Miracle-dz/UniFAR-FTUs huggingface dataset:
An N‐glycopeptide MS/MS data analysis workflow leveraging two complementary glycoproteomic software tools for more confident identification and assignments

Complete coverage of all N‐glycosylation sites on the SARS‐CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS2 spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site‐specific N‐glycosylation pattern could be constructed based on quantifying only the overlapping subset of N‐glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.
Initial experience of coaxial percutaneous liver biopsy with tract embolization using N-Butyl cyanoacrylate

Abstract Objective Bleeding occurs after liver biopsy in up to 10.9% cases, and patients with impaired hemostasis or ascites are considered to have absolute or relative contraindications. N-butyl cyanoacrylate enables immediate hemostasis, even in lethal situations. Therefore, percutaneous liver biopsy combined with tract embolization using N-butyl cyanoacrylate is expected to enable safe biopsy, even in patients for whom conventional biopsy is contraindicated. Here we describe our initial experience with coaxial percutaneous biopsy with tract embolization using N-butyl cyanoacrylate. Materials and methods Eighty-six patients who underwent tract embolization using N-butyl cyanoacrylate between October 2014 and July 2020, including 21 patients who had absolute or relative contraindications for liver biopsy, were retrospectively analyzed. Tract embolization using N-butyl cyanoacrylate comprised two steps: (1) liver biopsy with a biopsy needle inserted via a coaxial introducer needle and (2) embolization of the puncture route by injecting N-butyl cyanoacrylate via the coaxial needle. Results No complications occurred in any patient. The mean number of biopsies per patient was 3.30 (range, 1–7). Histologically adequate samples were acquired in all cases, and pathological diagnoses were obtained. The mean time required for tract embolization was 52.8 s (range, 6–132 s). The mean peak skin dose was 9.97 mGy (range, 2–68 mGy), which is far below the 3-Gy threshold dose for temporary erythema. Conclusions This proposed technique may be a promising and straightforward alternative to improve the management of patients with severe liver disease by allowing safer biopsy, including patients for whom conventional liver biopsy is contraindicated.
Associations between parental dispositional attributions, dismissing and coaching reactions to children’s emotions, and children’s problem behaviour moderated by child gender

ABSTRACT This study examined whether parents’ attribution of their child’s emotions (internalizing, externalizing) to dispositional causes is associated with children’s problem behaviour (internalizing, externalizing). The mediating roles of parents’ emotion-dismissing and -coaching reactions and the moderating role of child’s gender was also examined. Participants were 241 US parents with a child (43% girls) between the ages of 5 and 7. Parents were presented with vignettes in which a gender-neutral child displayed internalizing and externalizing emotions and were asked to imagine their own child in the vignettes. Subsequently, parents indicated whether they attributed the child’s emotion to dispositional causes and the likelihood of reacting in an emotion-dismissing and -coaching way in each situation. Child problem behaviour was measured using the CBCL. Results show that parental dispositional attributions were associated with child internalizing and externalizing problems, and this association was consistently mediated by emotion-dismissing reactions. The association between parental dispositional attributions and emotion-dismissing, as well as its indirect effect on child internalizing problems, was stronger for boys than for girls, whereas the indirect effect via emotion-coaching was stronger for girls than for boys. Thus, the parental attribution process seems to be different for boys and girls.
MMER: Multimodal Multi-task Learning for Speech Emotion Recognition

In this paper, we propose MMER, a novel Multimodal Multi-task learning approach for Speech Emotion Recognition. MMER leverages a novel multimodal network based on early-fusion and cross-modal self-attention between text and acoustic modalities and solves three novel auxiliary tasks for learning emotion recognition from spoken utterances. In practice, MMER outperforms all our baselines and achieves state-of-the-art performance on the IEMOCAP benchmark. Additionally, we conduct extensive ablation studies and results analysis to prove the effectiveness of our proposed approach.
Discovering Dynamic Causal Space for DAG Structure Learning

Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.The recent invention of differentiable score-based DAG learners is a crucial enabler, which reframes the combinatorial optimization problem into a differentiable optimization with a DAG constraint over directed graph space. Despite their great success, these cutting-edge DAG learners incorporate DAG-ness independent score functions to evaluate the directed graph candidates, lacking in considering graph structure. As a result, measuring the data fitness alone regardless of DAG-ness inevitably leads to discovering suboptimal DAGs and model vulnerabilities. Towards this end, we propose a dynamic csusal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground-truth DAG. CASPER revises the learning process as well as enhances the DAG structure learning via adaptive attention to DAG-ness. Grounded by empirical visualization, CASPER, as a space, satisfies a series of desired properties, such as structure awareness and noise-robustness. Extensive experiments on both synthetic and real-world datasets clearly validate the superiority of our CASPER over the state-of-the-art causal discovery methods in terms of accuracy and robustness.
Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations

Neural implicits have become popular for representing surfaces because they offer an adaptive resolution and support arbitrary topologies. While previous works rely on ground truth point clouds, they often ignore the effect of input quality and sampling methods during reconstructing process. In this paper, we introduce a sampling method with an uncertainty-augmented surface implicit representation that employs a sampling technique that considers the geometric characteristics of inputs. To this end, we introduce a strategy that efficiently computes differentiable geometric features, namely, mean curvatures, to augment the sampling phase during the training period. The uncertainty augmentation offers insights into the occupancy and reliability of the output signed distance value, thereby expanding representation capabilities into open surfaces. Finally, we demonstrate that our method leads to state-of-the-art reconstructions on both synthetic and real-world data.
Memorization Capacity of Multi-Head Attention in Transformers

Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive. This paper investigates the memorization abilities of multi-head attention mechanisms, examining how many example sequences they can memorize, as a function of the number of heads and sequence length. Motivated by experimental findings on vision transformers, we introduce novel assumptions about the linear independence of input data, distinct from the commonly used general-position assumption. Under these assumptions, we demonstrate that an attention layer with $H$ heads, dimension $d$, and context size $n<d$, featuring $\Theta(Hd^2)$ parameters, can memorize $\Omega(Hn)$ examples. Our analysis sheds light on how different attention heads handle various example sequences, aided by the softmax operator's saturation property. We validate our findings through experiments on synthetic data.
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency

Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to an interpretable temporal pattern. While explainers from other modalities can be applied to time series, their inductive biases do not transfer well to the inherently challenging interpretation of time series. We present TimeX, a time series consistency model for training explainers. TimeX trains an interpretable surrogate to mimic the behavior of a pretrained time series model. It addresses the issue of model faithfulness by introducing model behavior consistency, a novel formulation that preserves relations in the latent space induced by the pretrained model with relations in the latent space induced by TimeX. TimeX provides discrete attribution maps and, unlike existing interpretability methods, it learns a latent space of explanations that can be used in various ways, such as to provide landmarks to visually aggregate similar explanations and easily recognize temporal patterns. We evaluate TimeX on eight synthetic and real-world datasets and compare its performance against state-of-the-art interpretability methods. We also conduct case studies using physiological time series. Quantitative evaluations demonstrate that TimeX achieves the highest or second-highest performance in every metric compared to baselines across all datasets. Through case studies, we show that the novel components of TimeX show potential for training faithful, interpretable models that capture the behavior of pretrained time series models.
Optimal Control for Articulated Soft Robots

Soft robots can execute tasks with safer interactions. However, control techniques that can effectively exploit the systems' capabilities are still missing. Differential dynamic programming (DDP) has emerged as a promising tool for achieving highly dynamic tasks. But most of the literature deals with applying the DDP to articulated soft robots by using numerical differentiation, in addition to using pure feed-forward control to perform explosive tasks. Further, underactuated compliant robots are known to be difficult to control and the use of DDP-based algorithms to control them is not yet addressed. We propose an efficient DDP-based algorithm for trajectory optimization of articulated soft robots that can optimize the state trajectory, input torques, and stiffness profile. We provide an efficient method to compute the forward dynamics and the analytical derivatives of series elastic actuators (SEA)/variable stiffness actuators (VSA) and underactuated compliant robots. We present a state-feedback controller that uses locally optimal feedback policies obtained from the DDP. We show through simulations and experiments that the use of feedback is crucial in improving the performance and stabilization properties of various tasks. We also show that the proposed method can be used to plan and control underactuated compliant robots with varying degrees of underactuation effectively.
Weakly-Supervised Conditional Embedding for Referred Visual Search

This paper introduces a new challenge for image similarity search in the context of fashion, addressing the inherent ambiguity in this domain stemming from complex images. We present Referred Visual Search (RVS), a task allowing users to define more precisely the desired similarity, following recent interest in the industry. We release a new large public dataset, LAION-RVS-Fashion, consisting of 272k fashion products with 842k images extracted from LAION, designed explicitly for this task. However, unlike traditional visual search methods in the industry, we demonstrate that superior performance can be achieved by bypassing explicit object detection and adopting weakly-supervised conditional contrastive learning on image tuples. Our method is lightweight and demonstrates robustness, reaching Recall at one superior to strong detection-based baselines against 2M distractors. Code, data and models are available at https://www.github. com/Simon-Lepage/CondViT-LRVSF .
A Short Proof of Allard’s and Brakke’s Regularity Theorems

