Miracle-dz/UniFAR-FTUs huggingface dataset:
PolypSeg+: A Lightweight Context-Aware Network for Real-Time Polyp Segmentation

Automatic polyp segmentation from colonoscopy videos is a prerequisite for the development of a computer-assisted colon cancer examination and diagnosis system. However, it remains a very challenging task owing to the large variation of polyps, the low contrast between polyps and background, and the blurring boundaries of polyps. More importantly, real-time performance is a necessity of this task, as it is anticipated that the segmented results can be immediately presented to the doctor during the colonoscopy intervention for his/her prompt decision and action. It is difficult to develop a model with powerful representation capability, yielding satisfactory segmentation results and, simultaneously, maintaining real-time performance. In this article, we present a novel lightweight context-aware network, namely, PolypSeg+, attempting to capture distinguishable features of polyps without increasing network complexity and sacrificing time performance. To achieve this, a set of novel lightweight techniques is developed and integrated into the proposed PolypSeg+, including an adaptive scale context (ASC) module equipped with a lightweight attention mechanism to tackle the large-scale variation of polyps, an efficient global context (EGC) module to promote the fusion of low-level and high-level features by excluding background noise and preserving boundary details, and a lightweight feature pyramid fusion (FPF) module to further refine the features extracted from the ASC and EGC. We extensively evaluate the proposed PolypSeg+ on two famous public available datasets for the polyp segmentation task: 1) Kvasir-SEG and 2) CVC-Endoscenestill. The experimental results demonstrate that our PolypSeg+ consistently outperforms other state-of-the-art networks by achieving better segmentation accuracy in much less running time. The code is available at https://github.com/szu-zzb/polypsegplus.
Optogenetic activators of apoptosis, necroptosis, and pyroptosis

Targeted and specific induction of cell death in individual or groups of cells holds the potential for new insights into the response of tissues or organisms to different forms of death. Here we report the development of optogenetically-controlled cell death effectors (optoCDEs), a novel class of optogenetic tools that enables light-mediated induction of three types of programmed cell death (PCD) – apoptosis, pyroptosis and necroptosis – using Arabidopsis thaliana photosensitive protein Cryptochrome2. OptoCDEs enable rapid and highly specific induction of PCD in human, mouse and zebrafish cells and are suitable for a wide range of applications, such as sub-lethal cell death induction or precise elimination of single cells or cell populations in vitro and in vivo. As the proof-of-concept, we utilize optoCDEs to assess the differences in the neighboring cell response to apoptotic or necrotic PCD, revealing a new role for shingosine-1-phosphate signaling in regulating the efferocytosis of apoptotic cell by epithelia.
The sodium-glucose cotransporter isoform 1 (SGLT-1) is important for sperm energetics, motility, and fertility

Abstract Glucose is a key substrate for supporting sperm energy production and function. Previous studies have demonstrated that sperm glucose uptake is facilitated by several isoforms of the glucose transporters (GLUT). Here, we report that sperm also expresses the Na+-dependent sodium glucose cotransporter (SGLT). This was first suggested by our observation that genetic deletion of the testis-specific Na,K-ATPase α4, which impairs the sperm plasma membrane Na+ gradient, reduces glucose uptake and ATP production. Immunoblot analysis revealed the presence of an SGLT in sperm, with specific expression of isoform 1 (SGLT-1), but not of isoform 2 (SGLT-2). Immunocytochemistry identified SGLT-1 in the mid- and principal piece of the sperm flagellum. Inhibition of SGLT-1 with the isotype-selective inhibitor phlorizin significantly reduced glucose uptake, glycolytic activity, and ATP production in noncapacitated and capacitated sperm from wild-type mice. Phlorizin also decreased total sperm motility, as well as other parameters of sperm movement. In contrast, inhibition of SGLT-1 had no significant effect on sperm hyperactivation, protein tyrosine phosphorylation, or acrosomal reaction. Importantly, phlorizin treatment impaired the fertilizing capacity of sperm. Altogether, these results demonstrate that mouse sperm express a functional SGLT transport system that is important for supporting sperm energy production, motility, and fertility. Summary Sentence The Na+-dependent glucose cotransporter type 1 (SGLT-1) is expressed in mouse spermatozoa and plays an important role in sperm physiology. Graphical Abstract
Classification of patients with embolic stroke of undetermined source into cardioembolic and non‐cardioembolic profile subgroups

