Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Bandwidth Scanning of Sample Properties in Atomic Force Microscopy

This paper exemplifies methods to estimate sample properties, including topographical properties, from a high bandwidth estimate of tip-sample interaction forces between the probe tip and the sample surface in an atomic force microscope. The tipsample interaction force is the most fundamental quantity that can be detected by the probe tip. The fact that sample features as well as physical properties of the sample are a function of tipsample interaction model chosen is exploited, and the property estimates are obtained by fitting appropriate physical models to the force estimate data. The underlying idea is to treat the nonlinear tip-sample interactions as a disturbance to the cantilever subsystem and design a feedback controller that ensures the cantilever deflection tracks a desired trajectory. This tracking allows scanning speeds as high as 1 10 th of the cantilever resonance frequency compared to typical scanning modes that regulate derivatives of the probe deflection such as amplitude or phase, providing much lower scan speeds. The high bandwidth disturbance rejection and consequent estimation provides estimates of the tipsample interaction force.
Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using LSTM

Interpreting noun compounds is a challenging task. It involves uncovering the underlying predicate which is dropped in the formation of the compound. In most cases, this predicate is of the form VERB+PREP. It has been observed that uncovering the preposition is a significant step towards uncovering the predicate. In this paper, we attempt to paraphrase noun compounds using prepositions. We consider noun compounds and their corresponding prepositional paraphrases as parallelly aligned sequences of words. This enables us to adapt different architectures from cross-lingual embedding literature. We choose the architecture where we create representations of both noun compound (source sequence) and its corresponding prepositional paraphrase (target sequence), such that their sim- ilarity is high. We use LSTMs to learn these representations. We use these representations to decide the correct preposition. Our experiments show that this approach performs considerably well on different datasets of noun compounds that are manually annotated with prepositions.
Aerobic and anaerobic microbial degradation of weathered and fresh oil from the BP Deepwater Horizon Oil Spill

This study compared the bacterial degradation rates of two varieties of Macondo Oil from the BP Deepwater Horizon well under both aerobic and anaerobic conditions. The oil types used were fresh oil directly from the drilling well and weathered oil collected from the shore after the 2010 well blowout in the Gulf of Mexico. I set up 120 mL serum bottles, which incubated for a total of 20 days and were sampled at 4,10, and 20 days. The anaerobic conditions were enhanced with nitrate and iron as electron acceptors to aid the degradation process, while the aerobic treatments were maintained with only oxygen as the electron acceptor. I analyzed the amount of oil degradation by: bacterial productivity with 14 C-Leucine, CO2 respiration with gas chromatography, and ammonium uptake levels with colorimetric analyses on a spectrophotometer. Through these analyses I found highest oil degradation with oxygen, followed by nitrate and iron. I found higher rates of bacterial degradation in the weathered oil treatment under aerobic conditions. Alternatively under anaerobic conditions the bacteria more successfully degraded new oil.
Formulation and Evaluation of Immediate Release Telmisartan Tablets using Hydrophilic Polymers

Aim: Telmisartan is an anti-hypertensive drug under the class of angiotensin II receptor blocker. The aim is to formulate and evaluated immediate release telmisartan tablets using hydrophilic polymers. Materials and Method: The hydrophilic polymers are polyethylene glycol 4000 (PEG-4000) and PEG-6000. Solid dispersion is prepared with polymers with different ratios 1:0.5, 1:1, 1:2 and formulated into tablets. Formulations were characterized for drug content studies, drug release studies, and drug-polymer interactions using Fourier transform infrared spectroscopy (FTIR) spectrum. Results and Discussions: Formulation containing 1:2 ratio of drug: PEG-6000 showed the best release with a cumulative release of 99.67% as compared to 74.36% for the PEG-4000. The FTIR studies showed that there is no interaction between the drug and polymer. Conclusion: Based evaluation of different parameters it was concluded that formulation of immediate release tablets of telmisartan was successfully done and F6 shows 100% at 60 min.
A Study of Virtual Energy Storage From Thermostatically Controlled Loads Under Time-Varying Weather Conditions

We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically controlled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. A key constraint is to ensure that consumers' quality of service (QoS) is maintained. Our proposal involves replacing the thermostats at the loads by a randomized controller, following recent proposals in this direction. The new local controller has a tunable parameter that serves as the control command from the balancing authority (BA). Compared to prior work in this area, our proposed architecture can handle large disturbances from the outside temperature. Weather-induced disturbance also imposes an algorithm-independent limit on the capacity of the virtual energy storage the loads can provide. This key limitation, which was ignored in prior work, is incorporated in our formulation in a principled manner.
Phase Field Model of Hydraulic Fracturing in Poroelastic Media: Fracture Propagation, Arrest, and Branching Under Fluid Injection and Extraction

