Miracle-dz/UniFAR-FTUs huggingface dataset:
A click chemistry-based, free radical-initiated delivery system for the capture and release of payloads

Abstract Click chemistries are efficient and selective reactions that have been leveraged for multi-stage drug delivery. A multi-stage system allows independent delivery of targeting molecules and drug payloads, but targeting first-phase materials specifically to disease sites remains a challenge. Stimuli-responsive systems are an emerging strategy where common pathophysiological triggers are used to target payloads. Oxidative stress is widely implicated in disease, and we have previously demonstrated that reactive oxygen species (ROS) can crosslink and immobilize polyethylene glycol diacrylate (PEGDA) in tissue mimics. To build on these promising results, we present a two-step, catch-and-release system using azide-DBCO click chemistry and demonstrate the capture and eventual release of a fluorescent payload at defined times after the formation of a PEGDA capturing net. The azide component is included with radical-sensitive PEGDA, and the payload is conjugated to the DBCO group. In cell-free and cell-based tissue mimic models, azides were incorporated at 0–30% in the first-phase polymer net, and DBCO was delivered at 2.5–10 µM in the second phase to control payload delivery. The payload could be captured at multiple timepoints after initial net formation, yielding a flexible and versatile targeting system. Matrix metalloproteinase (MMP)-degradable peptides were incorporated into the polymer backbone to engineer fluorescent payload release by MMPs, which are broadly upregulated in diseases, through degradation of the capture net and directly from the DBCO. Taken together, this research demonstrates proof-of-principle for a responsive and clickable biomaterial to serve as a multi-potent agent for the treatment of diseases compounded by high free radicals.
Effect of gate length on breakdown voltage in AlGaN/GaN high-electron-mobility transistor

The effects of gate length LG on breakdown voltage VBR are investigated in AlGaN/GaN high-electron-mobility transistors (HEMTs) with LG = 1 μm~ 20 μm. With the increase of LG, VBR is first increased, and then saturated at LG = 3 μm. For the HEMT with LG = 1 μm, breakdown voltage VBR is 117 V, and it can be enhanced to 148 V for the HEMT with LG = 3 μm. The gate length of 3 μm can alleviate the buffer-leakage-induced impact ionization compared with the gate length of 1 μm, and the suppression of the impact ionization is the reason for improving the breakdown voltage. A similar suppression of the impact ionization exists in the HEMTs with LG > 3 μm. As a result, there is no obvious difference in breakdown voltage among the HEMTs with LG = 3 μm~20 μm, and their breakdown voltages are in a range of 140 V–156 V.
Design of a decentralized tracking control for a class of switched large-scale systems

This paper proposes a new design of a decentralized output-feedback tracking control for a class of switched large-scale systems with external bounded disturbances. The controller proposed herein is synthesized to satisfy the robust H  tracking performance with local disturbance attenuation levels. Based on multiple switched Lyapunov functions, sufficient conditions proving the existence of the proposed controller are formulated in terms of Linear Matrix Inequalities (LMI). A deep simulation is proposed to illustrate the effectiveness of the obtained results.
Extrinsic Manipulation on a Support Plane by Learning Regrasping

Extrinsic manipulation, a technique that enables robots to leverage extrinsic resources for object manipulation, presents practical yet challenging scenarios. Particularly in the context of extrinsic manipulation on a supporting plane, regrasping becomes essential for achieving the desired final object poses. This process involves sequential operation steps and stable placements of objects, which provide grasp space for the robot. To address this challenge, we focus on predicting diverse placements of objects on the plane using deep neural networks. A framework that comprises orientation generation, placement refinement, and placement discrimination stages is proposed, leveraging point clouds to obtain precise and diverse stable placements. To facilitate training, a large-scale dataset is constructed, encompassing stable object placements and contact information between objects. Through extensive experiments, our approach is demonstrated to outperform the start-of-the-art, achieving an accuracy rate of 90.4\% and a diversity rate of 81.3\% in predicted placements. Furthermore, we validate the effectiveness of our approach through real-robot experiments, demonstrating its capability to compute sequential pick-and-place steps based on the predicted placements for regrasping objects to goal poses that are not readily attainable within a single step. Videos and dataset are available at https://sites.google. com/view/pmvlr2022/.
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds

