Miracle-dz/UniFAR-FTUs huggingface dataset:
Raf kinase inhibitory protein reduces bradykinin receptor desensitization

Inflammatory hyperalgesia represents a nociceptive phenotype that can become persistent in nature through dynamic protein modifications. However, a large gap in knowledge exists concerning how the integration of intracellular signaling molecules coordinates a persistent inflammatory phenotype. Herein, we demonstrate that Raf Kinase Anchoring Protein (RKIP) interrupts a vital canonical desensitization pathway to maintain bradykinin (BK) receptor activation in primary afferent neurons. Biochemical analyses of primary neuronal cultures indicate bradykinin‐stimulated PKC phosphorylation of RKIP at Ser153. Furthermore, BK exposure increases G‐protein Receptor Kinase 2 (GRK2) binding to RKIP, inhibiting pharmacological desensitization of the BK receptor. Additional studies found that molecular RKIP down‐regulation increases BK receptor desensitization in real‐time imaging of primary afferent neurons, identifying a key pathway integrator in the desensitization process that controls multiple GRK2‐sensitive G‐protein coupled receptors. Therefore, RKIP serves as an integral scaffolding protein that inhibits BK receptor desensitization.
Anlotinib plus platinum‐etoposide as a first‐line treatment for extensive‐stage small cell lung cancer: A single‐arm trial

Anlotinib as a third‐line or beyond therapy for extensive‐stage small‐cell lung cancer (ES‐SCLC) was studied. This single‐arm phase II trial was to investigate the value of anlotinib plus platinum‐etoposide as first‐line treatment in ES SCLC.
OxidaseMacrophages Lacking a Functional NADPH Granulomatous Disease Monocytes and in Chronic Granulibacter bethesdensis Persistence of the Bacterial Pathogen

Granulibacter bethesdensis is a Gram-negative pathogen in patients with chronic granulomatous disease (CGD), a deﬁciency in the phagocyte NADPH oxidase. Repeated isolation of genetically identical strains from the same patient over years, and prolonged waxing and waning seropositivity in some subjects, raises the possibility of long-term persistence. G. bethesdensis resists killing by serum, CGD polymorphonuclear leukocytes (PMN), and antimicrobial peptides, indicating resistance to nonoxidative killing mechanisms. Although G. bethesdensis extends the survival of PMN, persistent intracellular bacterial survival might rely on longer-lived macrophages and their precursor monocytes. Therefore, we examined phagocytic killing by primary human monocytes and monocyte-derived macrophages (MDM). Cells from both normal and CGD subjects internalized G. bethesdensis similarly. G. bethesdensis stimulated superoxide production in normal monocytes, but to a lesser degree than in normal PMN. Normal but not CGD monocytes and MDM killed G. bethesdensis and required in vitro treatment with IFN- g to maintain this killing effect. Although in vitro IFN- g did not enhance G. bethesdensis killing in CGD monocytes, it restricted growth in proportion to CGD PMN residual superoxide production, providing a potential method to identify patients responsive to IFN-g therapy. In IFN- g –treated CGD MDM, G. bethesdensis persisted for the duration of the study (7 d) without decreasing viability of the host cells. These results indicate that G. bethesdensis is highly resistant to oxygen-independent microbicides of myeloid cells, requires an intact NADPH oxidase for clearance, and can persist long-term in CGD mononuclear phagocytes, most likely
Impacts of demand and supply factors on the capacity scheduling performance of logistics service supply chain with mass customisation service modes: an empirical study from China

Abstract This paper studies the influence of the demand–supply environment on the capacity scheduling performance of the logistics service supply chain. According to the key characteristics of the demand–supply environment, the cases of five Chinese companies were introduced and cross-analysed, then research hypotheses were developed. After receiving 154 valid questionnaires and testing our hypotheses, some key conclusions were obtained. From the aspect of demand, there is a positive correlation between the customised levels of demand and the scheduling cost of logistics service integrators but a negative correlation between the customised levels of demand and the scheduling flexibility; From the aspect of supply, a higher self-support ratio of logistics source and a higher sourcing integrity of logistics service integrators will lead to a higher scheduling cost and a lower scheduling flexibility; a whole-process performance evaluation is a moderator, which will positively improve the impact of the whole process scheduling performance.
Effects of outliers on productivity analyses based on the Farm Accountancy Data Network (EU-FADN - DG AGRI)

Outline of the pMST procedure: 1. calculate Euclidean distances between each pair of observations 2. calculate the minimum spanning tree (MST) 3. find a theshold r and drop all edges longer than r from the MST⇒ pruned MST 4. determine the largest connected subset of the pruned MST ⇒ "good" subset 5. all observations not belonging to the "good" subset are handled as outliers Note: Threshold r is determined by a two-step approach using Chebychev’s inequality, see Kirschstein et al. (2013)
Threats to Science: Politicization, Misinformation, and Inequalities

