subgroups of children with common profiles of behavioural and language strengths and difficulties. We then investigated whether and how these data-driven groupings could be distinguished from a comparison sample (N = 158) on academic, socio-emotional, and neural white matter characteristics. We identified three distinct subgroups of children, each with different levels of difficulties in structural language, pragmatic communication, and hot and cool executive functions. All three subgroups struggled with academic and socio-emotional skills relative to the comparison sample, potentially representing three alternative but related developmental pathways to difficulties in these areas. The children with the weakest language skills had the most widespread difficulties with learning, whereas those with more pronounced difficulties with hot executive skills experienced the most severe difficulties within the socio-emotional domain. Each data-driven subgroup could be distinguished from the comparison sample based on both shared and subgroup-unique patterns of neural white matter organisation. These findings advance our understanding of commonly co-morbid behavioural and language problems and their relationship to behavioural outcomes and neurobiological substrates.
Some Clustering-based Change-point Detection Methods Applicable to High Dimension, Low Sample Size Data

Detection of change-points in a sequence of high-dimensional observations is a very challenging problem, and this becomes even more challenging when the sample size (i.e., the sequence length) is small. In this article, we propose some change-point detection methods based on clustering, which can be conveniently used in such high dimension, low sample size situations. First, we consider the single change-point problem. Using k-means clustering based on some suitable dissimilarity measures, we propose some methods for testing the existence of a change-point and estimating its location. High-dimensional behavior of these proposed methods are investigated under appropriate regularity conditions. Next, we extend our methods for detection of multiple change-points. We carry out extensive numerical studies to compare the performance of our proposed methods with some state-of-the-art methods.
Improving Customer Service Chatbots with Attention-based Transfer Learning

With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores two possibilities. Firstly, whether transfer learning can aid in the improvement of customer service chatbots between business domains. Second, the implementation of a framework for physical robots for in-person interaction. Modelled on social interaction with Twitter customer support accounts, transformer-based chatbot models are initially assigned to learn one domain from an initial random weight distribution. Given shared vocabulary, each model is then tasked with learning another domain by transferring knowledge from the previous. Following studies on 19 different businesses, results show that the majority of models are improved when transferring weights from at least one other domain, in particular those that are more data-scarce than others. General language transfer learning occurs, as well as higher-level transfer of similar domain knowledge, in several cases. The chatbots are finally implemented on Temi and Pepper robots, with feasibility issues encountered and solutions are proposed to overcome them.
Learning a Weight Map for Weakly-Supervised Localization

In the weakly supervised localization setting, supervision is given as an image-level label. We propose employing an image classifier f and training a generative network g that outputs, given the input image, a per-pixel weight map that indicates the location of the object within the image. Network g is trained by minimizing the discrepancy between the output of the classifier f on the original image and its output given the same image weighted by the output of g. Our results indicate that the method outperforms existing localization methods on the challenging fine-grained classification datasets.
Robust On-Policy Data Collection for Data-Efficient Policy Evaluation

This paper considers how to complement ofﬂine reinforcement learning (RL) data with additional data collection for the task of policy evaluation. In policy evaluation, the task is to estimate the expected return of an evaluation policy on an environment of interest. Prior work on ofﬂine policy evaluation typically only considers a static dataset. We consider a setting where we can collect a small amount of additional data to combine with a potentially larger ofﬂine RL dataset. We show that simply running the evaluation policy – on-policy data collection – is sub-optimal for this setting. We then introduce two new data collection strategies for policy evaluation, both of which consider previously collected data when collecting future data so as to reduce distribution shift (or sampling error) in the entire dataset collected. Our empirical results show that compared to on-policy sampling, our strategies produce data with lower sampling error and generally lead to lower mean-squared error in policy evaluation for any total dataset size. We also show that these strategies can start from initial off-policy data, collect additional data, and then use both the initial and new data to produce low mean-squared error policy evaluation without using off-policy corrections.
Materialising Data Feminism – How Textile Designers Are Using Materials to Explore Data Experience

