Miracle-dz/UniFAR-FTUs huggingface dataset:
MICROFLOW CONTROL USING THERMALLY RESPONSIVE TRIBLOCK COPOLYMERS

Reliable microvalves are crucial to flow control in microfluidic devices. While many types of mechanical microvalves require elaborate fabrication processes that make integration into complex systems nontrivial, the valves often suffer from slow actuation, high leakage rates or failure of moving parts. Alternative microvalve concepts based on stimuli responsive polymers have been developed, where for example locally polymerized hydrogels respond to a changes in pH of the working fluid by swelling or shrinking [ 11. Shirasaki et al. [2] demonstrated gel formation of an aqueous solution of a thermosensitive diblock copolymer in a microchannel using external light sources for heating. In a similar flow control strategy, poloxamers can simply be mixed into the working fluid for active and passive microflow management. The solution gels reversibly at elevated temperatures so that a microchannel can be completely and reliably blocked using integrated resistive heaters for localized heating (Fig. 1). Poloxamers are also useful for passive flow control, where viscous heating in high-shear rate flows can lead to automatic gel formation in a microchannel. The simple fabrication process of the flow control structures allows easy integration into a complex device.
Efficient prediction of relational structure and its application to natural language processing

Many tasks in Natural Language Processing (NLP) require us to predict a relational structure over entities. For example, in Semantic Role Labelling we try to predict the ’semantic role’ relation between a predicate verb and its argument constituents. Often NLP tasks not only involve related entities but also relations that are stochastically correlated. For instance, in Semantic Role Labelling the roles of different constituents are correlated: we cannot assign the agent role to one constituent if we have already assigned this role to another. Statistical Relational Learning (also known as First Order Probabilistic Logic) allows us to capture the aforementioned nature of NLP tasks because it is based on the notions of entities, relations and stochastic correlations between relationships. It is therefore often straightforward to formulate an NLP task using a First Order probabilistic language such as Markov Logic. However, the generality of this approach comes at a price: the process of finding the relational structure with highest probability, also known as maximum a posteriori (MAP) inference, is often inefficient, if not intractable. In this work we seek to improve the efficiency of MAP inference for Statistical Relational Learning. We propose a meta-algorithm, namely Cutting Plane Inference (CPI), that iteratively solves small subproblems of the original problem using any existing MAP technique and inspects parts of the problem that are not yet included in the current subproblem but could potentially lead to an improved solution. Our hypothesis is that this algorithm can dramatically improve the efficiency of existing methods while remaining at least as accurate. We frame the algorithm in Markov Logic, a language that combines First Order Logic and Markov Networks. Our hypothesis is evaluated using two tasks: Semantic Role Labelling and Entity Resolution. It is shown that the proposed algorithm improves the efficiency of two existing methods by two orders of magnitude and leads an approximate method to more probable solutions. We also give show that CPI, at convergence, is guaranteed to be at least as accurate as the method used within its inner loop. Another core contribution of this work is a theoretic and empirical analysis of the boundary conditions of Cutting Plane Inference. We describe cases when Cutting Plane Inference will definitely be difficult (because it instantiates large networks or needs many iterations) and when it will be easy (because it instantiates small networks and needs only few iterations).
Information Extraction tasks : a survey

An Information Extraction activity is a complex process that can be decomposed into several tasks. This decomposition brings the following advantages: (i) for each task it becomes possible to choose the best technique independently from the other tasks; (ii) an Information Extraction program can be developed as a set of independent modules (one for each task), making it easy to perform local debugging; (iii) it becomes easy to customize the Information Extraction activity through reordering, selection or even composition the tasks. This paper presents a commonly used decomposition of the Information Extraction activities and gives detail about the most used machine learning and rule-based techniques for each task.
A Nekrasov-Okounkov type formula for affine f C

In 2008, Han rediscovered an expansion of powers of Dedekind η function due to Nekrasov and Okounkov by using Macdonald’s identity in type (cid:101) A . In this paper, we obtain new combinatorial expansions of powers of η , in terms of partition hook lengths, by using Macdonald’s identity in type (cid:101) C and a new bijection. As applications, we derive a symplectic hook formula and a relation between Macdonald’s identities in types (cid:101) C , (cid:101) B , and (cid:103) BC . R´esum´e. En 2008, Han a red´ecouvert un d´eveloppement des puissances de la fonction η de Dedekind, dˆu `a Nekrasov et Okounkov, en utilisant l’identit´e de Macdonald en type (cid:101) A . Dans cet article, nous obtenons un nouveau d´eveloppement combinatoire des puissances de η , en termes de longueurs d’´equerres de partitions, en utilisant l’identit´e de Macdonald en type (cid:101) C ainsi qu’une nouvelle bijection. Plusieurs applications en sont d´eduites, comme un analogue symplectique de la formule des ´equerres, ou une relation entre les identit´es de Macdonald en types (cid:101) C , (cid:101) B et (cid:103) BC .
Attitudinal patterns in Brazilian portuguese intonation: analysis and synthesis

