Miracle-dz/UniFAR-FTUs huggingface dataset:
LargeEA: Aligning Entities for Large-scale Knowledge Graphs

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world EA scenarios. To tackle this challenge, we propose LargeEA to align entities between large-scale KGs. LargeEA consists of two channels, i.e., structure channel and name channel. For the structure channel, we present METIS-CPS, a memory-saving mini-batch generation strategy, to partition large KGs into smaller mini-batches. LargeEA, designed as a general tool, can adopt any existing EA approach to learn entities' structural features within each mini-batch independently. For the name channel, we first introduce NFF, a name feature fusion method, to capture rich name features of entities without involving any complex training process; we then exploit a name-based data augmentation to generate seed alignment without any human intervention. Such design fits common real-world scenarios much better, as seed alignment is not always available. Finally, LargeEA derives the EA results by fusing the structural features and name features of entities. Since no widely-acknowledged benchmark is available for large-scale EA evaluation, we also develop a large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive experiments confirm the superiority of LargeEA against state-of-the-art competitors.
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing

Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpretations need to be robust for trustworthy NLP applications in high-stake areas like medicine or finance. Our paper demonstrates how interpretations can be manipulated by making simple word perturbations on an input text. Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations). Simultaneously, the generated examples achieve the same prediction label as the seed yet are given a substantially different explanation by the interpretation methods. Our experiments generate fragile interpretations to attack two SOTA interpretation methods, across three popular Transformer models and on three different NLP datasets. We observe that the rank order correlation and top-K intersection score drops by over 20% when less than 10% of words are perturbed on average. Further, rank-order correlation keeps decreasing as more words get perturbed. Furthermore, we demonstrate that candidates generated from our method have good quality metrics.
A Note on the Number of Permutations whose Cycle Lengths Are Prime Numbers

Let A be a set of natural numbers and let Sn,A be the set of all permutations of [n] = {1, 2, . , n} with cycle lengths belonging to A. For A(n) = A ∩ [n], the limit ρ = limn→∞ |A(n)|/n (if it exists) is usually called the density of set A. (Here |B| stands for the cardinality of the set B.) Several studies show that the asymptotic behavior of the cardinality |Sn,A|, as n → ∞, depends on the density ρ. It turns out that the assumption ρ > 0 plays an essential role in the asymptotic analysis of |Sn,A|. Kolchin (1999) noticed that there is a lack of studies on classes of permutations satisfying ρ = 0 and proposed investigations of certain particular cases. In this note, we consider the permutations whose cycle lengths are prime numbers, that is, we assume that A = P , where P denotes the set of all primes. From the Prime Number Theorem it follows that ρ = 0 for this class of permutations. We deduce an asymptotic formula for the summatory function ∑ k≤n |Sk,P |/k! as n → ∞. In our proof we employ the classical Hardy-Littlewood-Karamata Tauberian theorem.
AGN feeding and feedback in Fornax A. Kinematical analysis of the multi-phase ISM

We present a multi-wavelength study of the gaseous medium surrounding the nearby active galactic nucleus (AGN), Fornax A. Using MeerKAT, ALMA, and MUSE observations, we reveal a complex distribution of the atomic (H i ), molecular (CO), and ionised gas in its centre and along the radio jets. By studying the multi-scale kinematics of the multi-phase gas, we reveal the presence of concurrent AGN feeding and feedback phenomena. Several clouds and an extended 3kpc ﬁlament – perpendicular to the radio jets and the inner disk ( r (cid:46) 4 . 5kpc) – show highly-turbulent kinematics, which likely induces non-linear condensation and subsequent chaotic cold accretion (CCA) onto the AGN. In the wake of the radio jets and in an external ( r (cid:38) 4 . 5kpc) ring, we identify an entrained massive ( ∼ 10 7 M (cid:12) ) multi-phase outﬂow ( v OUT ∼ 2000 kms − 1 ). The rapid ﬂickering of the nuclear activity of Fornax A ( ∼ 3Myr) and the gas experiencing turbulent condensation raining onto the AGN provide quantitative evidence that a recurrent, tight feeding and feedback cycle may be self-regulating the activity of Fornax A, in agreement with CCA simulations. To date, this is one of the most in-depth probes of such a mechanism, paving the way to apply these precise diagnostics to a larger sample of nearby AGN hosts and their multi-phase inter stellar medium.
Ross 19B: An Extremely Cold Companion Discovered via the Backyard Worlds: Planet 9 Citizen Science Project