We investigate the problem of fixed-budget best arm identification (BAI) for minimizing expected simple regret. In an adaptive experiment, a decision maker draws one of multiple treatment arms based on past observations and observes the outcome of the drawn arm. After the experiment, the decision maker recommends the treatment arm with the highest expected outcome. We evaluate the decision based on the expected simple regret, which is the difference between the expected outcomes of the best arm and the recommended arm. Due to inherent uncertainty, we evaluate the regret using the minimax criterion. First, we derive asymptotic lower bounds for the worst-case expected simple regret, which are characterized by the variances of potential outcomes (leading factor). Based on the lower bounds, we propose the Two-Stage (TS)-Hirano-Imbens-Ridder (HIR) strategy, which utilizes the HIR estimator (Hirano et al., 2003) in recommending the best arm. Our theoretical analysis shows that the TS-HIR strategy is asymptotically minimax optimal, meaning that the leading factor of its worst-case expected simple regret matches our derived worst-case lower bound. Additionally, we consider extensions of our method, such as the asymptotic optimality for the probability of misidentification. Finally, we validate the proposed method's effectiveness through simulations.
Biotransformation: Zellschutz und Entgiftung

Zusammenfassung Bei chronischen Erkrankungen sollte stets die Leberbelastung durch endogene und exogene Gifte bzw. die Entgiftungsleistung beachtet werden. Zahlreiche Single-Nukleotid-Polymorphismen können die Entgiftung beeinträchtigen, das Risko für nichtalkoholische Fettleber und metabolisches Syndrom steigt bei den Betroffenen. Eine umfangreiche Stuhl- und Verdauungsanalyse ist wichtiger Bestandteil der Diagnostik der Leberbelastung. In der Zellschutz- und Entgiftungstherapie spielen die mitochondrialen Schutzsubstanzen Coenzym Q10, α-Liponsäure, Glutathion, Vitamin E, Niacin, Melatonin und Silymarin aus der Mariendistel eine wichtige Rolle. Hyperbare Sauerstofftherapie verbessert die Regeneration von Leberzellen und die Entgiftung durch Plasmaoxygenierung.
Die Darm-Hirn-Verbindung: Leaky Gut – Leaky Brain

Zusammenfassung Zonulin ist ein Marker zur Feststellung einer erhöhten Durchlässigkeit der Darmbarriere und lässt sich im Blut oder in Stuhlproben nachweisen. Bei einem erhöhten Zonulinwert kommt es zu einem verstärkten Einstrom von Bakterienbestandteilen, unverdauten Nahrungsmitteln etc. in den Blutkreislauf. In der Folge können Allergien und Entzündungsreaktionen auftreten bzw. verstärkt werden. Entzündungsreaktionen im Darm können auch zu einem Leaky Brain, also zu einer Durchlässigkeit der Blut-Hirn-Schranke, führen. Verschiedene Blutmarker geben Hinweise auf gestörte Barrieren im Organismus, die als Hauptursache zahlreicher chronischer Erkrankungen gelten.
A Fully-distributed Shape-aware Neural Controller for Modular Robots

Modular robots are promising for their versatility and large design freedom. Modularity can also enable automatic assembly and reconfiguration, be it autonomous or via external machinery. However, these procedures are error-prone and often result in misassemblings. This, in turn, can cause catastrophic effects on the robot functionality, as the controller deployed in each module is optimized for a different robot shape than the actual one. In this work, we address such shortcoming by proposing a shape-aware modular controller, operating with (1) a self-discovery phase, in which each module controller identifies the shape it is assembled in, followed by (2) a parameter selection phase, where the controller selects its parameters according to the inferred shape. We deploy a self-classifying neural cellular automaton for phase (1), and we leverage evolutionary optimization for implementing a library of controller parameters for phase (2). We test the validity of the proposed method considering voxel-based soft robots, a class of modular soft robots, and the task of locomotion. Our findings confirm the effectiveness of such a controller paradigm, and also show that it can be used to partially overcome unforeseen damages or assembly mistakes.
Differentiable Soft-Robot Generation

Soft robots have multiple potential applications for artificial life, ergonomics and human interaction but they also present many design and control challenges. One of these challenges is morphogenesis adapted to common tasks such as gripping and walking. We propose a method based on the Material Point Method (MPM) and numerical optimization to co-optimize effectively the morphology and muscle actuation of a 3D soft robot in three different scenarios: walking, walking up stairs and avoiding obstacles. Thanks to efficient optimization techniques, we allow virtual soft robot morphogenesis with at least four orders of magnitude more variables and fewer iterations as compared to previous work based on genetic algorithms. We demonstrate generation of a soft robot at a resolution of 1003 and 2 million parameters. We extend our approach to generate rigid structure and create a multi rigidity robot. Finally, we introduce a concept of energy fraction to limit muscle mass.
Microwave Signal Sources Based on Spin-Torque Nano-Oscillators