The main goal of this paper is to investigate the p rosodic manifestation of the following attitudinal states: consideration, despair, disappointment, irony, justification, obvi ousness, and uncertainty. The sentence O Carlos Alberto ja sabe. [Carlos Alberto already knows it. ] was pronounced by a subj ect, who tried to convey each of these attitudes. Afterwards , it was presented to 20 panelists, which were asked to iden tify the original intention of each enunciation. The test re sults showed that the attitudes were, in general, correctly iden tified. The acoustic analysis revealed that the attitudinal pat terns make use of distinct prosodic parameters in their manife station: some are linked to segmental duration, be it global or localized; in other cases, the decisive prosodic co mponent is the fundamental frequency. Auditory tests using spe ech resynthesis turned it possible to evaluate the rela tive weight of the prosodic characteristics identified in the anal ysis.
SUMOylation at K340 inhibits tau degradation through deregulating its phosphorylation and ubiquitination

Significance Intracellular accumulation of the abnormally modified tau is hallmark pathology of AD, but the mechanism leading to tau aggregation is not fully characterized. In the present study, we studied the effects of tau SUMOylation on its phosphorylation, ubiquitination, degradation, and aggregation. We discovered that sumoylation competes with ubiquitination in modifying tau, correlating with tau hyperphosphorylation. Identification of the posttranslational modification on tau provides the new insight into the molecular mechanism in tau aggregation. Intracellular accumulation of the abnormally modified tau is hallmark pathology of Alzheimer’s disease (AD), but the mechanism leading to tau aggregation is not fully characterized. Here, we studied the effects of tau SUMOylation on its phosphorylation, ubiquitination, and degradation. We show that tau SUMOylation induces tau hyperphosphorylation at multiple AD-associated sites, whereas site-specific mutagenesis of tau at K340R (the SUMOylation site) or simultaneous inhibition of tau SUMOylation by ginkgolic acid abolishes the effect of small ubiquitin-like modifier protein 1 (SUMO-1). Conversely, tau hyperphosphorylation promotes its SUMOylation; the latter in turn inhibits tau degradation with reduction of solubility and ubiquitination of tau proteins. Furthermore, the enhanced SUMO-immunoreactivity, costained with the hyperphosphorylated tau, is detected in cerebral cortex of the AD brains, and β-amyloid exposure of rat primary hippocampal neurons induces a dose-dependent SUMOylation of the hyperphosphorylated tau. Our findings suggest that tau SUMOylation reciprocally stimulates its phosphorylation and inhibits the ubiquitination-mediated tau degradation, which provides a new insight into the AD-like tau accumulation.
Training Executive, Attention, and Motor Skills

Objective: To examine whether cognitive enhancement can be delivered through play to preschoolers with ADHD and whether it would affect severity of ADHD symptoms. Method: Twenty-nine 4- and 5-year-old children and their parents participated in separate group sessions (3-5 children/group). Child groups were introduced games designed to enhance inhibitory control, working memory, attention, visuospatial abilities, planning, and motor skills. Parent groups were encouraged playing these games with their children at least 30 to 45 min/day and taught strategies for scaffolding difficulty levels and dealing with obstacles to daily playing. Results: Parent ratings and session attendance indicated considerable satisfaction with the program. Parent (p < .001) and teacher (p = .003) ratings on the ADHD-Rating Scale–IV (ADHD-RS-IV) indicated significant improvement in ADHD severity from pre- to post-treatment, which persisted 3 months later. Conclusion: This play-based intervention for preschoolers with ADHD is readily implemented at home. Preliminary evidence suggests efficacy beyond the termination of active treatment.
The elderly in five nations: the importance of universal coverage.

This paper reports 1999 survey results on the population age sixty-five and older in five nations--Australia, Canada, New Zealand, the United Kingdom, and the United States. The majority of respondents were generally satisfied with the quality, affordability, and availability of health services in their nations. In many measures of access to and cost of care, the United States looks much like the other nations surveyed. However, as the elderly view their health systems, the direction they have taken in recent years with respect to caring for the elderly, and the future affordability of care in old age, U.S. respondents tended to be more pessimistic than were those in other nations.
Surgical treatment of non‐functioning pancreatic islet cell tumors

