Miracle-dz/UniFAR-FTUs huggingface dataset:
Acetylation Enhances the Tabletting Properties of Starch

ABSTRACT The aim of this study was the evaluation of starch acetate (SA) powders used as tablet excipients. Deformation during powder volume reduction, strain-rate sensitivity, intrinsic elasticity of the materials, and tensile strength of the tablets were examined. Results showed that SA with the lowest degree of substitution (ds) still possessed characteristics of native starch granules. Due to dissolution in synthesis, the properties of higher ds SAs depended on precipitation and drying processes. The acetate moiety, perhaps in combination with existing hydroxyl groups, was a very effective bond-forming substituent. The formation of strong molecular bonds increased, leading to a very firm and intact tablet structure. Small changes existed in compression-induced deformation due to acetylation. Some fragmentation was induced due to the slightly harder and more irregular shape of high-substituted SA particles. The plastic flow under compression was enhanced. Acetylated material was slightly less sensitive to fast elastic recovery in-die, but somewhat more elastic out-of-die. In spite of their superior bonding, SAs under compression behaved similarly to native starches. It was concluded that deformation properties were more the consequence of the molecular chain structure properties of the starch polymer than the effect of the acetate moiety itself. In contrast, the opposite seemed to be the case with the extensive improvement in bond-forming properties.
Intrinsic Multi-scale Evaluation of Generative Models

Generative models are often used to sample high-dimensional data points from a manifold with small intrinsic dimension. Existing techniques for comparing generative models focus on global data properties such as mean and covariance; in that sense, they are extrinsic and uni-scale. We develop the first, to our knowledge, intrinsic and multi-scale method for characterizing and comparing underlying data manifolds, based on comparing all data moments by lower-bounding the spectral notion of the Gromov-Wasserstein distance between manifolds. In a thorough experimental study, we demonstrate that our method effectively evaluates the quality of generative models; further, we showcase its efficacy in discerning the disentanglement process in neural networks.
Computing the Laplacian Spectrum of Linear Octagonal-Quadrilateral Networks and Its Applications

Abstract As a powerful tool for describing and studying the properties of compounds, the graphs spectrum analysis and calculations have attracted substantial attention of the scientific community. Let On denote linear octagonal-quadrilateral networks. In this paper, we investigate that the Laplacian spectrum of On consists of the Laplacian spectrum of and eigenvalues of a symmetric tridiagonal matrix of order As applications of the obtained results, we derive the explicit closed formulas of Kirchhoff index and complexity of On on the basis of the relationship between the coefficients and roots of the characteristic polynomial.
When to reply? Context Sensitive Models to Predict Instructor Interventions in MOOC Forums

Due to time constraints, course instructors often need to selectively participate in student discussion threads, due to their limited bandwidth and lopsided student--instructor ratio on online forums. We propose the first deep learning models for this binary prediction problem. We propose novel attention based models to infer the amount of latent context necessary to predict instructor intervention. Such models also allow themselves to be tuned to instructor's preference to intervene early or late. Our three proposed attentive model variants to infer the latent context improve over the state-of-the-art by a significant, large margin of 11% in F1 and 10% in recall, on average. Further, introspection of attention help us better understand what aspects of a discussion post propagate through the discussion thread that prompts instructor intervention.
RSN: Randomized Subspace Newton

We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces, which is a common setting in applications such as medical imaging, genomics and seismology. Our method leverages randomized sketching in a new way, by finding the Newton direction constrained to the space spanned by a random sketch. We develop a simple global linear convergence theory that holds for practically all sketching techniques, which gives the practitioners the freedom to design custom sketching approaches suitable for particular applications. We perform numerical experiments which demonstrate the efficiency of our method as compared to accelerated gradient descent and the full Newton method. Our method can be seen as a refinement and randomized extension of the results of Karimireddy, Stich, and Jaggi (2019).
Would Gaze-Contingent Rendering Improve Depth Perception in Virtual and Augmented Reality?

Near distances are overestimated in virtual reality, and far distances are underestimated, but an explanation for these distortions remains elusive. One potential concern is that whilst the eye rotates to look at the virtual scene, the virtual cameras remain static. Could using eye-tracking to change the perspective of the virtual cameras as the eye rotates improve depth perception in virtual reality? This paper identifies 14 distinct perspective distortions that could in theory occur from keeping the virtual cameras fixed whilst the eye rotates in the context of near-eye displays. However, the impact of eye movements on the displayed image depends on the optical, rather than physical, distance of the display. Since the optical distance of most head-mounted displays is over 1m, most of these distortions will have only a negligible effect. The exception are ‘gaze-contingent disparities’, which will leave near virtual objects looking displaced from physical objects that are meant to be at the same distance in augmented reality.
Expansions of the p‐adic numbers that interpret the ring of integers

