Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and safety of acupuncture as a complementary therapy for sepsis: a systematic review and meta-analysis

Background: Sepsis is a life-threatening organ dysfunction caused by dysregulation of the host response to infection. Acupuncture is used for treatment of inflammatory diseases; however, its effectiveness and safety as a complementary therapy for sepsis has not been fully explored. Methods: Data were retrieved from eight databases. Randomized controlled trials (RCTs) that compared acupuncture plus conventional therapies versus conventional therapies alone were included. Pre-specified primary outcomes were mortality at 28 days and Acute Physiologic and Chronic Health Evaluation (APACHE) II scores. Results: A total of 17 studies with 1099 participants were included in this study. In terms of the primary outcomes, acupuncture plus routine therapy reduced mortality at 28 days (risk ratio (RR)): 0.69, 95% confidence interval (CI): 0.52 to 0.91, p < 0.001) and APACHE II scores (mean difference (MD): −2.84, 95% CI: −4.09 to −1.58, p < 0.001) at day 7 after treatment compared with routine therapy alone. In terms of secondary outcomes, acupuncture plus routine therapy reduced white blood cell counts and levels of procalcitonin (PCT), tumor necrosis factor (TNF)-α, interleukin (IL)-6 and lactic acid and intra-abdominal pressure (IAP), and improved CD3+, CD4+ and monocytes of human leukocyte antigen (HLA)-DR at day 7 after treatment compared with routine therapy alone. However, acupuncture plus routine therapy had no significant effects on levels of IL-10, C-reactive protein (CRP), CD8+ and CD4+/CD8+ ratios compared with routine therapy alone. Quality of evidence was low to very low for all parameters (GRADE). Conclusion: The available evidence showed that combination of acupuncture and routine therapy may have benefit for sepsis compared with use of routine therapy only. Due to the low degree of certainty regarding its effects, further research is required. Trial registration number: ICRD42019141491 (PROSPERO).
Integrative analysis of scRNA-seq and scATAC-seq revealed transit-amplifying thymic epithelial cells expressing autoimmune regulator

Medullary thymic epithelial cells (mTECs) are critical for self-tolerance induction in T cells via promiscuous expression of tissue-specific antigens (TSAs), which are controlled by the transcriptional regulator, AIRE. Whereas AIRE-expressing (Aire+) mTECs undergo constant turnover in the adult thymus, mechanisms underlying differentiation of postnatal mTECs remain to be discovered. Integrative analysis of single-cell assays for transposase-accessible chromatin (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) suggested the presence of proliferating mTECs with a specific chromatin structure, which express high levels of Aire and co-stimulatory molecules, CD80 (Aire+CD80hi). Proliferating Aire+CD80hi mTECs detected using Fucci technology express a minimal number of Aire-dependent TSAs and are converted into quiescent Aire+CD80hi mTECs expressing high levels of TSAs after a transit amplification. These data provide evidence for the existence of transit-amplifying Aire+mTEC precursors during the Aire+mTEC differentiation process of the postnatal thymus.
Hierarchical Reinforcement Learning, Sequential Behavior, and the Dorsal Frontostriatal System

Abstract To effectively behave within ever-changing environments, biological agents must learn and act at varying hierarchical levels such that a complex task may be broken down into more tractable subtasks. Hierarchical reinforcement learning (HRL) is a computational framework that provides an understanding of this process by combining sequential actions into one temporally extended unit called an option. However, there are still open questions within the HRL framework, including how options are formed and how HRL mechanisms might be realized within the brain. In this review, we propose that the existing human motor sequence literature can aid in understanding both of these questions. We give specific emphasis to visuomotor sequence learning tasks such as the discrete sequence production task and the M × N (M steps × N sets) task to understand how hierarchical learning and behavior manifest across sequential action tasks as well as how the dorsal cortical–subcortical circuitry could support this kind of behavior. This review highlights how motor chunks within a motor sequence can function as HRL options. Furthermore, we aim to merge findings from motor sequence literature with reinforcement learning perspectives to inform experimental design in each respective subfield.
Triple helix organisations, communities of practice and time

