Miracle-dz/UniFAR-FTUs huggingface dataset:
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances – so-called “potentials of mean force” (PMFs) – have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state – a necessary component of these potentials – is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities “reference ratio distributions” deriving from the application of the “reference ratio method. ” This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.
The Physical-Virtual Patient Simulator

Supplemental digital content is available in the text. Introduction We introduce a new type of patient simulator referred to as the Physical-Virtual Patient Simulator (PVPS). The PVPS combines the tangible characteristics of a human-shaped physical form with the flexibility and richness of a virtual patient. The PVPS can exhibit a range of multisensory cues, including visual cues (eg, capillary refill, facial expressions, appearance changes), auditory cues (eg, verbal responses, heart sounds), and tactile cues (eg, localized temperature, pulse). Methods We describe the implementation of the technology, technical testing with healthcare experts, and an institutional review board–approved pilot experiment involving 22 nurse practitioner students interacting with a simulated child in 2 scenarios: sepsis and child abuse. The nurse practitioners were asked qualitative questions about ease of use and the cues they noticed. Results Participants found it easy to interact with the PVPS and had mixed but encouraging responses regarding realism. In the sepsis scenario, participants reported the following cues leading to their diagnoses: temperature, voice, mottled skin, attitude and facial expressions, breathing and cough, vitals and oxygen saturation, and appearance of the mouth and tongue. For the child abuse scenario, they reported the skin appearance on the arms and abdomen, perceived attitude, facial expressions, and inconsistent stories. Conclusions We are encouraged by the initial results and user feedback regarding the perceived realism of visual (eg, mottling), audio (eg, breathing sounds), and tactile (eg, temperature) cues displayed by the PVPS, and ease of interaction with the simulator.
Carnival: a modular framework for automated facial animation

Facial animation is difficult to do convincingly. The movements of the face are complex and subtle, and we are innately attuned to faces. It is particularly difficult and labor-intensive to accurately synchronize faces with speech. A technology-based solution to this problem is automated facial animation. There are various ways to automate facial animation, each of which drives a face from some input sequence. In performance-driven animation, the input sequence may be either facial motion capture or video of a face. In automatic lip-syncing, the input is audio (and possibly a text transcript), resulting in facial animation synchronized with that audio. In audio-visual text-to-speech synthesis (AVTTS), only text is input, and synchronous auditory and visual speech are synthesized.
Cobi: a community-informed conference scheduling tool

Effectively planning a large multi-track conference requires an understanding of the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of scheduling the program falls on a few dedicated organizers. Resolving conflicts becomes difficult due to the size and complexity of the schedule and the lack of insight into community members' needs and desires. Cobi presents an alternative approach to conference scheduling that engages the entire community in the planning process. Cobi comprises (a) communitysourcing applications that collect preferences, constraints, and affinity data from community members, and (b) a visual scheduling interface that combines communitysourced data and constraint-solving to enable organizers to make informed improvements to the schedule. This paper describes Cobi's scheduling tool and reports on a live deployment for planning CHI 2013, where organizers considered input from 645 authors and resolved 168 scheduling conflicts. Results show the value of integrating community input with an intelligent user interface to solve complex planning tasks.
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers

We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or in short as IbyL, relies on a multi-scale pruning scheme that progressively reduces the solution space by use of a coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with classic computer vision related MRF problems, where our novel framework constantly yields a significant time speed-up (with respect to the most efficient inference methods) and obtains a more accurate solution than directly optimizing the MRF. We make our code available on-line [4].
Knowledge Graphs for Processing Scientific Data: Challenges and Prospects

There is growing interest in the use of Knowledge Graphs (KGs) for the representation, exchange, and reuse of scientific data. While KGs offer the prospect of improving the infrastructure for working with scalable and reusable scholarly data consistent with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, the state-of-the-art Data Management Systems (DMSs) for processing large KGs leave somewhat to be desired. In this paper, we studied the performance of some of the major DMSs in the context of querying KGs with the goal of providing a finely-grained, comparative analysis of DMSs representing each of the four major DMS types. We experimented with four well-known scientific KGs, namely, Allie, Cellcycle, DrugBank, and LinkedSPL against Virtuoso, Blazegraph, RDF-3X, and MongoDB as the representative DMSs. Our results suggest that the DMSs display limitations in processing complex queries on the KG datasets. Depending on the query type, the performance differentials can be several orders of magnitude. Also, no single DMS appears to offer consistently superior performance. We present an analysis of the underlying issues and outline two integrated approaches and proposals for resolving the problem.
Embedded Large–Scale Handwritten Chinese Character Recognition

As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices. To achieve acceptable accuracy, we paid particular attention to data collection conditions, representativeness of writing styles, and training regimen. We found that, with proper care, even larger inventories are within reach. Our experiments show that accuracy only degrades slowly as the inventory increases, as long as we use training data of sufficient quality and in sufficient quantity.
Reports of Own and Others' Symptoms and Diagnosis on Social Media Predict COVID-19 Case Counts in Mainland China