Let Qp∼ be the field of p‐adic numbers in the language of rings. In this paper we consider the theory of Qp∼ expanded by two predicates interpreted by multiplicative subgroups αZ and βZ where α,β∈N are multiplicatively independent. We show that the theory of this structure interprets Peano arithmetic if α and β have positive p‐adic valuation. If either α or β has zero valuation we show that the theory of (Qp∼,αZ,βZ) has the NIP (“negation of the independence property”) and therefore does not interpret Peano arithmetic. In that case we also prove that the theory is decidable if and only if the theory of (Qp∼,αZβZ) is decidable.
Hyperbolic field space and swampland conjecture for DBI scalar

We study a model of two scalar fields with a hyperbolic field space and show that it reduces to a single-field Dirac-Born-Infeld (DBI) model in the limit where the field space becomes infinitely curved. We apply the de Sitter swampland conjecture to the two-field model and take the same limit. It is shown that in the limit, all quantities appearing in the swampland conjecture remain well-defined within the single-field DBI model. Based on a consistency argument, we then speculate that the condition derived in this way can be considered as the de Sitter swampland conjecture for a DBI scalar field by its own. The condition differs from those proposed in the literature and only the one in the present paper passes the consistency argument. As a byproduct, we also point out that one of the inequalities in the swampland conjecture for a multi-field model with linear kinetic terms should involve the lowest mass squared for scalar perturbations and that this quantity can be significantly different from the lowest eigenvalue of the Hessian of the potential in the local orthonormal frame if the field space is highly curved. Finally, we propose an extension of the de Sitter swampland conjecture to a more general scalar field with the Lagrangian of the form P(X,φ), where X=−(∂φ)2/2.
Some remarks on the spectral functions of the Abelian Higgs model

We consider the unitary Abelian Higgs model and investigate its spectral functions at one-loop order. This analysis allows to disentangle what is physical and what is not at the level of the elementary particle propagators, in conjunction with the Nielsen identities. We highlight the role of the tadpole graphs and the gauge choices to get sensible results. We also introduce an Abelian Curci-Ferrari action coupled to a scalar field to model a massive photon which, like the non-Abelian Curci-Ferarri model, is left invariant by a modified non-nilpotent BRST symmetry. We clearly illustrate its non-unitary nature directly from the spectral function viewpoint. This provides a functional analogue of the Ojima observation in the canonical formalism: there are ghost states with nonzero norm in the BRST-invariant states of the Curci-Ferrari model.
Efficient Curvature Estimation for Oriented Point Clouds

There is an immense literature focused on estimating the curvature of an unknown surface from point cloud dataset. Most existing algorithms estimate the curvature indirectly, that is, to estimate the surface locally by some basis functions and then calculate the curvature of such surface as an estimate of the curvature. Recently several methods have been proposed to estimate the curvature directly. However, these algorithms lack of theoretical guarantee on estimation error on small to moderate datasets. In this paper, we propose a direct and efficient method to estimate the curvature for oriented point cloud data without any surface approximation. In fact, we estimate the Weingarten map using a least square method, so that Gaussian curvature, mean curvature and principal curvatures can be obtained automatically from the Weingarten map. We show the convergence rate of our Weingarten Map Estimation (WME) algorithm is $n^{-2/3}$ both theoretically and numerically. Finally, we apply our method to point cloud simplification and surface reconstruction.
Power laws in code repositories: A skeptical approach

Software development as done using modern methodologies and source control management systems, has been often established as an example of self-organization, with code growing and evolving organically, through activities that do not stem from entralized power, leader or directives. The main challenge in proving these claims is that self organization cannot be detected through direct observation, but through measurements on the system, looking for hints such as the existence of power laws over some features, such as the size of changes over time. The problem we intend to tackle in this paper is to establish a methodology for checking, for a chosen set of repositories we had already measured in the past, if the claims about power laws actually hold from a precise mathematical point of view, since, although shown as pervasive in the software engineering literature (and others), power laws are more elusive than they might seem at first sight. For that reason, in this paper we present a statistically accurate set of tests that will help us decide, from the way repositories are changing, if they are really distributed by a power law, which could indicate us the existence of a state reached via self-organization, or actually, how accurately a power law fits the observed distribution of the size of changes of commits in git repositories of 16 open source repositories. We revisit one of the most representative papers of these observations to reevaluate its results and compare them with the current status of the repositories analyzed in it, trying to elucidate if there has been any change in the possible presence, or not, of a power law.
Single secretory granules of live cells recruit syntaxin-1 and synaptosomal associated protein 25 (SNAP-25) in large copy numbers

Before secretory vesicles undergo exocytosis, they must recruit the proteins syntaxin-1 and synaptosomal associated protein 25 (SNAP-25) in the plasma membrane. GFP-labeled versions of both proteins cluster at sites where secretory granules have docked. Single-particle tracking shows that minority populations of both molecules are strongly hindered in their mobility, consistent with their confinement in nanodomains. We measured the fluorescence of granule-associated clusters, the fluorescence of single molecules, and the numbers of unlabeled syntaxin-1 and SNAP-25 molecules per cell. There was a more than 10-fold excess of SNAP-25 over syntaxin-1. Fifty to seventy copies each of syntaxin-1 and SNAP-25 molecules were associated with a single docked granule, many more than have been reported to be required for fusion.
Bid Optimization by Multivariable Control in Display Advertising