Pancreatic endocrine tumors (PETs) are rare neoplasms originating from the amine precursor uptake and decarboxylation (APUD) stem cells. Although the majority of PETs are sporadic, they frequently occur in familial syndromes. PETs may cause a variety of functional syndromes or symptoms of local progression if they are non‐functional. General neuroendocrine tumor markers are highly sensitive in the diagnostic assessment of a PET. Imaging studies for tumor localization and staging include computer tomography (CT) scan, magnetic resonance imaging (MRI), In111‐octreotide scan, MIBG, and endoscopic ultrasonography (EUS). Treatment of PETs often requires a multi‐modality approach; however, surgical resection remains the only curative therapy for localized (non‐metastatic) disease. Treatment of metastatic disease includes biologic agents, cytotoxic chemotherapy, and liver‐directed therapies. J. Surg. Oncol. 2005;89:170–185. © 2005 Wiley‐Liss, Inc.
Emergence of Multiagent Coalition by Leveraging Complex Network Dynamics

Emergence of a single coalition among self-interested agents operating on large scale-free networks is a challenging task. Existing approaches, both centralized and decentralized, suffer from high overhead costs to maintain network wide communiation. Furthermore, these works start by assuming a pre-established static complex network platform and then employ agents on the nodes of the network for mutual interactions. In other words, agents do not have the capability to form the network. These approaches use a given structure of the complex network with fixed properties and do not explore the other possible network configurations by varying the topological features. In addition to this, they do not use the network dynamics to control the dynamics of agent interactions. In this paper, we present a decentralized game-theoretic approach to this single coalition emergence problem that limits agent communications only to their immediate neighbors. We enable the agents to choose their interaction partners to form a dynamically growing SF network and show that this network formation process facilitates that emergence phenomenon. We perform an extensive computational study by varying some topological properties over two models of the scale-free network. The novel insight that we gain is that by increasing degree-heterogeneity and clustering-coefficient, implemented through agents’ partner selection strategy, we could enhance the likelihood of the emergence of a sustained single coalition over various types of scale-free networks.
Aspects in Communications : Performance

This paper deals with the similarities between “Aspect Oriented modeling” and “Software Stability modeling” techniques. The design pattern for performance is considered to elaborate on these issues. “Aspects”, which might arise at any stage of the software lifecycle, have attributes that are similar to the “Enduring Business Themes” and “Business Objects” used in the stable modeling.
Vasculopathy in cyclosporine-treated renal allografts: possible protection by diltiazem.

Morphometric quantitative analysis of the renal arterioles (arteriole wall thickness and overall vessel size) in renal biopsies from 21 cadaveric renal allograft recipients taken immediately prior to (0 months) and 3 months after transplantation was carried out using a computer-aided image analyzer. Patients (n = 10) who received standard therapy (cyclosporine, azathioprine and prednisolone) showed an increase in arteriole wall thickness at 3 months compared to 0 months (p < 0.01). Patients (n = 11) who received standard therapy and oral diltiazem (60 mg t.d.s.) showed no such change in the arteriole wall thickness between 0 and 3 months. As cyclosporine toxicity and/or chronic vascular rejection are associated with an increase in arteriole wall thickness, the differences observed in this study may imply a protective role for diltiazem on renal arterioles.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxiliary models (each corresponding to a separate prediction task). The reduction enables the use of all recent algorithmic advances (e.g. neural nets, forests). We show that the estimated effect model is robust to estimation errors in the auxiliary models, by showing that the loss satisfies a Neyman orthogonality criterion. Our approach can be used to estimate projections of the true effect model on simpler hypothesis spaces. When these spaces are parametric, then the parameter estimates are asymptotically normal, which enables construction of confidence sets. We applied our method to estimate the effect of membership on downstream webpage engagement on TripAdvisor, using as an instrument an intent-to-treat A/B test among 4 million TripAdvisor users, where some users received an easier membership sign-up process. We also validate our method on synthetic data and on public datasets for the effects of schooling on income.
Learning Surrogate Losses

The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can minimize virtually any non-differentiable and non-decomposable loss function (e.g. Miss-classification Rate, AUC, F1, Jaccard Index, Mathew Correlation Coefficient, etc.) seamlessly. Our strategy learns smooth relaxation versions of the true losses by approximating them through a surrogate neural network. The proposed loss networks are set-wise models which are invariant to the order of mini-batch instances. Ultimately, the surrogate losses are learned jointly with the prediction model via bilevel optimization. Empirical results on multiple datasets with diverse real-life loss functions compared with state-of-the-art baselines demonstrate the efficiency of learning surrogate losses.
Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images