It is currently thought that embolic stroke of undetermined source (ESUS) has diverse underlying hidden etiologies, of which cardioembolism is one of the most important. The subgroup of patients with this etiology could theoretically benefit from oral anticoagulation, but it remains unclear if these patients can be correctly identified from other ESUS subgroups and which markers should be used. We aimed to determine whether a machine‐learning (ML) model could discriminate between ESUS patients with cardioembolic and those with non‐cardioembolic profiles using baseline demographic and laboratory variables.
Reinforcing Generated Images via Meta-Learning for One-Shot Fine-Grained Visual Recognition

One-shot fine-grained visual recognition often suffers from the problem of having few training examples for new fine-grained classes. To alleviate this problem, off-the-shelf image generation techniques based on Generative Adversarial Networks (GANs) can potentially create additional training images. However, these GAN-generated images are often not helpful for actually improving the accuracy of one-shot fine-grained recognition. In this paper, we propose a meta-learning framework to combine generated images with original images, so that the resulting “hybrid” training images improve one-shot learning. Specifically, the generic image generator is updated by a few training instances of novel classes, and a Meta Image Reinforcing Network (MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as image reinforcement. Our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. Furthermore, our analysis shows that the reinforced images have more diversity compared to the original and GAN-generated images.
S OFTWARE A RCHITECTURE M INING FROM S OURCE C ODE S OFTWARE A RCHITECTURE M INING FROM S OURCE C ODE

Software architecture plays a primary role in system specifications and design, acting as a bridge between requirements and implementation. As software systems evolve over time, conformance to the initial architecture may be broken, while unexpected or undesirable component dependencies may arise due to the way the source code evolves. Additionally, understanding the underlying architecture of large applications is required for effective maintenance and continuous improvement. However, the problem is that the architecture is not somehow reflected in the source code since it is not a programming language construct. As a consequence, methods and tools to extract architecture-related information from source code that can aid software developers in understanding system structure are required. Reverse engineering is the process of analyzing a system in order to identify and represent the relationships between its components. Architecture mining is a subset of reverse engineering in which meaningful high-level abstractions that represent system components are detected. In this context, reverse engineering methods may be used to compute concrete architectures and compare it to the original conceptual architecture. In this thesis, we propose an architecture mining tool with the aim of reconstructing the software architecture only from C++ source code. In order to accomplish this, we focus on class relationships, since they represent key elements at the source level. Specifically, our system parses C++ projects and statically analyzes the source code to extract class-related information. Then, it generates and visualizes a dependency graph that represents all the relationships between classes. On top of this graph, we apply clustering methods to identify high-level architectural entities. We use clustering algorithms like Louvain , Infomap and Layered Label Propagation , but also allow users to choose ad-hoc clustering via namespaces and folders. Finally, we have carried out a few case studies to assess and validate the utility of our architecture mining approach.
Antibodies to Coagulase of Staphylococcus aureus crossreact to Efb and reveal different binding of shared Fibrinogen binding repeats

Staphylococcus aureus pathology is caused by a plethora of virulence factors able to combat multiple host defence mechanisms. Fibrinogen (Fg), a critical component in the host coagulation cascade, plays an important role in the pathogenesis of this bacterium, as it is the target of multiple staphylococcal virulence proteins. Amongst its secreted virulence factors, Coagulase (Coa) and Extracellular fibrinogen-binding protein (Efb) share common Fg binding motives and have been described to form a Fg shield around staphylococcal cells, thereby allowing efficient bacterial spreading, phagocytosis escape and evasion of host immune system responses. Targeting these proteins with monoclonal antibodies thus represents a new therapeutic option against S. aureus. To this end, here we report the selection and characterization of fully human, sequence-defined, monoclonal antibodies selected against the C-terminus of Coagulase. Given the functional homology between Coa and Efb, we also investigated if the generated antibodies bound the two virulence factors. Thirteen unique antibodies were isolated from naïve antibodies gene libraries by antibody phage display. As anticipated, most of the selected antibodies showed cross-recognition of these two proteins and among them, four were able to block the interaction between Coa/Efb and Fg. Furthermore, our monoclonal antibodies could interact with the two main Fg binding repeats present at the C-terminus of Coa and distinguish them, suggesting the presence of two functionally different Fg-binding epitopes. Importance The death toll related to methicillin-resistant S. aureus piled to almost 1 million people in only one year (2019), ascribing S. aureus to the second leading cause of deaths associated with antimicrobial resistance. Therefore, new therapeutic strategies must be investigated. Blocking the adhesion step with the use of monoclonal antibodies is one promising alternative and Fg is a central plasma protein involved in staphylococcal infection. We present here a panel of monoclonal antibodies raised against Coa, cross-reacting to Efb and targeting the shared Fg binding repeats of Coa. In addition, we describe new epitope determinants in the repeated region of Coa, highlighted by differential binding of the newly selected antibodies.
Fast Few-shot Debugging for NLU Test Suites