Through the Backyard Worlds: Planet 9 citizen science project, we have identified a wide-separation (∼10′, ∼9900 au projected) substellar companion to the nearby (∼17.5 pc), mid-M dwarf Ross 19. We have developed a new formalism for determining chance alignment probabilities based on the BANYAN Σ tool, and find a 100% probability that this is a physically associated pair. Through a detailed examination of Ross 19A, we find that the system is metal-poor ([Fe/H] = −0.40 ± 0.12) with an age of 7.2−3.6+3.8 Gyr. Combining new and existing photometry and astrometry, we find that Ross 19B is one of the coldest known wide-separation companions, with a spectral type on the T/Y boundary, an effective temperature of 500−100+115 K, and a mass in the range 15–40 M Jup. This new, extremely cold benchmark companion is a compelling target for detailed characterization with future spectroscopic observations using facilities such as the Hubble Space Telescope or James Webb Space Telescope.
Controlling the False Split Rate in Tree-Based Aggregation

In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions such as sectors; microbes are commonly arranged in a taxonomic hierarchy from species to kingdoms; street blocks belong to neighborhoods, which in turn belong to larger-scale regions. The problem of tree-based aggregation that we consider in this paper asks which of these tree-defined subgroups of leaves should really be treated as a single entity and which of these entities should be distinguished from each other. We introduce the false split rate, an error measure that describes the degree to which subgroups have been split when they should not have been. We then propose a multiple hypothesis testing algorithm for tree-based aggregation, which we prove controls this error measure. We focus on two main examples of tree-based aggregation, one which involves aggregating means and the other which involves aggregating regression coefficients. We apply this methodology to aggregate stocks based on their volatility and to aggregate neighborhoods of New York City based on taxi fares.
Are Neural Ranking Models Robust?

Recently, we have witnessed the bloom of neural ranking models in the information retrieval (IR) field. So far, much effort has been devoted to developing effective neural ranking models that can generalize well on new data. There has been less attention paid to the robustness perspective. Unlike the effectiveness, which is about the average performance of a system under normal purpose, robustness cares more about the system performance in the worst case or under malicious operations instead. When a new technique enters into the real-world application, it is critical to know not only how it works in average, but also how would it behave in abnormal situations. So, we raise the question in this work: Are neural ranking models robust? To answer this question, first, we need to clarify what we refer to when we talk about the robustness of ranking models in IR. We show that robustness is actually a multi-dimensional concept and there are three ways to define it in IR: (1) the performance variance under the independent and identically distributed (I.I.D.) setting; (2) the out-of-distribution (OOD) generalizability; and (3) the defensive ability against adversarial operations. The latter two definitions can be further specified into two different perspectives, respectively, leading to five robustness tasks in total. Based on this taxonomy, we build corresponding benchmark datasets, design empirical experiments, and systematically analyze the robustness of several representative neural ranking models against traditional probabilistic ranking models and learning-to-rank (LTR) models. The empirical results show that there is no simple answer to our question. While neural ranking models are less robust against other IR models in most cases, some of them can still win two out of five tasks. This is the first comprehensive study on the robustness of neural ranking models. We believe the way we study the robustness as well as our findings would be beneficial to the IR community. We will also release all the data and codes to facilitate the future research in this direction.
Application of the small-tip-angle approximation in the toggling frame for the design of analytic robust pulses in quantum control

We apply the Small Tip-Angle Approximation in the Toggling Frame in order to analytically design robust pulses against resonance offsets for state to state transfer in two-level quantum systems. We show that a broadband or a local robustness up to an arbitrary order can be achieved. We provide different control parameterizations to satisfy experimental constraints and limitations on the amplitude or energy of the pulse. A comparison with numerical optimal solutions is made.
Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles

In this paper, we extend our PrInDT method (Weihs & Buschfeld 2021a) towards undersampling with different percentages of the smaller and the larger classes (psmall and plarge), stratification of predictors, varying the prediction threshold, and measuring variable importance in ensembles. An application of these methods to a linguistic example suggests the following: 1. In undersampling, a careful selection of the percentages plarge and psmall is important for building models with high balanced accuracies; 2. Stratification of predictors does not majorly enhance balanced accuracies; 3. Lowering the prediction threshold for the smaller class turns out to be an alternative method to undersampling because it increases the likelihood of the smaller class being selected. Finally, we introduce a method for ranking predictor importance that allows for a straightforward interpretation of the results.
Deep Learning Classification of Lake Zooplankton

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.
Learning Oculomotor Behaviors from Scanpath

Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing eye-tracking data, we develop a novel method that creates rich representations of oculomotor scanpaths to facilitate the learning of downstream tasks. The proposed stimulus-agnostic Oculomotor Behavior Framework (OBF) model learns human oculomotor behaviors from unsupervised and semi-supervised tasks, including reconstruction, predictive coding, fixation identification, and contrastive learning tasks. The resultant pre-trained OBF model can be used in a variety of applications. Our pre-trained model outperforms baseline approaches and traditional scanpath methods in autism spectrum disorder and viewed-stimulus classification tasks. Ablation experiments further show our proposed method could achieve even better results with larger model sizes and more diverse eye-tracking training datasets, supporting the model’s potential for future eye-tracking applications. Open source code: http://github.com/BeibinLi/OBF.
The Used, the Bloated, and the Vulnerable: Reducing the Attack Surface of an Industrial Application

Software reuse may result in software bloat when significant portions of application dependencies are effectively unused. Several tools exist to remove unused (byte)code from an application or its dependencies, thus producing smaller artifacts and, potentially, reducing the overall attack surface. In this paper we evaluate the ability of three debloating tools to distinguish which dependency classes are necessary for an application to function correctly from those that could be safely removed. To do so, we conduct a case study on a real-world commercial Java application. Our study shows that the tools we used were able to correctly identify a considerable amount of redundant code, which could be removed without altering the results of the existing application tests. One of the redundant classes turned out to be (formerly) vulnerable, confirming that this technique has the potential to be applied for hardening purposes. However, by manually reviewing the results of our experiments, we observed that none of the tools can handle a widely used default mechanism for dynamic class loading.
Natural language-guided programming

In today’s software world with its cornucopia of reusable software libraries, when a programmer is faced with a programming task that they suspect can be completed through the use of a library, they often look for code examples using a search engine and then manually adapt found examples to their specific context of use. We put forward a vision based on a new breed of developer tools that have the potential to largely automate this process. The key idea is to adapt code autocompletion tools such that they take into account not only the developer’s already-written code but also the intent of the task the developer is trying to achieve next, formulated in plain natural language. We call this practice of enriching the code with natural language intent to facilitate its completion natural language-guided programming. To show that this idea is feasible we design, implement and benchmark a tool that solves this problem in the context of a specific domain (data science) and a specific programming language (Python). Central to the tool is the use of language models trained on a large corpus of documented code. Our initial experiments confirm the feasibility of the idea but also make it clear that we have only scratched the surface of what may become possible in the future. We end the paper with a comprehensive research agenda to stimulate additional research in the budding area of natural language-guided programming.
Nanoparticles and Nanostructured Films with TGF-β3: Preparation, Characterization, and Efficacy

TGF-β3 has been reported to have a strong therapeutic efficacy in wound healing when externally administered, but TGF-β3’s active form is rapidly metabolized and removed from the body. Therefore, a drug delivery system that can provide a new non-toxic and an effective treatment that could be locally applied and also be able to protect the stability of the protein and provide controlled release is required. The aim of the study is to prepare and characterize nanoparticles and nanostructured films with TGF-β3 and to evaluate in vitro cytotoxicity of the loaded nanoparticles. PCL-based films containing TGF-β3 or TGF-β3-loaded PLGA nanoparticles were prepared with non-toxic modified solvent displacement method. The particle size and protein loading efficiency of TGF-β3-loaded PLGA nanoparticles were 204.9 ± 10.3 nm and 42.42 ± 2.03%, respectively. In vitro release studies of TGF-β3-loaded PLGA nanoparticle formulations revealed that the protein was completely released from the nanoparticles at the end of 24 h. In vitro release profile of film formulation containing TGF-β3-loaded nanoparticles was similar. TGF-β3 released from nanoparticles do not have a significant effect on proliferation of HepG2 cells demonstrating their biocompatibility. Additionally, prepared films were tested with in vivo wound healing mouse model and showed to heal significantly faster and with improved scarring. PCL films loaded with TGF-β3 or TGF-β3 nanoparticles prepared in this study may be an effective treatment approach for wound healing therapy after injury.
Response of Laryngopharyngeal Symptoms to Transoral Incisionless Fundoplication in Patients with Refractory Proven Gastroesophageal Reflux

