Miracle-dz/UniFAR-FTUs huggingface dataset:
An ab initio Study of the Structure and Energetics of Hydrogen Bonding in Ionic Liquids

Unlike typical hydrogen-bonded networks such as water, hydrogen bonded ionic liquids display some unusual characteristics due to the complex interplay of electrostatics, polarization, and dispersion forces in the bulk. Protic ionic liquids in particular contain close-to traditional linear hydrogen bonds that define their physicochemical properties. This work investigates whether hydrogen bonded ionic liquids (HBILs) can be differentiated from aprotic ionic liquids with no linear hydrogen bonds using state-of-the-art ab initio calculations. This is achieved through geometry optimizations of a series of single ion pairs of HBILs in the gas phase and an implicit solvent. Using benchmark CCSD(T)/CBS calculations, the electrostatic and dispersion components of the interaction energy of these systems are compared with those of aprotic ionic liquids. The inclusion of the implicit solvent significantly influenced geometries of single ion pairs, with the gas phase shortening the hydrogen bond to reduce electrostatic interactions. HBILs were found to have stronger interactions by at least 10EtMeNH0 kJ mol−1 over aprotic ILs, clearly highlighting the electrostatic nature of hydrogen bonding. Geometric and energetic parameters were found to complement each other in determining the extent of hydrogen bonding present in these ionic liquids.
Tischler graphs of critically fixed rational maps and their applications

A rational map $f:\widehat{\mathbb{C}}\to\widehat{\mathbb{C}}$ on the Riemann sphere $\widehat{\mathbb{C}}$ is called critically fixed if each critical point of $f$ is fixed under $f$. In this article we study properties of a combinatorial invariant, called Tischler graph, associated with such a map. More precisely, we show that the Tischler graph of a critically fixed rational map is always connected, establishing a conjecture made by Kevin Pilgrim. We also discuss the relevance of this result for classical open problems in holomorphic dynamics, such as combinatorial classification problem and global curve attractor problem.
Practical Reinforcement Learning of Stabilizing Economic MPC

Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MPC) is a popular control technique which is able to deal with nonlinear dynamics and state and input constraints. The main drawback of MPC is the need of identifying an accurate model, which in many cases cannot be easily obtained. Because of model inaccuracy, MPC can fail at delivering satisfactory closed-loop performance. Using RL to tune the MPC formulation or, conversely, using MPC as a function approximator in RL allows one to combine the advantages of the two techniques. This approach has important advantages, but it requires an adaptation of the existing algorithms. We therefore propose an improved RL algorithm for MPC and test it in simulations on a rather challenging example.
Learning Classifiers for Target Domain with Limited or No Labels

In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training"models from scratch,"and methods that adapt existing models, trained on the presented training environment, to the new scenario are required. We propose a novel visual attribute encoding method that encodes each image as a low-dimensional probability vector composed of prototypical part-type probabilities. The prototypes are learnt to be representative of all training data. At test-time we utilize this encoding as an input to a classifier. At test-time we freeze the encoder and only learn/adapt the classifier component to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct extensive experiments on benchmark datasets. Our method outperforms state-of-art methods trained for the specific contexts (ZSL, FSL, DA).
Cell-Free and Yeast-Based Production of the Malarial Lactate Transporter, PfFNT, Delivers Comparable Yield and Protein Quality

Cell-free protein production is an attractive alternative to cell-based expression. Rapid results, small-volume reactions, irrelevance of protein toxicity, flexibility, and openness of the system are strong points in favor of the cell-free system. However, the in vitro situation lacks the cellular quality control machinery comprising e.g., the translocon for inserting membrane proteins into lipid bilayers, and chaperon-assisted protein degradation pathways. Here, we compare yield and protein quality of the lactate transporter, PfFNT, from malaria parasites when produced in Pichia pastoris yeast, or in an Escherichia coli S30-extract-based cell-free system. Besides solubilization and correct folding, PfFNT requires oligomerization into homopentamers. We assessed PfFNT folding/oligomerization and function by transmission electron microscopy imaging, transport assays, and binding of small-molecule inhibitors. For the latter, we used chromatography of the PfFNT-inhibitor complex with dual-wavelength detection, and biolayer interferometry. Our data show, that PfFNT possesses an intrinsic capability for assuming the correct fold, oligomerization pattern, and functionality during in vitro translation. This competence depended on the detergent present in the cell-free reaction. The choice of detergent further affected purification and inhibitor binding. In conclusion, in the presence of a suitable detergent, cell-free systems are very well capable of producing high quality membrane proteins.
A Pontryagin Maximum Principle in Wasserstein spaces for constrained optimal control problems

In this paper, we prove a Pontryagin Maximum Principle for constrained optimal control problems in the Wasserstein space of probability measures. The dynamics is described by a transport equation with non-local velocities which are affine in the control, and is subject to end-point and running state constraints. Building on our previous work, we combine the classical method of needle-variations from geometric control theory and the metric differential structure of the Wasserstein spaces to obtain a maximum principle formulated in the so-called Gamkrelidze form.
Local constraints can globally shatter Hilbert space: a new route to quantum information protection