Can public social media data be harnessed to predict COVID-19 case counts? We analyzed more than 12 million COVID-19 related posts on Weibo, a popular Twitter-like social media platform in China, from November 20, 2019 to March 3, 2020. We developed a machine learning classifier to identify “sick posts,” which are reports of one’s own and other people’s symptoms and diagnosis related to COVID-19. We then modeled the predictive power of sick posts and other COVID-19 posts on daily case counts. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to seven days ahead of official statistics. But other COVID-19 posts did not have similar predictive power. For a subset of geotagged posts (2.85% of all retrieved posts), we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, it is crucial to sift through the contents and efficiently identify true signals from noise.
PhICNet: Physics-Incorporated Convolutional Recurrent Neural Networks for Modeling Dynamical Systems

Dynamical systems involving partial differential equations (PDEs) and ordinary differential equations (ODEs) arise in many ﬁelds of science and engineering. In this paper, we present a physics-incorporated deep learning framework to model and predict the spatiotemporal evolution of dynamical systems governed by partially-known in-homogenous PDEs with unobservable source dynamics. We formulate our model PhICNet as a convolutional recurrent neural network which is end-to-end trainable for spatiotemporal evolution prediction of dynamical systems. Experimental results show the long-term prediction capability of our model.
Learning from Aggregate Observations

We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals. A well-known example is multiple instance learning (MIL). In this paper, we extend MIL beyond binary classification to other problems such as multiclass classification and regression. We present a probabilistic framework that is applicable to a variety of aggregate observations, e.g., pairwise similarity for classification and mean/difference/rank observation for regression. We propose a simple yet effective method based on the maximum likelihood principle, which can be simply implemented for various differentiable models such as deep neural networks and gradient boosting machines. Experiments on three novel problem settings -- classification via triplet comparison and regression via mean/rank observation indicate the effectiveness of the proposed method.
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders

State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. In this paper, we propose an alternative approach that is based on language-specific encoder-decoders, and can thus be more easily extended to new languages by learning their corresponding modules. So as to encourage a common interlingua representation, we simultaneously train the N initial languages. Our experiments show that the proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average, while allowing to add new languages without the need to retrain the rest of the modules. All in all, our work closes the gap between shared and language-specific encoderdecoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.
Understanding What Software Engineers Are Working on The Work-Item Prediction Challenge

Understanding what a software engineer (a developer, an incident responder, a production engineer, etc.) is working on is a challenging problem - especially when considering the more complex software engineering workflows in software-intensive organizations: i) engineers rely on a multitude (perhaps hundreds) of loosely integrated tools; ii) engineers engage in concurrent and relatively long running workflows; ii) infrastructure (such as logging) is not fully aware of work items; iv) engineering processes (e.g., for incident response) are not explicitly modeled. In this paper, we explain the corresponding’ work-item prediction challenge’ on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning.
Relativistic X-Ray Jets from the Black Hole X-Ray Binary MAXI J1820+070

The black hole MAXI J1820+070 was discovered during its 2018 outburst and was extensively monitored across the electromagnetic spectrum. Following the detection of relativistic radio jets, we obtained four Chandra X-ray observations taken between 2018 November and 2019 June, along with radio observations conducted with the Very Large Array and MeerKAT arrays. We report the discovery of X-ray sources associated with the radio jets moving at relativistic velocities with a possible deceleration at late times. The broadband spectra of the jets are consistent with synchrotron radiation from particles accelerated up to very high energies (>10 TeV) by shocks produced by the jets interacting with the interstellar medium. The minimal internal energy estimated from the X-ray observations for the jets is ∼1041 erg, significantly larger than the energy calculated from the radio flare alone, suggesting most of the energy is possibly not radiated at small scales but released through late-time interactions.
Iguratimod, A Synthetic Disease Modifying Anti- Rheumatic Drug (Sdmard), and Various Dmards Suppress Joint Destruction. The Pathophysiological Mechanisms of the Inhibition of Bone/Cartilage Destruction

Objective: To elucidate the radiographic outcomes for rheumatoid arthritis (RA) patients using the synthetic disease-modifying antirheumatic drug (sDMARD) Iguratimod (IGU) and other DMARDs including injectable sodium aurothiomalate, bucillamine, salazosulphapyridine, infliximab, etanercept, tocilizumab and/or abatacept.Patients and Methods: 213 patients were enrolled in this study. Total Genant-modified Sharp scores (GSS) of hands/wrists and feet at baseline and at week 104 were calculated in 31 RA patients treated with a daily dose of 25 mg or 50 mg for 104 weeks.Results: Total GSS of 31 patients at week 104 showed no progression (total GSS <= 0.84: the smallest detectable change) in 16 (52%) patients with a mean score reduction (95% CI) of-4.3 (-8.1 ~ -0.5) (p < 0.05). Conclusion: Treatment with the sDMARD, IGU showed no radiographic progression in 16 (52%) RA patients at week 104. Concerning the suppression mechanism of joint destruction by IGU and other DMARDs, we speculate that DMARDs prevent bone/cartilage destruction by inhibiting the receptor activator of nuclear factor-kappa B (NF- kB) lig and (RANKL) and through other antirheumatic actions.
Extensions of Random Orthogonal Matrix Simulation for Targetting Kollo Skewness