The spin-transfer torque (STT) effect provides a new method of manipulating the magnetization in nano-scale objects. According to the STT effect, a bias dc current traversing magnetic multilayers can transfer angular magnetic moments from one layer to another, which can give rise to the microwave dynamics of magnetization in the layer. This phenomenon can be used to develop novel microwave signal sources (MSSs). In this work, we review the main research results on MSSs based on the STT effect obtained at Taras Shevchenko National University of Kyiv and its foreign collaborating institutions in the recent years.
Translating a low-resource language using GPT-3 and a human-readable dictionary

We investigate how well words in the polysynthetic language Inuktitut can be translated by combining dictionary definitions, without use of a neural machine translation model trained on parallel text. Such a translation system would allow natural language technology to benefit from resources designed for community use in a language revitalization or education program, rather than requiring a separate parallel corpus. We show that the text-to-text generation capabilities of GPT-3 allow it to perform this task with BLEU scores of up to 18.5. We investigate prompting GPT-3 to provide multiple translations, which can help slightly, and providing it with grammar information, which is mostly ineffective. Finally, we test GPT-3’s ability to derive morpheme definitions from whole-word translations, but find this process is prone to errors including hallucinations.
Automatic Coding at Scale: Design and Deployment of a Nationwide System for Normalizing Referrals in the Chilean Public Healthcare System

The disease coding task involves assigning a unique identifier from a controlled vocabulary to each disease mentioned in a clinical document. This task is relevant since it allows information extraction from unstructured data to perform, for example, epidemiological studies about the incidence and prevalence of diseases in a determined context. However, the manual coding process is subject to errors as it requires medical personnel to be competent in coding rules and terminology. In addition, this process consumes a lot of time and energy, which could be allocated to more clinically relevant tasks. These difficulties can be addressed by developing computational systems that automatically assign codes to diseases. In this way, we propose a two-step system for automatically coding diseases in referrals from the Chilean public healthcare system. Specifically, our model uses a state-of-the-art NER model for recognizing disease mentions and a search engine system based on Elasticsearch for assigning the most relevant codes associated with these disease mentions. The system’s performance was evaluated on referrals manually coded by clinical experts. Our system obtained a MAP score of 0.63 for the subcategory level and 0.83 for the category level, close to the best-performing models in the literature. This system could be a support tool for health professionals, optimizing the coding and management process. Finally, to guarantee reproducibility, we publicly release the code of our models and experiments.
SigMoreFun Submission to the SIGMORPHON Shared Task on Interlinear Glossing

In our submission to the SIGMORPHON 2023 Shared Task on interlinear glossing (IGT), we explore approaches to data augmentation and modeling across seven low-resource languages. For data augmentation, we explore two approaches: creating artificial data from the provided training data and utilizing existing IGT resources in other languages. On the modeling side, we test an enhanced version of the provided token classification baseline as well as a pretrained multilingual seq2seq model. Additionally, we apply post-correction using a dictionary for Gitksan, the language with the smallest amount of data. We find that our token classification models are the best performing, with the highest word-level accuracy for Arapaho and highest morpheme-level accuracy for Gitksan out of all submissions. We also show that data augmentation is an effective strategy, though applying artificial data pretraining has very different effects across both models tested.
Hybrid Denoising Algorithm Based on Wavelet Decomposition and Classification Filtering

Noise reduction is a widespread problem in image processing field. In this paper, a kind of image denoising method based on wavelet decomposition and classification filtering is proposed. First, use wavelet thresholding denoising method to remove the noise in the high frequency part of the image, then use classification filtering denoising algorithm to remove the noise in the low frequency approximation part of the image, then use high frequency wavelet coefficients after threshold processing to reconstruction of high frequency part of the image, at last, combined with low frequency part and high frequency part image to get final noise reduction image. This algorithm not only can deal with gauss noise, but also can reduce salt and pepper noise, and gauss noise and salt and pepper mixed noise. This algorithm both can be used for grayscale image denoising and RGB colour image denoising. This algorithm structure is simple and easy to implement. Experiments show that this algorithm has good performance in denoising, noise reduction image is clarity.
Myths about Writing Systems in Speech & Language Technology

Natural language processing is largely focused on written text processing. However, many computational linguists tacitly endorse myths about the nature of writing. We highlight two of these myths—the conflation of language and writing, and the notion that Chinese, Japanese, and Korean writing is ideographic—and suggest how the community can dispel them.
The SIGMORPHON 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year’s task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14% in the crosslingual subtask and 14% in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples.
Transfer Learning for Low-Resource Clinical Named Entity Recognition

We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.
Tü-CL at SIGMORPHON 2023: Straight-Through Gradient Estimation for Hard Attention