We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem. Given a few debugging examples of a certain phenomenon, and a held-out test set of the same phenomenon, we aim to maximize accuracy on the phenomenon at a minimal cost of accuracy on the original test set. We examine several methods that are faster than full epoch retraining. We introduce a new fast method, which samples a few in-danger examples from the original training set. Compared to fast methods using parameter distance constraints or Kullback-Leibler divergence, we achieve superior original accuracy for comparable debugging accuracy.
FiN: A Smart Grid and Power Line Communication Dataset

The increasing complexity of low-voltage networks poses a growing challenge for the reliable and fail-safe operation of electricity grids. The reasons for this include an increasingly decentralized energy generation (photovoltaic systems, wind power, etc.) and the emergence of new types of consumers (e-mobility, domestic electricity storage, etc.). At the same time, the low-voltage grid is largely unmonitored and local power failures are sometimes hard to detect. To overcome this, power line communication (PLC) has emerged as a potential solution for reliable monitoring of the low-voltage grid. In addition to establishing a communication infrastructure, PLC also offers the possibility of evaluating the cables themselves, as well as the connection quality between individual cable distributors based on their Signal-to-Noise Ratio (SNR). The roll-out of a large-scale PLC infrastructure therefore not only ensures communication, but also introduces a tool for monitoring the entire network. To evaluate the potential of this data, we installed 38 PLC modems in three different areas of a German city with a population of about 150,000 as part of the F\"uhler-im-Netz project. Over a period of 22 months, an SNR spectrum of each connection between adjacent PLC modems was generated every quarter of an hour. % and the voltage was measured every minute. The availability of this real-world PLC data opens up new possibilities to react to the increasingly complex challenges in future smart grids. This paper provides a detailed analysis of the data generation and describes how the data was collected during normal operation of the electricity grid. In addition, we present common anomalies, effects, and trends that could be observed in the PLC data at daily, weekly, or seasonal levels. Finally, we discuss potential use cases and the remote inspection of a cable section is highlighted as an example.
A Universality-Individuality Integration Model for Dialog Act Classification

Dialog Act (DA) reveals the general intent of the speaker utterance in a conversation. Accurately predicting DAs can greatly facilitate the development of dialog agents. Although researchers have done extensive research on dialog act classification, the feature information of classification has not been fully considered. This paper suggests that word cues, part-of-speech cues and statistical cues can complement each other to improve the basis for recognition. In addition, the different types of the three lead to the diversity of their distribution forms, which hinders the mining of feature information. To solve this problem, we propose a novel model based on universality and individuality strategies, called Universality-Individuality Integration Model (UIIM). UIIM not only deepens the connection between the clues by learning universality, but also utilizes the learning of individuality to capture the characteristics of the clues themselves. Experiments were made over two most popular benchmark data sets SwDA and MRDA for dialogue act classification, and the results show that extracting the universalities and individualities between cues can more fully excavate the hidden information in the utterance, and improve the accuracy of automatic dialogue act recognition.
Evolving modular soft robots without explicit inter-module communication using local self-attention

Modularity in robotics holds great potential. In principle, modular robots can be disassembled and reassembled in different robots, and possibly perform new tasks. Nevertheless, actually exploiting modularity is yet an unsolved problem: controllers usually rely on inter-module communication, a practical requirement that makes modules not perfectly interchangeable and thus limits their flexibility. Here, we focus on Voxel-based Soft Robots (VSRs), aggregations of mechanically identical elastic blocks. We use the same neural controller inside each voxel, but without any inter-voxel communication, hence enabling ideal conditions for modularity: modules are all equal and interchangeable. We optimize the parameters of the neural controller---shared among the voxels---by evolutionary computation. Crucially, we use a local self-attention mechanism inside the controller to overcome the absence of inter-module communication channels, thus enabling our robots to truly be driven by the collective intelligence of their modules. We show experimentally that the evolved robots are effective in the task of locomotion: thanks to self-attention, instances of the same controller embodied in the same robot can focus on different inputs. We also find that the evolved controllers generalize to unseen morphologies, after a short fine-tuning, suggesting that an inductive bias related to the task arises from true modularity.1
Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions

A major part of computational materials science and computational chemistry concerns calculations of total energy differences and electronic excitations of poly-atomic systems. Currently, the most prevalent method for such computations is density-functional theory (Hohenberg & Kohn, 1964) (DFT) based on the Kohn-Sham formalism (Kohn & Sham, 1965) (KS) together with one of the numerous exchange-correlation (xc) approximations (Civalleri et al., 2012; Sousa et al., 2007). The trade-off between comparably low computational cost and often reliably accurate results render it the dominant method in the field. Often, however, the accuracy of the xc approximations is not sufficent and uncertain, in particular when electronic correlations play a decisive role (Savin & Johnson, 2014; Zhang et al., 2016; Zhang & Grüneis, 2019). Coupled-cluster (CC) methods (Čı́žek, 1966), while substantially more computationally expensive have proven to be significantly more accurate and reliable, at least for molecules and non-metallic solids. On these grounds, they allow for a systematic accuracy benchmark of other methods. Indeed, in molecular quantum chemistry the CC approach is considered the gold standard for a theoretical description of binding energies and electronic properties (Chan, 2019). It is typically used (at least) for benchmark studies. While the original CC methodology allows one to calculate the ground-state total energy of a system, it is also possible to compute properties of excited states using the equation-of-motion formalism of CC (EOM-CC) with comparable accuracy and reliability (Stanton & Bartlett, 1993). The conventional CC approach is limited, however, to systems with about 50 electrons (Feller & Dixon, 2001; Gyevi-Nagy et al., 2019). By employing approximations, which exploit the locality of electronic correlation, materials with a couple of hundreds of electrons have been calculated via local natural orbital CC (LNO-CC) (Nagy & Kállay, 2019) and explicitly correlated pair natural orbital CC (PNO-CC-F12) (Ma & Werner, 2021). The extension of CC to periodic systems (Hirata et al., 2004) has been explored to a great degree by the research groups of Andreas Grüneis and Garnet Chan.
The Dynamics of Scalar-Field Quintom Cosmological Models

We shall present a complete (compactified) dynamical systems analysis of the Quintom model comprised of an interacting quintessence scalar field and a phantom. We find a range for the model parameters $\kappa, \lambda$ such that there are expanding Quintom cosmologies that undergo two inflationary periods, and this behaviour is not destabilized by spatial curvature. We also discuss a class of bouncing cosmologies. Finally, the linear cosmological perturbations are studied.
S-type Stars Discovered in Medium-resolution Spectra of LAMOST DR9

In this paper, we report on 606 S-type stars identified from Data Release 9 of the LAMOST medium-resolution spectroscopic (MRS) survey; 539 of them are reported for the first time. The discovery of these stars is a three-step process, i.e., selecting with ZrO-band indices greater than 0.25, excluding non-S-type stars with the iterative Support Vector Machine method, and finally retaining stars with absolute bolometric magnitude larger than −7.1. The 606 stars are consistent with the distribution of known S-type stars in the color–magnitude diagram. We estimated the C/Os using the [C/Fe] and [O/Fe] provided by APOGEE and the MARCS model for S-type stars, respectively, and the results of the two methods show that the C/Os of all stars are larger than 0.5. Both the locations on the color–magnitude diagram and C/Os further verify the nature of our S-type sample. Investigating the effect of TiO and atmospheric parameters on ZrO with the sample, we found that log g has a more significant impact on ZrO than T eff and [Fe/H], and both TiO and log g may negatively correlate with ZrO. According to the criterion of Tian et al., a total of 238 binary candidates were found by the zero-point-calibrated radial velocities from the officially released catalog of LAMOST MRS and the catalog of Zhang et al. A catalog of these 606 S-type stars is available from the following link: doi.org/10.12149/101097.
On the holographic domain-wall/cosmology correspondence and scalar one-point functions

: We review the construction of holography for cosmologies with an emphasis on asymptotically de Sitter cosmologies. We do this by composition of the AdS/CFT correspondence and domain-wall/cosmology ( DW/C ) correspondence with the novel added beneﬁt of keeping track of the DW/C marker η . This allows us to build explicitly the holographic dual of the DW/C correspondence. We then test this framework on a toy model: the GPPZ ﬂow. We ﬁnd the scalar one-point function for this ﬂow to be invariant under the domain-wall/cosmology correspondence. Moreover, our construction seems to agree with previous literature on holography for cosmologies.
Decentralized Collaborative Learning Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models’ dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.