Abstract In our increasingly technology driven world, many human experiences are at risk of falling into the hands of Big Tech who trade, manipulate and design with our data. Catherine Ignacio and Lauren F Klein (2020) propose data feminism as a set of considerations to challenge power and injustices using data science practices. The work of textile designers can also support critical thinking by acknowledging alternative forms of knowledge, experience and activism. Textile designers collect, organise, analyse and present data in formats and contexts which engage and elevate people and stories from different communities. This paper explores the ways that textile designers experimenting with data experience embody the proposed suggestions to tackle issues and challenge systems of oppression. Textile design artefacts can inspire and provoke considerations for data ethics by enabling engagement with information which supports critical thinking. This paper demonstrates the principles of data feminism using examples of contemporary textile practitioners to illustrate how data representation is being designed to evoke emotional response, to communicate meaning and to consider alternative forms of knowledge production. Engaging material artefacts to explore ethical implications of data products is proposed as an application for textile design viewed through the lens of data feminism. This paper argues from the perspective of textile designers, for textile and material thinking be considered as valid methodologies to support dialogue used to challenge injustices and oppression perpetuated through data experience. This paper contributes to textile design research and practice which is expanding the traditional space for textile and material thinking to engage science and technology. Using textile practice to enable a connection and through intersectional feminist framing, a position is established for textile designers to challenge issues found in data science. This paper contributes to the interpretations of soft systems by introducing ways that designers are using materials to reconceptualise and question the status quo of engagement with the digital world. Engaging textile design practices can support critical thinking on data ethics; this paper contributes to research which strengthens the case and scope for textile design and explores relevant and timely issues facing society today.
Emotion regulation deficits and depression-related maladaptive interpersonal behaviours

ABSTRACT Coyne’s interpersonal theory of depression posits that those with depressive symptoms engage in maladaptive interpersonal behaviours that, although intended to assuage distress, push away social supports and increase depressive symptoms (Coyne, 1976). Excessive reassurance seeking, negative feedback seeking, and conversational self-focus are three behaviours implicated in Coyne’s theory, yet their correlates- apart from depressive symptoms- are poorly understood. The current study considered the potential role of intrapersonal emotion regulation deficits as an additional vulnerability factor for these behaviours. Mediation models further tested whether linkages between emotion regulation deficits and maladaptive interpersonal behaviours helped to explain short-term increases in depressive symptoms, as further suggested by theory. Older adolescents (N = 291, M age = 18.9) completed self-report measures of emotion regulation deficits, depressive symptoms, and the three maladaptive interpersonal behaviours during an initial lab visit and again four weeks later. A series of multiple regression models suggested that emotion regulation difficulties are uniquely associated with each of the behaviours over and above the impact of depressive symptoms. Mediation analyses suggested that only excessive reassurance seeking mediated the association between initial emotion regulation deficits and increased depressive symptoms over time. The clinical implications of these findings are discussed.
Field-testing of food-based dietary guidelines

Food-based dietary guidelines (FBDGs) translate nutrition recommendations based on the best available scientific evidence into messages that are short, easy to understand, realistic and easy to apply in everyday settings. Development of the guidelines usually follows a structured and transparent process and applies methodology that balances rigour and pragmatism. The first phase in the development of FBDGs involves identification of (i) diet-health relationships, (ii) country specific diet-related health problems, (iii) nutrients of public health importance, (iv) foods relevant for FBDGs and (v) food consumption patterns. The developed FBDGs should then be tested and optimised, whereafter graphical illustrations of the guidelines should be developed. FBDGs provide a basis for public health programs and policies and are therefore country-specific, locally developed, and should be regularly revised. In instances where guidelines developed in one country are adapted to be used in other settings, cultural and social diversities as well as differences in dietary habits should be considered.
Deep Learning Models to Detect Online False Information: A Systematic Literature Review