This paper describes our systems participating in the 2023 SIGMORPHON Shared Task on Morphological Inflection and in the 2023 SIGMORPHON Shared Task on Interlinear Glossing. We propose methods to enrich predictions from neural models with discrete, i.e. interpretable, information. For morphological inflection, our models learn deterministic mappings from subsets of source lemma characters and morphological tags to individual target characters, which introduces interpretability. For interlinear glossing, our models learn a shallow morpheme segmentation in an unsupervised way jointly with predicting glossing lines. Estimated segmentation may be useful when no ground-truth segmentation is available. As both methods introduce discreteness into neural models, our technical contribution is to show that straight-through gradient estimators are effective to train hard attention models.
Interactive Span Recommendation for Biomedical Text

Motivated by the scarcity of high-quality labeled biomedical text, as well as the success of data programming, we introduce KRISS-Search. By leveraging the Unified Medical Language Systems (UMLS) ontology, KRISS-Search addresses an interactive few-shot span recommendation task that we propose. We first introduce unsupervised KRISS-Search and show that our method outperforms existing methods in identifying spans that are semantically similar to a given span of interest, with >50% AUPRC improvement relative to PubMedBERT. We then introduce supervised KRISS-Search, which leverages human interaction to improve the notion of similarity used by unsupervised KRISS-Search. Through simulated human feedback, we demonstrate an enhanced F1 score of 0.68 in classifying spans as semantically similar or different in the low-label setting, outperforming PubMedBERT by 2 F1 points. Finally, supervised KRISS-Search demonstrates competitive or superior performance compared to PubMedBERT in few-shot biomedical named entity recognition (NER) across five benchmark datasets, with an average improvement of 5.6 F1 points. We envision KRISS-Search increasing the efficiency of programmatic data labeling and also providing broader utility as an interactive biomedical search engine.
Morphological reinflection with weighted finite-state transducers

This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection. In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection. We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection. We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency. Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.
SIGMORPHON 2022 Shared Task on Grapheme-to-Phoneme Conversion Submission Description: Sequence Labelling for G2P

This paper describes our participation in the Third SIGMORPHON Shared Task on Grapheme-to-Phoneme Conversion (Low-Resource and Cross-Lingual) (McCarthy et al.,2022). Our models rely on different sequence labelling methods. The main model predicts multiple phonemes from each grapheme and is trained using CTC loss (Graves et al., 2006). We find that sequence labelling methods yield worse performance than the baseline when enough data is available, but can still be used when very little data is available. Furthermore, we demonstrate that alignments learned by the sequence labelling models can be easily inspected.
Cross-domain German Medical Named Entity Recognition using a Pre-Trained Language Model and Unified Medical Semantic Types

Information extraction from clinical text has the potential to facilitate clinical research and personalized clinical care, but annotating large amounts of data for each set of target tasks is prohibitive. We present a German medical Named Entity Recognition (NER) system capable of cross-domain knowledge transferring. The system builds on a pre-trained German language model and a token-level binary classifier, employing semantic types sourced from the Unified Medical Language System (UMLS) as entity labels to identify corresponding entity spans within the input text. To enhance the system’s performance and robustness, we pre-train it using a medical literature corpus that incorporates UMLS semantic term annotations. We evaluate the system’s effectiveness on two German annotated datasets obtained from different clinics in zero- and few-shot settings. The results show that our approach outperforms task-specific Condition Random Fields (CRF) classifiers in terms of accuracy. Our work contributes to developing robust and transparent German medical NER models that can support the extraction of information from various clinical texts.
Systematic review of the values and preferences regarding the use of injectable pre‐exposure prophylaxis to prevent HIV acquisition

Pre‐exposure prophylaxis (PrEP) is an important HIV prevention option. Two randomized trials have provided efficacy evidence for long‐acting injectable cabotegravir (CAB‐LA) as PrEP. In considering CAB‐LA as an additional PrEP modality for people at substantial risk of HIV, it is important to understand community response to injectable PrEP. We conducted a systematic review of values, preferences and perceptions of acceptability for injectable PrEP to inform global guidance.
Updating public accountability: a conceptual framework of voluntary accountability

ABSTRACT Voluntary accountability is widely documented. Yet, the concept does not sit comfortably within prevailing frameworks that conceptualize accountability as an obligation. This paper sets out to update our understanding of accountability by explicitly embedding this phenomenon within the broader conceptual structure of public accountability. It demonstrates how the proposed conceptual framework can help us distinguish between voluntary accountability and related, but distinct, concepts, such as transparency. It further illustrates how the framework can help us answer pressing questions about the varying rationales behind voluntary account-giving, the audiences targeted by accounts as well as the consequences of voluntary accountability for democracy.