Commercialisation activities require knowledge sharing between groups such as researchers and commercial managers. The existence of research based Knowledge-stewarding Communities of Practice (CoPs) within industry/research/government innovation collaborations has important implications for innovation management practice. The context of the study is four Australian Cooperative Research Centres (CRCs) composed of academic, government and industry personnel. Semi-structured interviews with a total of twenty scientists, engineers and managers explored collectively shared and dissimilar perceptions of time in commercialisation activities. Knowledge-stewarding CoP members and commercial participants in triple helix organisations working to commercialise inventions report differing temporal perceptions. Commercial and research groups described distinctively different views of pace and flexibility, contributing to tension, distrust and negatively influencing knowledge sharing, communication and commercialisation outcomes. Management techniques in use in the four CRCs and an agenda for future research conclude the chapter. This chapter contributes to the literature on collaborative innovation management and inter-organisational CoPs.
Ultrahigh-Speed GaN High-Electron-Mobility Transistors With f T / f max of 454/444 GHz

This letter reports record RF performance of deeply scaled depletion-mode GaN-high-electron-mobility transistors (GaN-HEMTs). Based on double heterojunction AlN/GaN/AlGaN epitaxial structure, fully passivated devices were fabricated by self-aligned-gate technology featuring recessed n + -GaN ohmic contact regrown by molecular beam epitaxy. Record-high fT of 454 GHz and simultaneous fmax of 444 GHz were achieved on a 20-nm gate HEMT with 50-nm-wide gate- source and gate-drain separation. With an OFF-state breakdown voltage of 10 V, the Johnson figure of merit of this device reaches 4.5 THz-V, representing the state-of-the-art performance of GaN transistor technology to-date. Compared with previous E-mode GaN-HEMTs of similar device structure, significantly reduced extrinsic gate capacitance and enhanced average electron velocity are the key reasons for improved frequency characteristic.
Factors shaping academics’ use of technology in teaching: a proposed model

Whilst academics have generally adopted the use of new technologies in their teaching, literature indicates that only a minority is exploiting technology to provide pedagogically rich learning experiences. This is a significant issue for universities, many of which are making considerable strategic investment into technology-infused teaching, as one way of responding to the pressures of globalization and meeting the needs technology-savvy student cohorts. Providing appropriate support to assist academics to implement effective, technology-infused teaching strategies is thus critical. It is argued that development of appropriate academic support should be informed by an understanding of why academics use technology as they do in their teaching. Towards this end, a model of factors influencing how academics use technology in their teaching is proposed in the chapter. The model arises from a synthesis of relevant literature and the identification of pertinent conceptual frameworks.
Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification

In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask—reflecting a 3.4% relative improvement compared to previous work.
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy

Experience has shown that trading in stock and cryptocurrency markets has the potential to be highly profitable. In this light, considerable effort has been recently devoted to investigate how to apply machine learning and deep learning to interpret and predict market behavior. This research introduces a new deep neural network architecture and a novel idea of how to prepare financial data before feeding them to the model. In the data preparation part, the first step is to generate many features using technical indicators and then apply the XGBoost model for feature engineering. Splitting data into three categories and using separate autoencoders, we extract high-level mixed features at the second step. This data preprocessing is introduced to predict price movements. Regarding modeling, different convolutional layers, an long short-term memory unit, and several fully-connected layers have been designed to perform binary classification. This research also introduces a trading strategy to exploit the trained model outputs. Three different datasets are used to evaluate this method, where results indicate that this framework can provide us with profitable and robust predictions.
Effects of diversity on beneficial and pest arthropods

Vegetational diversity within agroecosystems can vary in in three basic ways; the species richness of plants, the spatial arrangement of species and the temporal relationship among species. Agroecosystems with greater vegetational diversity are associated with decreased pest abundance and increased natural enemy abundance. The objectives of this dissertation were to test the effect of farming practices that alter vegetational diversity on beneficial arthropod communities and pest insects. Farming practices investigated included the addition rye cover crop (Secale cereale L.) to corn (Zea mays L.) and the use of extended crop rotations. Two separate studies were conducted on experimental plots to compare the effects of the addition of a rye cover crop and extended crop rotations against practices that lacked diversity on epigeal and canopy, beneficial arthropod communities. In both studies we hypothesized that practices that increased vegetational diversity would positively affect beneficial arthropods. Only modest differences were observed in the composition and abundance of beneficial arthropods with the addition of a rye cover crop and none were detected among crop rotations with different lengths. Two on-farm studies were conducted to quantify the effects rye cover crop and the use of extended crop rotations on insect pests of corn. Farming practices, corn root injury and abundance of Diabrotica spp. were compared among cornfields that differed in the frequency of crop rotation and previous pest injury. Root injury and abundance of Diabrotica spp. were similar among cornfields, however, fields that had a history of crop rotation required significantly fewer pest management inputs. In the second study, the abundance of early season insect pests and injury to corn were compared between fields with and without a rye cover crop. Although adult moths were captured around all cornfields, significantly greater abundance of Mythimna unipuncta and greater proportion of defoliated corn were found in fields where corn followed a rye cover crop.
Designing future turnouts – where research capabilities meet industry needs