We give new short proofs of Allard’s regularity theorem for varifolds with bounded first variation and Brakke’s regularity theorem for integral Brakke flows with bounded forcing. They are based on a decay of flatness, following from weighted versions of the respective monotonicity formulas, together with a characterization of non-homogeneous blow-ups using the viscosity approach introduced by Savin.
Influence of the Magnetic Dipole Moment of a Metal Nanoellipsoid on the Scattering of Electromagnetic Waves

The influence of the magnetic dipole moment of a non-spherical metal nanoparticle on the scattering of electromagnetic radiation by the particle has been studied in the framework of the kinetic approach. Analytical expressions for the scattering cross-section of spheroidal particles are obtained, and their dependence on the incident radiation wavelength and the nanoparticle eccentricity is analyzed. The contribution of a magnetic moment to the scattering at frequencies far from the plasmon resonance is shown to be comparable with that of the electric moment, with the ratio between the magnetic and electric moment contributions being maximum for spherical nanoparticles. The calculations are performed for an arbitrary ratio between the particle size and the free electron path, which enables our results to be compared with the Mie theory in the case where the electron scattering in the particle bulk dominates.
EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures

Gestures that share similarities in their forms and are related in their meanings, should be easier for learners to recognize and incorporate into their existing lexicon. In that regard, to be more readily accepted as standard by the Deaf and Hard of Hearing community, technical gestures in American Sign Language (ASL) will optimally share similar forms with their lexical neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical relations within a set of technical gestures. We use automated identification for 3 unique sub-lexical properties in ASL- location, handshape and movement. EdGCon assigned an iconicity rating based on the lexical property similarities of the new gesture with an existing set of technical gestures and the relatedness of the meaning of the new technical word to that of the existing set of technical words. We collected 30 ad hoc crowdsourced technical gestures from different internet websites and tested them against 31 gestures from the DeafTEC technical corpus. We found that EdGCon was able to correctly auto-assign the iconicity ratings 80.76% of the time.
Galois representations with large image in global Langlands correspondence

The global Langlands conjecture for $\text{GL}_n$ over a number field $F$ predicts a correspondence between certain algebraic automorphic representations $\pi$ of $\text{GL}_n(\mathbb{A}_F)$ and certain families $\{ \rho_{\pi,\ell} \}_\ell$ of $n$-dimensional $\ell$-adic Galois representations of $\text{Gal}(\overline{F}/F)$. In general, it is expected that the image of the residual Galois representations $\overline{\rho}_{\pi,\ell}$ of the $\rho_{\pi,\ell}$'s should be as large as possible for almost all primes $\ell$, unless there is an automorphic reason for it does not happen. In this paper, we study the images of certain compatible systems of Galois representations $\{\rho_{\pi,\ell} \}_\ell$ associated to regular algebraic, polarizable, cuspidal automorphic representations $\pi$ of $\text{GL}_n(\mathbb{A}_F)$ by using only standard techniques and currently available tools (e.g., Fontaine-Laffaille theory, Serre's modularity conjecture, classification of the maximal subgroups of Lie type groups, and known results about irreducibility of automorphic Galois representations and Langlands functoriality). In particular, when $F$ is a totally real field and $n$ is an odd prime number $\leq 393$, we prove that (under certain automorphic conditions) the images of the residual representations $\overline{\rho}_{\pi,\ell}$ are as large as possible for infinitely many primes $\ell$. In fact, we prove the large image conjecture (i.e. large image for almost all primes $\ell$) when $F=\mathbb{Q}$ and $n=5$.
Adaptive and Personalized Exercise Generation for Online Language Learning

Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student’s evolving knowledge states from their learning history and a controlled text generation model that generates exercise sentences based on the student’s current estimated knowledge state and instructor requirements of desired properties (e.g., domain knowledge and difficulty). We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises. Then, we discuss the potential use of our model in educational applications using various simulations. These simulations show that our model can adapt to students’ individual abilities and can facilitate their learning efficiency by personalizing learning sequences.
Efficient exact enumeration of single-source geodesics on a non-convex polyhedron

In this paper, we consider enumeration of geodesics on a polyhedron, where a geodesic means locally-shortest path between two points. Particularly, we consider the following preprocessing problem: given a point [Formula: see text] on a polyhedral surface and a positive real number [Formula: see text], to build a data structure that enables, for any point [Formula: see text] on the surface, to enumerate all geodesics from [Formula: see text] to [Formula: see text] whose length is less than [Formula: see text]. First, we present a naive algorithm by removing the trimming process from the MMP algorithm (1987). Next, we present an improved algorithm which is practically more efficient on a non-convex polyhedron, in terms of preprocessing time and memory consumption. Moreover, we introduce a single-pair geodesic graph to succinctly encode a result of geodesic query. Lastly, we compare these naive and improved algorithms by some computer experiments.
Utilizing ChatGPT to Enhance Clinical Trial Enrollment

Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.