Science is often considered the best available route to knowledge and, thus, essential for societal progress. Yet contemporary science faces several challenges. These challenges include politicization, misinformation, and inequalities. I outline each of these threats, detailing the ways in which they can undermine the optimal production and application of science. I provide an overview of various research agendas on each, as covered in this volume. Without minimizing the seriousness posed by each threat, I also suggest that existing work provides reason for hope that the scientific enterprise can address these challenges and continue to improve societal well-being.
Bounded-Degree Plane Geometric Spanners in Practice

The construction of bounded-degree plane geometric spanners has been a focus of interest since 2002 when Bose, Gudmundsson, and Smid proposed the first algorithm to construct such spanners. To date, 11 algorithms have been designed with various tradeoffs in degree and stretch-factor. We have implemented these sophisticated spanner algorithms in C++ using the CGAL library and experimented with them using large synthetic and real-world pointsets. Our experiments have revealed their practical behavior and real-world efficacy. We share the implementations via GitHub for broader uses and future research. We design and engineer EstimateStretchFactor, a simple practical algorithm, which can estimate stretch-factors (obtains lower bounds on the exact stretch-factors) of geometric spanners—a challenging problem for which no practical algorithm is known yet. In our experiments with bounded-degree plane geometric spanners, we found that EstimateStretchFactor estimated stretch-factors almost precisely. Further, it gave linear runtime performance in practice for the pointset distributions considered in this work, making it much faster than the naive Dijkstra-based algorithm for calculating stretch-factors.
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. Many of the currently available approaches to predict PHQ scores use passive data, e.g., from smartphones. However, there are several other scores and data besides PHQ, e.g., the Behavioral Activation for Depression Scale–Short Form (BADSSF), the Center for Epidemiologic Studies Depression Scale (CESD), or the Personality Dynamics Diary (PDD), all of which can be effortlessly collected on a daily basis. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets

Supervised person re-identification methods rely heavily on high-quality cross-camera training label. This significantly hinders the deployment of re-ID models in real-world applications. The unsupervised person re-ID methods can reduce the cost of data annotation, but their performance is still far lower than the supervised ones. In this paper, we make full use of the auxiliary information mined from the datasets for multi-modal feature learning, including camera information, temporal information and spatial information. By analyzing the style bias of cameras, the characteristics of pedestrians' motion trajectories and the positions of camera network, this paper designs three modules: Time-Overlapping Constraint (TOC), Spatio-Temporal Similarity (STS) and Same-Camera Penalty (SCP) to exploit the auxiliary information. Auxiliary information can improve the model performance and inference accuracy by constructing association constraints or fusing with visual features. In addition, this paper proposes three effective training tricks, including Restricted Label Smoothing Cross Entropy Loss (RLSCE), Weight Adaptive Triplet Loss (WATL) and Dynamic Training Iterations (DTI). The tricks achieve mAP of 72.4% and 81.1% on MARS and DukeMTMC-VideoReID, respectively. Combined with auxiliary information exploiting modules, our methods achieve mAP of 89.9% on DukeMTMC, where TOC, STS and SCP all contributed considerable performance improvements. The method proposed by this paper outperforms most existing unsupervised re-ID methods and narrows the gap between unsupervised and supervised re-ID methods. Our code is at https://github.com/tenghehan/AuxUSLReID.
Convex Analysis at Infinity: An Introduction to Astral Space

Not all convex functions on $\mathbb{R}^n$ have finite minimizers; some can only be minimized by a sequence as it heads to infinity. In this work, we aim to develop a theory for understanding such minimizers at infinity. We study astral space, a compact extension of $\mathbb{R}^n$ to which such points at infinity have been added. Astral space is constructed to be as small as possible while still ensuring that all linear functions can be continuously extended to the new space. Although astral space includes all of $\mathbb{R}^n$, it is not a vector space, nor even a metric space. However, it is sufficiently well-structured to allow useful and meaningful extensions of concepts of convexity, conjugacy, and subdifferentials. We develop these concepts and analyze various properties of convex functions on astral space, including the detailed structure of their minimizers, exact characterizations of continuity, and convergence of descent algorithms.
Dual-Level Decoupled Transformer for Video Captioning

Video captioning aims to understand the spatio-temporal semantic concept of the video and generate descriptive sentences. The de-facto approach to this task dictates a text generator to learn from offline-extracted motion or appearance features from pre-trained vision models. However, these methods may suffer from the so-called "couple" drawbacks on both video spatio-temporal representation and sentence generation. For the former, "couple" means learning spatio-temporal representation in a single model(3DCNN), resulting the problems named disconnection in task/pre-train domain and hard for end-to-end training. As for the latter, "couple" means treating the generation of visual semantic and syntax-related words equally. To this end, we present D2 - a dual-level decoupled transformer pipeline to solve the above drawbacks: (i) for video spatio-temporal representation, we decouple the process of it into "first-spatial-then-temporal" paradigm, releasing the potential of using dedicated model(e.g. image-text pre-training) to connect the pre-training and downstream tasks, and makes the entire model end-to-end trainable. (ii) for sentence generation, we propose Syntax-Aware Decoder to dynamically measure the contribution of visual semantic and syntax-related words. Extensive experiments on three widely-used benchmarks (MSVD, MSR-VTT and VATEX) have shown great potential of the proposed D2 and surpassed the previous methods by a large margin in the task of video captioning.
Computing subalgebras and $\mathbb{Z}_2$-gradings of simple Lie algebras over finite fields