The simulation of fluid‐driven fracture propagation in a porous medium is a major computational challenge, with applications in geosciences and engineering. The two main families of modeling approaches are those models that represent fractures as explicit discontinuities and solve the moving boundary problem and those that represent fractures as thin damaged zones, solving a continuum problem throughout. The latter family includes the so‐called phase field models. Continuum approaches to fracture face validation and verification challenges, in particular grid convergence, well posedness, and physical relevance in practical scenarios. Here we propose a new quasi‐static phase field formulation. The approach fully couples fluid flow in the fracture with deformation and flow in the porous medium, discretizes flow in the fracture on a lower‐dimension manifold, and employs the fluid flux between the fracture and the porous solid as coupling variable. We present a numerical assessment of the model by studying the propagation of a fracture in the quarter five‐spot configuration. We study the interplay between injection flow rate and rock properties and elucidate fracture propagation patterns under the leak‐off toughness dominated regime as a function of injection rate, initial fracture length, and poromechanical properties. For the considered injection scenario, we show that the final fracture length depends on the injection rate, and three distinct patterns are observed. We also rationalize the system response using dimensional analysis to collapse the model results. Finally, we propose some simplifications that alleviate the computational cost of the simulations without significant loss of accuracy.
Highly Sensitive Metabolite Biosensor Based on Organic Electrochemical Transistor Integrated with Microfluidic Channel and Poly(N‐vinyl‐2‐pyrrolidone)‐Capped Platinum Nanoparticles

Organic electrochemical transistors (OECTs) are used as highly sensitive glucose and lactate sensors by modifying the gate electrode with glucose oxidase/lactate oxidase and poly(n‐vinyl‐2‐pyrrolidone)‐capped platinum nanoparticles (Pt NPs). The Pt NPs are deposited by using a two‐step dip coating method without bias instead of the conventional electrodeposition method and followed by an UV‐Ozone post treatment to enhance the catalytic ability of the Pt NPs. The modified OECT sensors have extremely high sensitivity, and can achieve a detection limit of glucose and lactate down to 10−7 and 10−6m, respectively. A polydimethylsiloxane microfluidic channel is successfully integrated with the OECT sensors, which provides a compact chip size of the sensors, a short detection time of around 1 min and extremely low consumption of analyte (30 μ L). The cross talk between individual sensors in multianalyte sensing devices is also reduced by the dual microfluidic channel structure. Practical applications, such as for detecting glucose in saliva, can therefore be realized, and a prototype of a portable glucose sensor has been successfully created in this study. This portable glucose sensor has excellent potential for real‐time and noninvasive glucose sensing applications.
Modeling Hypertrophic Cardiomyopathy with Human Induced Pluripotent Stem Cells

Research of genetic cardiovascular diseases has lacked of good disease models because rodents, which are primarily used, differ greatly from humans. The ability to derive human induced pluripotent stem cells (hiPSCs) from patients carrying inherited cardiac diseases has revolutionized research in the cardiovascular field. The aim for this chapter is to review the current hiPSC reprogramming methods and methods for differentiating human pluripotent stem cells (hPSCs) into cardiomyocytes. The chapter focuses on the published hiPSC models for hypertrophic cardiomyopathy (HCM) and discusses the challenges related to modeling this interesting disease using hiPSC technology.
Title Effects of progressive backward body weight supported treadmill training on gait ability in chronic stroke patients: A randomized controlled trial

BACKGROUND AND OBJETIVE: The Stroke patient with hemiplegic gait is generally described as being slow and asymmetric. Body weight-supported treadmill training and backward gait training are recent additions to therapeutic gait trainings and may help improve gait ability in stroke patient with hemiplegic gait. Therefore, the examined to effect on gait ability using progressively backward body weight-supported treadmill training in chronic stroke patients with hemiplegic gait. Thirty subjects were divided to experimental group and control group. METHODS: The experimental group of 15 patients underwent progressively backward body weight-supported treadmill training and control group of 15 patients underwent general treadmill gait training on five times per week, for a total of four weeks. The OptoGait was used to analyze gait kinematics, and the dynamic gait index (DGI) and results of the 6-minute walk test were used as the clinical evaluation indicators, and a follow-up test was carried out four weeks later to examine persistence of the exercise effects. RESULTS: The experimental group showed statistically significant results in all dependent variables until the 4 weeks compared to the control group. However, until the eighth week, only the dependent variables of affected step length (ASL), stride length (SL), and DGI differed significantly between the two groups. CONCLUSION: This study verified that progressively bodyweight-supported treadmill training had a positive influence on the temporospatial characteristics of gait and the clinical gait evaluation index in chronic stroke patients.
Rampart: Protecting Web Applications from CPU-Exhaustion Denial-of-Service Attacks