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.
Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.
Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames

Pose stability analysis is the key to understanding locomotion and control of body equilibrium, with applications in numerous fields such as kinesiology, medicine, and robotics. In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait. We propose and validate a novel approach to learn CoP from pose of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure heatmaps, and hence the CoP locations, from 2D human pose derived from video. We have collected a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named PressNET. Cross-subject validation results show promising performance of PressNET, significantly outperforming the baseline method of K-Nearest Neighbors. Furthermore, we demonstrate that our computation of center of pressure (CoP) from PressNET is not only significantly more accurate than those obtained from the baseline approach but also meets the expectations of corresponding lab-based measurements of stability studies in kinesiology.
Portable EEG recording system for BCI application

Before few decades disabled persons were not able to perform daily tasks such as turning the light on, making a phone call, and even controlling the TV. Disability was an obstacle therefore disabled individuals needed daily monitoring and service. With the evolution of computation power and the progress in neuronal studies, modern technology and science managed to overcome the human disability for example the ability to walk was restored with the help of fully automated prosthetic legs controlled by brain signals.This work aimed to aid the disabled people that are not able to simply grasp a TV remote control to switch the TV on or browse through the channels; therefore a brain machine interface must be implemented. Low cost portable ElectroEncephaloGraph (EEG) system is designed and tested that allow a person to control the TV through his eye blinks. An overall accuracy of 90% has been obtained in testing 5 TV control events. The total price of the prototype did not exceed the 60 USD.
DRUG DELIVERY PRODUCTS

The new APV course, which will be held in Braunschweig on November 29-30, will be chaired by Dr. Oskar Kalb and Dr. Carsten Timpe from Hoffmann-La Roche AG, Basel, Switzerland. In addition to nanotechnology aspects (nanomilling, screening tools for stable nanosuspensions and downstream processing), it will focus on the parenteral drug delivery of poorly soluble drugs and also on other new technologies and innovative analytical approaches to prepare and characterize these systems in order to improve the biopharmaceutical properties of challenging new compounds. One course highlight will be a visit to the innovative nanomilling facility at the Institute for Particle Technology (Technical University Braunschweig, Prof. Arno Kwade). Details
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasingly multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.
Engineering Kernelization for Maximum Cut

Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kernelization algorithms are theoretically highly efficient for various parameterizations. However, the efficacy of these reduction rules in practice---to aid solving highly challenging benchmark instances to optimality---remains entirely unexplored. We engineer a new suite of efficient data reduction rules that subsume most of the previously published rules, and demonstrate their significant impact on benchmark data sets, including synthetic instances, and data sets from the VLSI and image segmentation application domains. Our experiments reveal that current state-of-the-art solvers can be sped up by up to multiple orders of magnitude when combined with our data reduction rules. On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers; three of these instances are now solved in less than two seconds.
The observability radius of network systems

This paper introduces the observability radius of network systems, which measures the robustness of a network to perturbations of the edges. We consider linear networks, where the dynamics are described by a weighted adjacency matrix, and dedicated sensors are positioned at a subset of nodes. We allow for perturbations of certain edge weights, with the objective of preventing observability of some modes of the network dynamics. Our work considers perturbations with a desired sparsity structure, thus extending the classic literature on the controllability and observability radius of linear systems. We propose an optimization framework to determine a perturbation with smallest Frobenius norm that renders a desired mode unobservable from a given set of sensor nodes. We derive optimality conditions and a heuristic optimization algorithm, which we validate through an example.
Neuropsychological outcomes after subthalamic nucleus deep brain stimulation.

This multi-centre study, from a number of authoritative US deep brain stimulation (DBS) centres, further adds to the literature suggesting that subthalamic nucleus (STN) DBS does not result in neuropsychological disturbance compared to a non-DBS control group, other than for potential effects on some aspects of verbal performance. For example, depression scores were improved in the DBS group over a 12-month follow-up period. Although verbal symptoms resulted in communication dissatisfaction in the DBS group, there was no overall reduction in quality of life. A strength of this study is the detailed neuropsychological assessment protocol used, suggesting that the results are likely to be robust.
Validation of Self-Image of Aging Scale for Chinese Elders

Researchers are increasingly interested in the “image of aging” concept. Models on the image of aging abound, but few have rigorously tested measures that are culturally sensitive and domain-specific. This study first translates Levy et al.‘s (2004) Image of Aging Scale into the Chinese language and revises it into the Chinese Version of the Self-Image of Aging Scale (SIAS-C). Based on the results of a survey of 445 elderly people in Wuhan-China, it then reports the factorial structure of SIAS-C and some of its psychometric properties. Confirmatory factor analysis (CFA) supports a conceptually meaningful five-factor model, as suggested in an exploratory factor analysis (EFA). The 14-item SIAS-C vindicates an acceptable level of internal consistency and test-retest reliability. Its criteria-referenced validity is demonstrated by its correlation with several criteria in expected directions. In conclusion, the SIAS-C is a psychometrically sound instrument which is recommended for use among Chinese older people.