Railway switches and crossings (SC while at the same time the window for maintenance and intervention is being drastically narrowed. One fundamental aspect emerging from these previous research programmes is that a lot of the observed failures surrounding S&C have their root cause explained by the discontinuities experienced by the vehicle at S&C. The nature of these is related to the varying geometrical interface between the vehicle wheels and the guiding rails (e.g. load transfer between stock and switch rails), as well as the uneven vertical support offered to the vehicle through the S&C (e.g. uneven loading of long bearers, increased bending stiffness of cast crossings). A large number of reliability issues find their source in the physical vibrations and dynamically amplified loads occurring because of these two facts, this includes some of the gradual deterioration experienced by POE. Research institutes, including the Institute of Railway Research at the University of Huddersfield, have helped develop advanced simulation tools capable of confidently predicting these phenomena and assess contributory factors to degradation modes as well as iteratively work on remedial actions. These might either be small step changes to existing designs or they might be radical concept changes, covering short, medium and long term vision for 2050 railways in Europe. The author will summarise some of the outcome of recent EU projects, give examples of were simulation technology might be used to assess future improvements and radical changes, as well as opportunities for validating these and other wheel-rail interface research projects using advanced testing techniques.
Fast Consensus of High-Order Multiagent Systems

In this article, the fast consensus problem of high-order multiagent systems (MASs) under undirected topologies is considered. The direct link between the consensus convergence rate and the control gains is established. A gradient descent-based algorithm is proposed to optimize the convergence rate. By applying the Routh–Hurwitz stability criterion, the lower bound on the convergence rate is derived, and explicit control gains are derived as conditions to achieve the optimal convergence rate. Moreover, a protocol with time-varying control gains is designed to achieve the finite-time consensus. Explicit formulas for the time-varying control gains and the final consensus state are given. Numerical examples and simulation results are presented to illustrate the obtained theoretical results.
Revisiting the Updates of a Pre-trained Model for Few-shot Learning

Most of the recent few-shot learning algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is updated using a small amount of target data afterward. In transfer-based few-shot learning, sophisticated pre-training meth-ods have been widely studied for universal and improved representation. However, there is little study on updating pre-trained models for few-shot learning. In this paper, we compare the two popular updating methods, ﬁne-tuning (i.e., updating the entire network) and linear probing (i.e., up-dating only the linear classiﬁer), considering the distribution shift between the source and target data. We ﬁnd that ﬁne-tuning is better than linear probing as the number of samples increases, regardless of distribution shift. Next, we investigate the effectiveness and ineffectiveness of data augmentation when pre-trained models are ﬁne-tuned. Our fundamental analyses demonstrate that careful considerations of the details about updating pre-trained models are required for better few-shot performance.
The Diversity of Argument-Making in the Wild: from Assumptions and Definitions to Causation and Anecdote in Reddit's "Change My View"

What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site “Change My View” (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about deﬁnitions, relevance, possi-bility and cause, and personal experience. Data from r/CMV also reveal differences in efﬁcacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person’s view, while arguments about relevance are the least. Finally, our methods reveal a gradi- ent of argument-making preferences among users: a two-axis model, of “personal–impersonal” and “concrete–abstract”, can account for nearly 80% of the strategy variance between individuals.
Minimum Tournaments with the Strong Sk-Property and Implications for Teaching

A tournament is said to have the S k -property if, for any set of k players, there is another player who beats them all. Minimum tournaments having this property have been explored very well in the 1960’s and the early 1970’s. In this paper, we deﬁne a strengthening of the S k -property that we name “strong S k -property”. We show, ﬁrst, that several basic results on the weaker notion remain valid for the stronger notion (and the corresponding modiﬁcation of the proofs requires only little extra-eﬀort). Second, it is demonstrated that the stronger notion has applications in the area of Teaching. Speciﬁcally, we present an inﬁnite family of concept classes all of which can be taught with a single example in the No-Clash model of teaching while, in order to teach a class C of this family in the recursive model of teaching, order of log |C| many examples are required. This is the ﬁrst paper that presents a concrete and easily constructible family of concept classes which separates the No-Clash from the recursive model of teaching by more than a constant factor. The separation by a logarithmic factor is remarkable because the recursive teaching dimension is known to be bounded by log |C| for any concept class C .
Exploring the Interactive Guidance for Unified and Effective Image Matting