This paper introduces two new algorithms for Lie algebras over finite fields and applies them to the investigate the known simple Lie algebras of dimension at most $20$ over the field $\mathbb{F}_2$ with two elements. The first algorithm is a new approach towards the construction of $\mathbb{Z}_2$-gradings of a Lie algebra over a finite field of characteristic $2$. Using this, we observe that each of the known simple Lie algebras of dimension at most $20$ over $\mathbb{F}_2$ has a $\mathbb{Z}_2$-grading and we determine the associated simple Lie superalgebras. The second algorithm allows us to compute all subalgebras of a Lie algebra over a finite field. We apply this to compute the subalgebras, the maximal subalgebras and the simple subquotients of the known simple Lie algebras of dimension at most $16$ over $\mathbb{F}_2$ (with the exception of the $15$-dimensional Zassenhaus algebra).
Approximate convex decomposition for 3D meshes with collision-aware concavity and tree search

Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape. It thus enables efficient geometry processing algorithms specifically designed for convex shapes and has been widely used in game engines, physics simulations, and animation. While prior works can capture the global structure of input shapes, they may fail to preserve fine-grained details (e.g., filling a toaster's slots), which are critical for retaining the functionality of objects in interactive environments. In this paper, we propose a novel method that addresses the limitations of existing approaches from three perspectives: (a) We introduce a novel collision-aware concavity metric that examines the distance between a shape and its convex hull from both the boundary and the interior. The proposed concavity preserves collision conditions and is more robust to detect various approximation errors. (b) We decompose shapes by directly cutting meshes with 3D planes. It ensures generated convex hulls are intersection-free and avoids voxelization errors. (c) Instead of using a one-step greedy strategy, we propose employing a multi-step tree search to determine the cutting planes, which leads to a globally better solution and avoids unnecessary cuttings. Through extensive evaluation on a large-scale articulated object dataset, we show that our method generates decompositions closer to the original shape with fewer components. It thus supports delicate and efficient object interaction in downstream applications.
CompactIE: Compact Facts in Open Information Extraction

A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are compact and share constituents. To this end, we study the problem of identifying compact extractions with neural-based methods. We propose CompactIE, an OpenIE system that uses a novel pipelined approach to produce compact extractions with overlapping constituents. It first detects constituents of the extractions and then links them to build extractions. We train our system on compact extractions obtained by processing existing benchmarks. Our experiments on CaRB and Wire57 datasets indicate that CompactIE finds 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.
Statistical Data Privacy: A Song of Privacy and Utility

To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential privacy (DP). Despite framing differences, both SDC and DP share the same statistical problems at their core. For inference problems, either we may design optimal release mechanisms and associated estimators that satisfy bounds on disclosure risk measures, or we may or adjust existing sanitized output to create new statistically valid and optimal estimators. Regardless of design or adjustment, in evaluating risk and utility, valid statistical inferences from mechanism outputs require uncertainty quantification that accounts for the effect of the sanitization mechanism that introduces bias and/or variance. In this review, we discuss the statistical foundations common to both SDC and DP, highlight major developments in SDP, and present exciting open research problems in private inference. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 10 is March 2023. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media

Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.
Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders from different data collection processes. Owing to the superiority of meta-learning, our method does not rely on large-scale datasets like conventional deep learning models. Moreover, we design a subject-specific training process to assist the model in fitting the data pattern of different patients or healthy people. Our method achieves a high accuracy of 73.8% in the cross-dataset classification. We conduct an extensive analysis to show the effectiveness of side information of ears in enhancing model performance and side-aware meta-learning in improving the quality of the learned features.
Universal relations for quasinormal modes of neutron stars in $R^2$ gravity

We construct quasinormal modes for neutron stars in R 2 gravity in the Einstein frame, considering scalar masses in the sub-neV range. In particular, we investigate the fundamental quadrupole ﬂuid f-modes and the dipole ﬂuid F-modes. Employing six equations of state covering matter content with nucleons, hyperons and quarks, we then propose universal relations for the quadrupole f-modes and dipole F-modes. The dipole F-modes are ultra-long lived and, for the lower scalar masses, their frequencies are inversely proportional to the corresponding Compton wavelength.
A Review on Text-Based Emotion Detection - Techniques, Applications, Datasets, and Future Directions

AI has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented. 1 Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India. 2 Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India 3 University of Hertfordshire, Hatfield, Hertfordshire, United Kingdom, 4 University of Agder, Norway.