Objective: Patients with laryngopharyngeal reflux (LPR) symptoms may not respond to proton pump inhibitors (PPI) if they have an alternative laryngeal diagnosis or high-volume reflux. Transoral incisionless fundoplication (TIF) or TIF with concomitant hiatal hernia repair (cTIF) are effective in decreasing symptoms of gastroesophageal reflux disease (GERD) but are not well studied in patients with LPR symptoms. This prospective multicenter study assessed the patient-reported and clinical outcomes after TIF/cTIF in patients with LPR symptoms and proven GERD. Methods: Patients with refractory LPR symptoms (reflux symptom index [RSI] > 13) and with erosive esophagitis, Barrett’s esophagus, and/or pathologic acid reflux by distal esophageal pH testing were evaluated before and after a minimum of 6 months after TIF/cTIF. The primary outcome was normalization of RSI. Secondary outcomes were >50% improvement in GERD-Health-Related Quality of Life (GERD-HRQL), normalization of esophageal acid exposure time, discontinuation of PPI, and patient satisfaction. Results: Forty-nine patients had TIF (n = 26) or cTIF (n = 23) with at least 6 months follow-up. Mean pre- and post TIF/cTIF RSI were 23.6 and 5.9 (mean difference: 17.7, P < .001). Post TIF/cTIF, 90% of patients had improved GERD-HQRL score, 85% normalized RSI, 75% normalized esophageal acid exposure time, and 80% discontinued PPI. No serious procedure-related adverse events occurred. Patient satisfaction was 4% prior to TIF/cTIF and 73% after TIF/cTIF (P < .001). Conclusion: In patients with objective evidence of GERD, TIF, or cTIF are safe and effective in controlling LPR symptoms as measured by normalization of RSI and improvement in patient satisfaction after TIF/cTIF. Level of Evidence Level 4
Giardia duodenalis: Biology and Pathogenesis

Giardia duodenalis captured the attention of Leeuwenhoek in 1681 while he was examining his own diarrheal stool, but, ironically, it did not really gain attention as a human pathogen until the 1960s, when outbreaks were reported. Key technological advances, including in vitro cultivation, genomic and proteomic databases, and advances in microscopic and molecular approaches, have led to understand that this is a eukaryotic organism with a reduced genome rather than a truly premitochondriate eukaryote. SUMMARY Giardia duodenalis captured the attention of Leeuwenhoek in 1681 while he was examining his own diarrheal stool, but, ironically, it did not really gain attention as a human pathogen until the 1960s, when outbreaks were reported. Key technological advances, including in vitro cultivation, genomic and proteomic databases, and advances in microscopic and molecular approaches, have led to an understanding that this is a eukaryotic organism with a reduced genome rather than a truly premitochondriate eukaryote. This has included the discovery of mitosomes (vestiges of mitochondria), a transport system with many of the features of the Golgi apparatus, and even evidence for a sexual or parasexual cycle. Cell biology approaches have led to a better understanding of how Giardia survives with two nuclei and how it goes through its life cycle as a noninvasive organism in the hostile environment of the lumen of the host intestine. Studies of its immunology and pathogenesis have moved past the general understanding of the importance of the antibody response in controlling infection to determining the key role of the Th17 response. This work has led to understanding of the requirement for a balanced host immune response that avoids the extremes of an excessive response with collateral damage or one that is unable to clear the organism. This understanding is especially important in view of the remarkable ranges of early manifestations, which range from asymptomatic to persistent diarrhea and weight loss, and longer-term sequelae that include growth stunting in children who had no obvious symptoms and a high frequency of postinfectious irritable bowel syndrome (IBS).
The removal of heavy metal ions from aqueous solutions by amine functionalized cellulose pretreated with microwave-H2O2

A new biosorbent (PEI/SA-MCCMV) with abundant amino and carboxyl groups was prepared by grafting polyethylenimine (PEI) onto carboxylated microcrystalline cellulose (SA-MCCMV), which was obtained through grafting succinic anhydride (SA) on pretreated microcrystalline cellulose (MCCMV) with microwave-H2O2. It was confirmed by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray photoelectron microscopy (XPS) and thermogravimetric (TG) that the amino and carboxyl groups were introduced onto the microcrystalline cellulose, and the amino and carboxyl groups content were 2.61 mmol g−1 and 4.64 mmol g−1, respectively. The effects of the contact time and pH on heavy metal ion uptake were investigated. The adsorption kinetic data was described well with the pseudo-second-order model (R2 > 0.99) and the adsorption isotherms were well fitted by the Freundlich isotherm model, demonstrating that chemisorption was the rate-controlling factor for heavy metal ion adsorption on the PEI/SA-MCCMV biosorbent. Furthermore, the remarkable adsorption capacity (217.3 and 357.1 mg g−1 for Cd(II) and Pb(II), respectively) obtained from the Langmuir isotherm indicated that this biomass adsorbent has a promising application in water treatment.