We show how local constraints can globally ‘shatter’ Hilbert space into subsectors, leading to an unexpected dynamics with features reminiscent of both many body localization and quantum scars. A crisp example of this phenomenon is provided by a ‘fractonic circuit’ a model of quantum circuit dynamics in one dimension constrained to conserve both charge and dipole moment. We show how the Hilbert space of the fractonic circuit dynamically fractures into disconnected emergent subsectors within a particular charge and dipole symmetry sector. A large number of the emergent subsectors, exponentially many in the size of the system, have dimension one and exhibit strictly localized quantum dynamics—even in the absence of spatial disorder and in the presence of temporal noise. Exponentially large localized subspaces can be proven to exist for any one dimensional fractonic circuit with finite spatial range, and provide a potentially new route for the robust storage of quantum information. Other emergent subsectors display non-trivial dynamics and may be constructed by embedding finite sized non-trivial blocks into the localized subspace. The shattering of a particular symmetry sector into a distribution of dynamical subsectors with varying sizes leads to the coexistence of high and low entanglement states, reminiscent of quantum scars. We discuss the detailed pattern of fracturing and its implications. We also discuss other mechanisms for similarly shattering Hilbert space.
Guidelines for Experimental Algorithmics in Network Analysis

The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis -- an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines -- including statistical analyses -- for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts.
Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition

Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., person or location) and have equipped language models with an access to external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.
Privacy-Preserving Hierarchical Clustering: Formal Security and Efficient Approximation

Machine Learning (ML) is widely used for predictive tasks in a number of critical applications. Recently, collaborative or federated learning is a new paradigm that enables multiple parties to jointly learn ML models on their combined datasets. Yet, in most application domains, such as healthcare and security analytics, privacy risks limit entities to individually learning local models over the sensitive datasets they own. In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets. First, we provide a formal definition that balances accuracy and privacy, and we present a provably secure protocol along with an optimized version for single linkage clustering. Second, we explore the integration of our protocol with existing approximation algorithms for hierarchical clustering, resulting in a protocol that can efficiently scale to very large datasets. Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results. For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocol requires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09% accuracy.
Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

Soft robots have the potential to significantly change the way that robots interact with the environment and with humans. However, accurately modeling soft robot and soft actuator dynamics in order to perform model-based control can be extremely difficult. Deep neural networks are a powerful tool for modeling systems with complex dynamics such as the pneumatic, continuum joint, six degree-of-freedom robot shown in this paper. Unfortunately it is also difficult to apply standard model-based control techniques using a neural net. In this work, we show that the gradients used within a neural net to relate system states and inputs to outputs can be used to formulate a linearized discrete state space representation of the system. Using the state space representation, model predictive control (MPC) was developed with a six degree of freedom pneumatic robot with compliant plastic joints and rigid links. Using this neural net model, we were able to achieve an average steady state error across all joints of approximately 1 and 2° with and without integral control respectively. We also implemented a first-principles based model for MPC and the learned model performed better in terms of steady state error, rise time, and overshoot. Overall, our results show the potential of combining empirical modeling approaches with model-based control for soft robots and soft actuators.
Functional Implications of RFRP-3 in the Central Control of Daily and Seasonal Rhythms in Reproduction

Adaptation of reproductive activity to environmental changes is essential for breeding success and offspring survival. In mammals, the reproductive system displays regular cycles of activation and inactivation which are synchronized with seasonal and/or daily rhythms in environmental factors, notably light intensity and duration. Thus, most species adapt their breeding activity along the year to ensure that birth and weaning of the offspring occur at a time when resources are optimal. Additionally, female reproductive activity is highest at the beginning of the active phase during the period of full oocyte maturation, in order to improve breeding success. In reproductive physiology, it is therefore fundamental to delineate how geophysical signals are integrated in the hypothalamo-pituitary-gonadal axis, notably by the neurons expressing gonadotropin releasing hormone (GnRH). Several neurochemicals have been reported to regulate GnRH neuronal activity, but recently two hypothalamic neuropeptides belonging to the superfamily of (Arg)(Phe)-amide peptides, RFRP-3 and kisspeptin, have emerged as critical for the integration of environmental cues within the reproductive axis. The goal of this review is to survey the current understanding of the role played by RFRP-3 in the temporal regulation of reproduction, and consider how its effect might combine with that of kisspeptin to improve the synchronization of reproduction to environmental challenges.
Thinkey: A Scalable Blockchain Architecture