Denial-of-Service (DoS) attacks pose a severe threat to the availability of web applications. Traditionally, attackers have employed botnets or amplification techniques to send a significant amount of requests to exhaust a target web server's resources, and, consequently, prevent it from responding to legitimate requests. However, more recently, highly sophisticated DoS attacks have emerged, in which a single, carefully crafted request results in significant resource consumption and ties up a web application's back-end components for a non-negligible amount of time. Unfortunately, these attacks require only few requests to overwhelm an application, which makes them difficult to detect by state-of-the-art detection systems. In this paper, we present RAMPART, which is a defense that protects web applications from sophisticated CPU-exhaustion DoS attacks. RAMPART detects and stops sophisticated CPU-exhaustion DoS attacks using statistical methods and function-level program profiling. Furthermore, it synthesizes and deploys filters to block subsequent attacks, and it adaptively updates them to minimize any potentially negative impact on legitimate users. We implemented RAMPART as an extension to the PHP Zend engine. RAMPART has negligible performance overhead and it can be deployed for any PHP application without having to modify the application's source code. To evaluate RAMPART'S effectiveness and efficiency, we demonstrate that it protects two of the most popular web applications, WordPress and Drupal, from real-world and synthetic CPU-exhaustion DoS attacks, and we also show that RAMPART preserves web server performance with low false positive rate and low false negative rate.
Effectiveness of Voice Quality Features in Detecting Depression

Automatic assessment of depression from speech signals is affected by variabilities in acoustic content and speakers. In this study, we focused on addressing these variabilities. We used a database comprised of recordings of interviews from a large number of female speakers: 735 individuals suffering from depressive (dysthymia and major depression) and anxiety disorders (generalized anxiety disorder, panic disorder with or without agoraphobia) and 953 healthy individuals. Leveraging this unique and extensive database, we built an i-vector framework. In order to capture various aspects of speech signals, we used voice quality features in addition to conventional cepstral features. The features (F0, F1, F2, F3, H1-H2, H2-H4, H4-H2k, A1, A2, A3, and CPP) were inspired by a psychoacoustic model of voice quality [1]. An i-vector-based system using Mel Frequency Cepstral Coefﬁcients (MFCCs) and another using voice quality features was developed. Voice quality features performed as well as MFCCs. A score-level fusion was then used to combine these two systems, resulting in a 6% relative improvement in accuracy in comparison with the i-vector system based on MFCCs alone. The system was robust even when the duration of the utterances was shortened to 10 seconds.
In situ vertical profiles of aerosol extinction, mass, and composition over the southeast United States during SENEX and SEAC 4 RS: observations of a modest aerosol enhancement aloft

Abstract. Vertical profiles of submicron aerosol from in situ aircraft-based measurements were used to construct aggregate profiles of chemical, microphysical, and optical properties. These vertical profiles were collected over the southeastern United States (SEUS) during the summer of 2013 as part of two separate field studies: the Southeast Nexus (SENEX) study and the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS). Shallow cumulus convection was observed during many profiles. These conditions enhance vertical transport of trace gases and aerosol and create a cloudy transition layer on top of the sub-cloud mixed layer. The trace gas and aerosol concentrations in the transition layer were modeled as a mixture with contributions from the mixed layer below and the free troposphere above. The amount of vertical mixing, or entrainment of air from the free troposphere, was quantified using the observed mixing ratio of carbon monoxide (CO). Although the median aerosol mass, extinction, and volume decreased with altitude in the transition layer, they were ~10 % larger than expected from vertical mixing alone. This enhancement was likely due to secondary aerosol formation in the transition layer. Although the transition layer enhancements of the particulate sulfate and organic aerosol (OA) were both similar in magnitude, only the enhancement of sulfate was statistically significant. The column integrated extinction, or aerosol optical depth (AOD), was calculated for each individual profile, and the transition layer enhancement of extinction typically contributed less than 10 % to the total AOD. Our measurements and analysis were motivated by two recent studies that have hypothesized an enhanced layer of secondary aerosol aloft to explain the summertime enhancement of AOD (2–3 times greater than winter) over the southeastern United States. The first study attributes the layer aloft to secondary organic aerosol (SOA) while the second study speculates that the layer aloft could be SOA or secondary particulate sulfate. In contrast to these hypotheses, the modest enhancement we observed in the transition layer was not dominated by OA and was not a large fraction of the summertime AOD.
Self-Protection of Android Systems from Inter-component Communication Attacks