The amount of disseminated information from online content volume is increasing rapidly including trusted and untrusted information that are published by different sources. To counter this problem, we need a comprehensive knowledge of existing methods and techniques emerging in the area of False News Detection (FND). This research survey provides a comprehensive review of most effective Deep Learning (DL) models that are used to detect false news and information. We are focusing in DL models and techniques, which use the textual published content and perform FND based on content features. We have considered the research papers in the last five years starting from 2017 onward. In this research paper, the published articles about proposing and developing FND based DL models are included whether the dataset are collected from social platforms or extracted from other news sources. In addition, this research study helps the researchers to have a complete view of the developed DL models that have been proposed in the field of false information detection, the DL models gaps in FND and how they can be improved.
Surface excitations relaxation in the Kondo insulator 
Sm1−xGdxB6

J. C. Souza, M. König, M. V. Ale Crivillero, M. O. Malcolms, R. R. Urbano, Z. Fisk, P. F. S. Rosa, P. G. Pagliuso, S. Wirth, and J. Sichelschmidt Instituto de F́ısica “Gleb Wataghin”, UNICAMP, 13083-859, Campinas, SP, Brazil Max Planck Institute for Chemical Physics of Solids, D-01187 Dresden, Germany Department of Physics and Astronomy, University of California, Irvine, California 92697,USA Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA (Dated: June 10, 2021)
Ecological Interface Design for Knee and Hip Automatic Physiotherapy Assistant and Rehabilitation System

In this paper, we report on a recent interface design and evaluation process for a new knee and hip automatic physiotherapy assistant and rehabilitation system (ARS). Interface design was concurrent with the development of ARS. The ARS has the potential to improve the automation of rehabilitation treatments, by providing quantitative measures of a patient’s motion. However, the complexity of rehabilitation information available to the therapist has increased with this additional information. We applied Ecological Interface Design (EID) to understand the domain of physiotherapy and the role of the automation. Results of a Work Domain Analysis (WDA) revealed new functions and constraints in rehabilitation now accessible through the ARS, and provided the design requirements for interface design. A novel interface was designed which is currently undergoing evaluation to see if it improves the quality and experience of physiotherapy. This study provides an example of the advantages of using EID at the early phase of design, and how to apply EID to a system of increasing automaticity.
A miniaturized bandpass filter fabricated on high dielectric constant ceramic substrates

A four‐pole open‐loop resonator bandpass filter fabricated on high dielectric constant (Zn0.9Mg0.1)TiO3 ceramic substrates is presented. The full‐wave simulator IE3D is utilized to calculate the coupling coefficients of each basic coupling structure and simulate S‐parameters of the designed bandpass filters. The center frequency of designed bandpass filter is fo = 2.45 GHz and the fractional bandwidth is 4%. The center frequency of the fabricated bandpass filter is at 2.47 GHz and the fractional bandwidth is 5%. The measured insertion loss and return loss of the filter is −3.68 and −14.92 dB, respectively. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2087–2090, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22698
Proteomic Characterization of Serum Small Extracellular Vesicles in Human Breast Cancer

There is a lack of comprehensive understanding of breast cancer (BC) specific sEVs characteristics and composition on BC unique proteomic information from human samples. Here, we interrogated the proteomic landscape of sEVs in 167 serum samples from patients with BC, benign mammary disease (BD) and from healthy donors (HD). The analysis provides a comprehensive landscape of serum sEVs with totally 9,589 proteins identified, considerably expanding the panel of sEVs markers. Of note, serum BC-sEVs protein signatures were distinct from those of BD and HD, representing stage- and molecular subtype-specific patterns. We constructed specific sEVs protein identifiers that could serve as a liquid biopsy tool for diagnosis and classification of BC from benign mammary disease, molecular subtypes, as well as assessment of lymph node metastasis. We also identified 11 potential survival biomarkers for distant metastasis. This work may provide reference value for the accurate diagnosis and monitoring of BC progression using serum sEVs.