Quantification of kidney function in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) requires careful segmentation of the renal region of interest (ROI). Traditionally, human experts are required to manually delineate the kidney ROI across multiple images in the dynamic sequence. This approach is costly, time-consuming and labour intensive, and therefore acts to limit patient throughout and acts as one of the factors limiting the wider adoption of DCR-MRI in clinical practice. Therefore, to address this issue, we present the first use of Dynamic Mode Decomposition (DMD) as a basis for automatic segmentation of a dynamic sequence, in this case, kidney ROIs in DCE-MRI. Using DMD coupled combined with thresholding and connected component analysis is first validated on synthetically generated data with known ground-truth, and then applied to ten healthy volunteers' DCE-MRI datasets. We find that the segmentation result obtained from our proposed DMD framework is comparable to that of expert observers and very significantly better than that of an a-priori bounding box segmentation. Our result gives a mean Jaccard coefficient of 0.87, compared to mean scores of 0.85, 0.88 and 0.87 produced from three independent manual annotations. This represents the first use of DMD as a robust automatic data-driven segmentation approach without requiring any human intervention. This is a viable, efficient alternative approach to current manual methods of isolation of kidney function in DCE-MRI.
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

In this paper we study yes/no questions that are naturally occurring — meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, and require difficult entailment-like inference to solve. We also explore the effectiveness of a range of transfer learning baselines. We find that transferring from entailment data is more effective than transferring from paraphrase or extractive QA data, and that it, surprisingly, continues to be very beneficial even when starting from massive pre-trained language models such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on our train set. It achieves 80.4% accuracy compared to 90% accuracy of human annotators (and 62% majority-baseline), leaving a significant gap for future work.
On the Reusability of Postexperimental Field Data for Underwater Acoustic Communications R&D

Field data are often expensive to collect, time-consuming to prepare to collect, and even more time-consuming to process after the experiment has concluded. However, it is often the practice that such data are used for little after the funded research activity that was concomitant with the experiment is completed. Immutability of the original experimental configuration either results in regathering of expensive field data, or in absence of such data, model-dependent analysis that partially captures the real-world dynamics. For underwater acoustic research and development, the standard communication pipeline might be modified to enable greater reusability of experimental field data. This paper first characterizes the necessary modifications to the standard communication pipeline to prepare signals for transmission and subsequent recording such that research trades for different modulation and coding schemes may be undertaken postexperiment, without the need for retransmission of additional waveforms. Then, using the modified mathematical framework, sufficient conditions for reliable postexperimental replay of the environment are recognized. Finally, techniques are discussed to collect sufficient environmental statistics such that subsequent research can be accomplished long after the experiment has been completed, and that results from a given experiment may be reasonably compared with those of another. Examples are provided using both synthetic and experimental data collected from at-sea field tests.
Marginal Fermi Liquid behaviour from charge density fluctuations in cuprates

Besides the mechanism responsible for high critical temperature superconductivity, the grand unresolved issue of the cuprates is the occurrence of a "strange" metallic state above the so-called pseudogap temperature $T^*$. Even though such state has been successfully described within a phenomenological scheme, the so-called Marginal Fermi Liquid (MFL) theory, a microscopic explanation is still missing. However, recent resonant inelastic X-ray scattering experiments on Nd$_{1+x}$Ba$_{2-x}$Cu$_3$O$_{7-\delta}$ and YBa$_2$Cu$_3$O$_{7-\delta}$ films identified a new class of charge density fluctuations (CDFs) characterized by low characteristic energies and very short correlation lengths. These CDFs are present over a very wide region of the temperature-vs-doping phase diagram and extend well above $T^*$. We investigated the consequences of CDFs on the electronic and transport properties and found that they can be used to explain the MFL phenomenology. Therefore, charge density fluctuations are likely the long-sought microscopic mechanism underlying the peculiarities of the normal state of cuprates.
Multiple roles of cytochrome c oxidase in mammalian cells under action of red and IR‐A radiation

This article reviews the current knowledge in photobiology and photomedicine about the influence of monochromatic, quasimonochromatic, and bread‐band radiation of red‐to‐near infrared (IR‐A) part on solar spectrum upon mammalian cells and human skin. The role of cytochrome c oxidase as the photoacceptor and photosignal transducer is underlined and its photosensitivity at certain circumstances is discussed. The role of ATP as a critical signaling molecule is discussed. © 2010 IUBMB IUBMB Life, 62(8): 607–610, 2010.
Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective

The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward. This is limiting in applications where we are concerned with not only the expected performance, but also the distribution of the performance. In this paper, we introduce micro-objective reinforcement learning --- an alternative RL formalism that overcomes this issue. In this new formulation, a RL task is specified by a set of micro-objectives, which are constructs that specify the desirability or undesirability of events. In addition, micro-objectives allow prior knowledge in the form of temporal abstraction to be incorporated into the global RL objective. The generality of this formalism, and its relations to single/multi-objective RL, and hierarchical RL are discussed.