The current security mechanisms for Android apps, both static and dynamic analysis approaches, are insufficient for detection and prevention of the increasingly dynamic and sophisticated security attacks. Static analysis approaches suffer from false positives whereas dynamic analysis approaches suffer from false negatives. Moreover, they all lack the ability to efficiently analyze systems with incremental changes–such as adding/removing apps, granting/revoking permissions, and dynamic components' communications. Each time the system changes, the entire analysis needs to be repeated, making the existing approaches inefficient for practical use. To mitigate their shortcomings, we have developed SALMA, a novel self-protecting Android software system that monitors itself and adapts its behavior at runtime to prevent a wide-range of security risks. SALMA maintains a precise architectural model, represented as a Multiple-Domain-Matrix, and incrementally and efficiently analyzes an Android system in response to incremental system changes. The maintained architecture is used to reason about the running Android system. Every time the system changes, SALMA determines (1) the impacted part of the system, and (2) the subset of the security analyses that need to be performed, thereby greatly improving the performance of the approach. Our experimental results on hundreds of real-world apps corroborate SALMA's scalability and efficiency as well as its ability to detect and prevent security attacks at runtime with minimal disruption.
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation

The performance of autonomous systems heavily relies on their ability to generate a robust representation of the environment. Deep neural networks have greatly improved vision-based perception systems but still fail in challenging situations, e.g. sensor outages or heavy weather. These failures are often introduced by data-inherent perturbations, which significantly reduce the information provided to the perception system. We propose a functionally modularized temporal filter, which stabilizes an abstract feature representation of a single-frame segmentation model using information of previous time steps. Our filter module splits the filter task into multiple less complex and more interpretable subtasks. The basic structure of the filter is inspired by a Bayes estimator consisting of a prediction and an update step. To make the prediction more transparent, we implement it using a geometric projection and estimate its parameters. This additionally enables the decomposition of the filter task into static representation filtering and low-dimensional motion filtering. Our model can cope with missing frames and is trainable in an end-to-end fashion. Using photorealistic, synthetic video data, we show the ability of the proposed architecture to overcome data-inherent perturbations. The experiments especially highlight advantages introduced by an interpretable and explicit filter module.
Likelihood Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable

Current estimating equation methods for logistic structural nested mean models (SNMMs) either rely heavily on possible ”uncongenial” modeling assumptions or involve a cumbersome integral equation needing to be solved, for each independent unit, at each step of solving the estimating equation. These drawbacks have impeded widespread use of these methods. In this paper, we present an alternative parametrization of the likelihood function for the logistic SNMM that circumvents computational complexity of existing methods while ensuring a congenial parametrization of SNMM. We also provide a goodnessof-fit (GOF) test statistic for evaluating certain parametric assumptions made by the Email address: rmatsoua@hsph.harvard. edu †Email address: etchetge@hsph.harvard. edu; Corresponding author.
Sparse Recovery From Multiple Measurement Vectors Using Exponentiated Gradient Updates

In this letter, we address the problem of reconstructing the common nonzero support of multiple joint sparse vectors from their noisy and underdetermined linear measurements. The support recovery problem is formulated as the selection of nonnegative hyperparameters of a correlation-aware, joint sparsity inducing Gaussian prior. The hyperparameters are recovered as a nonnegative sparse solution of covariance-matching constraints formulated in the observation space by solving a sequence of proximal regularized convex optimization problems. For proximal regularization based on Von Neumann Bregman matrix divergence, an exponentiated gradient (EG) update is proposed, which when applied iteratively, converges to hyperparameters with the correct sparse support. Compared to existing multiple measurement vector support recovery algorithms, the proposed multiplicative EG update has a significantly lower computational and storage complexity and takes fewer iterations to converge. We empirically demonstrate that the support-recovery algorithm based on the proposed EG update can solve million variable support recovery problems in tens of seconds. Additionally, by leveraging its correlation-awareness property, the proposed algorithm can recover supports of size as high as $O(m^2)$ from only $m$ linear measurements per joint sparse vector.
VARIATIONAL ALGORITHMS TO REMOVE STRIPES : A GENERALIZATION OF THE NEGATIVE NORMMODELS

Starting with a book of Y.Meyer in 2001, negative norm models attracted the attention of the imaging community in the last decade. Despite numerous works, these norms seem to have provided only luckwarm results in practical applications. In this work, we propose a framework and an algorithm to remove stationary noise from images. This algorithm has numerous practical applications and we show it on 3D data from a newborn microscope called SPIM. We also show that this model generalizes Meyer’s model and its successors in the discrete setting and allows to interpret them in a Bayesian framework. It sheds a new light on these models and allows to pick them according to some a priori knowledge on the texture statistics. Further results are available at http://www.math.univ-toulouse.fr/ ̃weiss/PagePublications.html.