This paper presents Thinkey, an efficient, secure, infinitely scalable and decentralized blockchain architecture. It ensures system correctness and liveness by a multi-layer structure. In particular, the system is based on a double-chain architecture and uses a multi-layer consensus protocol to guarantee consistency. Thinkey also uses a novel account model which is based on Actor Model to support the complex logic in the multi-chain structure. Experiment results show that the proposed Thinkey architecture can achieve higher throughput as the number of nodes increases.
NOVEL2GRAPH: Visual Summaries of Narrative Text Enhanced by Machine Learning

A machine learning approach to the creation of visual summaries for narrative text is presented. Standard natural language processing tools for named entities recognition are used together with a clustering al-gorithm to detect the characters of the novel and their aliases. The most relevant ones and their relations are evaluated on the basis of a simple statistical analysis. These characters are visually depicted as nodes of an undirected graph whose edges describe relations with other characters. Specialized sentiment analysis techniques based on sentence embedding decide the colours of characters/nodes and their relations/edges. Additional information about the characters (e.g., gender) and their relations (e.g., siblings or partnerships) are returned by binary classiﬁers and visually depicted in the graph. For those specialized tasks, small amounts of manually annotated data are suﬃcient to achieve good accuracy. Compared to analogous tools, the machine learning approach we present allows for a richer representation of texts of this kind. A case study to demonstrate this approach for a series of books is also reported.
Coping with the stress of practice

Veterinary practice is stressful and always will be. Stress is a natural animal adaptation that allows us to face challenges and survive. The thinking human brain gives us the unique opportunity to convert a physiologic mechanism meant to protect us into a chronic condition that causes us bodily harm. How then do we create coping strategies to stop our propensity for chronic, maladaptive stress? While there is excellent data that proper diet, exercise and sleep habits help, bovine veterinarians may be limited in their ability to employ these tools. Practicing techniques like meditation, gratitude, self-compassion, and story-checking, all of which can be done in the truck between calls, can wake us up to our habits of thought and change the volume and channel on the chatter in our brains. Waking up to our thoughts is the first step to changing how we relate to stress as it arises in the moment and being more resilient in our ability to cope with it.
ATLAS document

A search for direct pair production of top squarks, the supersymmetric partner of the top quark, is presented. The search focuses on final states with one isolated electron or muon, multiple jets, and large missing transverse momentum. The analysis is performed using the Large Hadron Collider Run 2 proton-proton dataset at a centre-of-mass energy of √ s = 13 TeV recorded by the ATLAS detector from 2015 to 2018, corresponding to an integrated luminosity of 139 fb−1. One particular signal scenario is considered, characterised by the mass-splitting between the top squark and the lightest neutralino, where each top squark decays via a 3-body process to a b quark, a W boson, and a neutralino. No significant deviation from the predicted Standard Model background is observed, and limits at 95% confidence level on the supersymmetric benchmark model are set, excluding top squark masses up to 720GeV with neutralino masses up to 580GeV.
Extracting Evidence Summaries from Detective Novels

Literary novels are a mine of information stored in a narrative. Information Extraction (IE) on these novels can be directed by a speciﬁc aim to derive knowledge of interest. In this paper, we explore the problem of extracting evidence summaries for all characters in a detective novel. But in this domain, there is no standard annotated text which provides the ground truth for evidence and there exists no ﬁxed metric of evaluation. For extracting summaries, we explore unsupervised methods of learning. We ﬁrst assume that all characters are equally likely to be culprits and build evidence against each character. Given this evidence, we assess the likelihood of each character being the culprit, eventually arriving at the true culprit(s). Our contribution is the formulation of evidence summaries for characters in determining the culprit. Our experiments are based on a corpus of detective novels spanning the last hundred years. We evaluate these summaries for comprehension with the help of human readers and broadly categorize novels into three groups.
Deaf Cultural Capital and its Conflicts with Hearing Culture: Navigational Successes and Failures

Despite the creation and implementation of laws intended to support and protect Deaf individuals, stories of limited opportunities and oppression within the workplace still exist and are pervasive. Current research in regard to Deaf individuals’ upward mobility includes a discussion of cultural capital, Imposter Syndrome, and navigational capital. To further understand the experiences of Deaf individuals, the research team conducted a mixed-methods study utilizing surveys and interviews. The results provided insight regarding challenges experienced by the participants in either-or-both their education and employment. The data suggests that the use of navigational capital was the most significant predictor for upward mobility.
Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.
Variable importance assessment in sliced inverse regression for variable selection

ABSTRACT We are interested in treating the relationship between a dependent variable y and a multivariate covariate in a semiparametric regression model. Since the purpose of most social, biological, or environmental science research is the explanation, the determination of the importance of the variables is a major concern. It is a way to determine which variables are the most important when predicting y. Sliced inverse regression methods allows to reduce the space of the covariate x by estimating the directions β that form an effective dimension reduction (EDR) space. The aim of this article is to propose a computational method based on importance variable measure (only relying on the EDR space) in order to select the most useful variables. The numerical behavior of this new method, implemented in R, is studied on a simulation study. An illustration on a real data is also provided.