Modelling multivariate systems is important for many applications in engineering and operational research. The multivariate distributions under scrutiny usually have no analytic or closed form. Therefore their modelling employs a numerical technique, typically multivariate simulations, which can have very high dimensions. Random Orthogonal Matrix (ROM) simulation is a method that has gained some popularity because of the absence of certain simulation errors. Specifically, it exactly matches a target mean, covariance matrix and certain higher moments with every simulation. This paper extends the ROM simulation algorithm presented by Hanke et al. (2017), hereafter referred to as HPSW, which matches the target mean, covariance matrix and Kollo skewness vector exactly. Our first contribution is to establish necessary and sufficient conditions for the HPSW algorithm to work. Our second contribution is to develop a general approach for constructing admissible values in the HPSW. Our third theoretical contribution is to analyse the effect of multivariate sample concatenation on the target Kollo skewness. Finally, we illustrate the extensions we develop here using a simulation study.
On the cone restriction conjecture in four dimensions and applications in incidence geometry

The first purpose of this paper is to solve the cone restriction conjecture in four dimensions in the finite field setting. We will also clarify conjectures for higher dimensional cases. The second is to introduce a new approach to study incidence problems via restriction theory. More precisely, using the cone restriction estimates, we will prove sharp point-sphere incidence bounds associated with complex-valued functions for sphere sets of small size.
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016

A brain–computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications—motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks—are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.
Analysis of the Cell Cycle Regulated Golgi Dynamics in Mammalian Cells.

In vitro assays identified the Golgi peripheral protein GRASP65 as a Golgi stacking factor that links adjacent Golgi cisternae by forming mitotically regulated trans-oligomers. These conclusions, however, require further confirmation in the cell. In this study, we demonstrated that the first 112 amino acids at the N-terminus (including the first PDZ domain, PDZ1) of the protein are sufficient for oligomerization. Systematic electron microscopic analysis showed that expression of non-regulatable GRASP65 mutants in HeLa cells enhanced Golgi stacking in interphase and inhibited Golgi fragmentation during mitosis. Depletion of GRASP65 by small interference RNA (siRNA) reduced the number of cisternae in the Golgi stacks; this reduction was rescued by expressing exogenous GRASP65. Further experiments revealed that inhibition of mitotic Golgi disassembly by expressing non-regulatable GRASP65 mutants did not affect equal partitioning of the Golgi membranes into the daughter cells. However, it delayed mitotic entry and suppressed cell growth; this effect was diminished by dispersing the Golgi apparatus with Brefeldin A treatment prior to mitosis, suggesting that Golgi disassembly at the onset of mitosis plays a role in cell cycle progression.
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible but malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples. In this paper we propose a novel probabilistic framework to generalize and extend adversarial attacks in order to produce a desired probability distribution for the classes when we apply the attack method to a large number of inputs. This novel attack strategy provides the attacker with greater control over the target model, and increases the complexity of detecting that the model is being attacked. We introduce three different strategies to efficiently generate such attacks, and illustrate our approach extending DeepFool, a state-of-the-art attack algorithm to generate adversarial examples. We also experimentally validate our approach for the spoken command classification task, an exemplary machine learning problem in the audio domain. Our results demonstrate that we can closely approximate any probability distribution for the classes while maintaining a high fooling rate and by injecting imperceptible perturbations to the inputs.
Inverse identification of dynamically important regions in turbulent flows using three-dimensional convolutional neural networks

Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall. These extreme events play a central role in regulating the energy budget of the boundary layer, and are analyzed here with the help of a three-dimensional (3D) Convolutional Neural Network (CNN). A CNN is trained on Direct Numerical Simulation data from a periodic channel flow to deduce the intensity of such extreme events, and more importantly, to reveal contiguous three-dimensional salient structures in the flow that are determined autonomously by the network to be critical to the formation and evolution of ejection events. These salient regions, reconstructed using a multilayer Gradient-weighted Class Activation Mapping (GradCAM) technique proposed here, correlate well with bursting streaks and coherent fluid packets being ejected away from the wall. The focus on explainable interpretation of the network's learned associations also reveals that ejections are not associated with regions where turbulent kinetic energy (TKE) production reaches a maximum, but instead with regions that entail extremely low dissipation and a significantly higher tendency for positive TKE production than negative production. This is a key finding of the study, and indicates that CNNs can help reveal dynamically important three-dimensional salient regions using a single scalar-valued metric provided as the quantity of interest, which in the present case is the ejection intensity. While the current work presents an alternate means of analyzing nonlinear spatial correlations associated with near-wall bursts, the framework presented is sufficiently general so as to be extendable to other scenarios where the underlying spatial dynamics are not known a-priori.