Recent image matting studies are developing towards proposing trimap-free or interactive methods for complete complex image matting tasks. Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations: (1) For the single image with multiple objects, it is essential to provide extra interaction information to help determining the matting target; (2) For transparent objects, the accurate regression of alpha matte from RGB image is much more difficult compared with the opaque ones. In this work, we propose a Unified Interactive image Matting method, named UIM, which solves the limitations and achieves satisfying matting results for any scenario. Specifically, UIM leverages multiple types of user interaction to avoid the ambiguity of multiple matting targets, and we compare the pros and cons of different annotation types in detail. To unify the matting performance for transparent and opaque objects, we decouple image matting into two stages, i.e., foreground segmentation and transparency prediction. Moreover, we design a multi-scale attentive fusion module to alleviate the vagueness in the boundary region. Experimental results demonstrate that UIM achieves state-of-the-art performance on the Composition-1K test set and a synthetic unified dataset.
The Role of Inner H i Mass in Regulating the Scatter of the Mass–Metallicity Relation

We use 789 disk-like, star-forming galaxies (with 596 H i detections) from H i follow-up observations for the SDSS-IV MaNGA survey to study the possible role of inner H i gas in causing secondary dependences in the mass–gas-phase metallicity relation. We use the gas-phase metallicity derived at the effective radii of the galaxies. We derive the inner H i mass within the optical radius, but also use the total H i mass and star formation rate (SFR) for a comparison. We confirm the anticorrelation between the total H i mass and gas-phase metallicity at fixed stellar mass, but the anticorrelation is significantly strengthened when the total H i mass is replaced by the inner H i mass. Introducing a secondary relation with the inner H i mass can produce a small but noticeable decrease (16%) in the scatter of the mass–gas-phase metallicity relation, in contrast to the negligible effect with the SFR. The correlation with the inner H i mass is robust when using different diagnostics of metallicity, but the correlation with SFR is not. The correlation with the inner H i mass becomes much weaker when the gas-phase metallicity is derived in the central region instead of at the effective radius. These results support the idea that the scatter in the mass–metallicity relation is regulated by gas accretion, and not directly by the SFR, and stress the importance of deriving the gas mass and the metallicity from roughly the same region. The new relation between inner H i mass and gas-phase metallicity will provide new constraints for chemical and galaxy evolution models.
The inverse Galois problem for connected algebraic groups

We show that each connected group scheme of ﬁnite type over an arbitrary ground ﬁeld is isomorphic to the component of the identity inside the automorphism group scheme of some projective, geometrically integral scheme. The main ingredients are embeddings into smooth group schemes, equivariant completions, blow-ups of orbit closures, Fitting ideals for K¨ahler diﬀerentials, and Blanchard’s Lemma.
A CMOS-based Characterisation Platform for Emerging RRAM Technologies

Mass characterisation of emerging memory devices is an essential step in modelling their behaviour for integration within a standard design flow for existing integrated circuit designers. This work develops a novel characterisation platform for emerging resistive devices with a capacity of up to 1 million devices on-chip. Split into four independent sub-arrays, it contains on-chip column-parallel DACs for fast voltage programming of the DUT. On-chip readout circuits with ADCs are also available for fast read operations covering 5-decades of input current (20nA to 2mA). This allows a device’s resistance range to be between 1k$\Omega$ and 10M$\Omega$ with a minimum voltage range of ±1.5V on the device.
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning

Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy. However, non-Shannon entropies suffer from approximation error and subsequent underperformance either due to its sensitivity or the lack of closed-form policy expression. To improve the tradeoff between flexibility and empirical performance, we propose to strengthen their error-robustness by enforcing implicit Kullback-Leibler (KL) regularization in MTE motivated by Munchausen DQN (MDQN). We do so by drawing connection between MDQN and advantage learning, by which MDQN is shown to fail on generalizing to the MTE framework. The proposed method Tsallis Advantage Learning (TAL) is verified on extensive experiments to not only significantly improve upon Tsallis-DQN for various non-closed-form Tsallis entropies, but also exhibits comparable performance to state-of-the-art maximum Shannon entropy algorithms.