Miracle-dz/UniFAR-FTUs huggingface dataset:
Inﬂuence of α -Al 2 O 3 Template and Process Parameters on Atomic Layer Deposition and Properties of Thin Films Containing High-Density TiO 2 Phases

: High-density phases of TiO 2 , such as rutile and high-pressure TiO 2 -II, have attracted interest as materials with high dielectric constant and refractive index values, while combinations of TiO 2 -II with anatase and rutile have been considered promising materials for catalytic applications. In this work, the atomic layer deposition of TiO 2 on α -Al 2 O 3 (0 0 0 1) (c-sapphire) was used to grow thin ﬁlms containing different combinations of TiO 2 -II, anatase, and rutile, and to investigate the properties of the ﬁlms. The results obtained demonstrate that in a temperature range of 300–400 ◦ C, where transition from anatase to TiO 2 -II and rutile growth occurs in the ﬁlms deposited on c-sapphire, the phase composition and other properties of a ﬁlm depend signiﬁcantly on the ﬁlm thickness and ALD process time parameters. The changes in the phase composition, related to formation of the TiO 2 -II phase, caused an increase in the density and refractive index, minor narrowing of the optical bandgap, and an increase in the hardness of the ﬁlms deposited on c-sapphire at T G ≥ 400 ◦ C. These properties, together with high catalytic efﬁciency of mixed TiO 2 -II and anatase phases, as reported earlier, make the ﬁlms promising for application in various functional coatings.
Resonance circuit with a voltage clamping device and dynamic control method thereof

The invention discloses a resonance circuit with a voltage clamping device, which comprises a resonance capacitor and a resonance inductor, and also comprises a voltage clamping device connected on the resonance capacitor and/or the resonance inductor in parallel. The invention also discloses a dynamic control method of the resonance circuit. The resonance circuit comprises the resonance capacitor and the resonance inductor. The dynamic control method comprises the step of clamping over-voltage during resonance by the voltage clamping device connected on the resonance capacitor and/or the resonance inductor in parallel. The invention can reduce the resonance peak, avoid higher voltage stress and current stress caused by higher resonance voltage, and improve the working reliability of the circuit.
MINIREVIEW Worms and Flies as Genetically Tractable Animal Models To Study Host-Pathogen Interactions

Flies and worms have been chosen as model genetic organisms based on a number of traits that strike an excellent com-promise between complexity and genetic tractability. Although an antibacterial defense system in ﬂies was reported in the early 1970s (11), Drosophila melanogaster actually emerged as a model animal to study innate immunity 20 years later, when the ﬁrst parallels between mammalian and Drosophila innate immunity were discovered after a NF- (cid:2) B/Rel protein was found to translocate to the nucleus in response to microbial infection (39, 53, 76). In addition, in seminal studies Lemaitre et al. demonstrated that Toll and imd genes control the expression of antimicrobial defense in D. melanogaster and showed that mutations in the Toll signaling pathway dramatically re-duce survival after fungal infection (52, 54). Caenorhabditis elegans emerged a few years later when Tan et al. reported in 1999 that the nematode can be used to model Pseudomonas aeruginosa pathogenesis in mammals (82). Since then, these organisms have been extensively used to study host-pathogen interactions (reviewed in references 1, 5, 14, 35, 50, 69, and 78). in vertebrates, physical barriers and antimicrobial substances protect D. melanogaster and C. elegans from microbial attacks, but successful microorganisms are capable of over-coming of innate immune responses. The C. elegans Tol-1 receptor is apparently not involved in this process. The intracellular TIR domain of Toll interacts with a similar domain contained in the MyD88 conserved protein. In mammals, this leads to the activation of both MAPKs and NF- (cid:2) B that ultimately activates the innate immune system. Similarly, Toll activation triggers innate immunity in D. melanogaster through the activation of the NF- (cid:2) B-like transcription factors Dorsal and DIF. Upon the recognition of microbial pathogens, a C. elegans receptor(s) yet to be identiﬁed activates innate immunity through a Tir-1/MAPK signaling pathway. As in mammals, C. elegans defense responses involve the activation of microbial killing pathways and apoptotic pathways. Although caspases are required for the activation of innate immunity in D. melanogaster , an apoptotic defense response has not yet been reported.
Word Embeddings of Monosemous Words in Dictionary for Word Sense Disambiguation

—In the recent past, word embedding techniques have shown to capture semantic and syntactic information of natural language which could be exploited to solve the Word Sense Disambiguation (WSD) task. Word embeddings are generated using words appearing in context. However, some co-occurrence words in context have multiple meanings and are ambiguous. Therefore, it is sometimes difﬁcult to identify the meaning of a target word by using word embeddings of context words. In this paper, we propose to use word embeddings of monosemous words for the WSD task. We consider that word embeddings of monosemous words can contribute to determining the correct sense of a target word. Also, by using word dependency in a sentence, it is possible to capture the semantic relationship between the target word and the co-occurrence word as a feature. To evaluate the efﬁciency of the proposed WSD method, we show that it is effective for the WSD task to use both monosemous word information and dependency relation to the target word.
Social media, political uncertainty, and the stock market

This study proposes a new measure of firm-level uncertainty exposure around important political events. More specifically, we construct a degree of (dis)agreement among social media users who jointly mention firms and politicians. We study a sample of over 23 million tweets mentioning both a firm from the S&P 500 composite and ‘Trump’ from October 2016 to May 2017. We then analyze the relationship between the (dis)agreement measure and individual stock features. The results suggest that increased disagreement among such tweets is associated with heightened stock price volatility and trading volume. This link is observed before the US Presidential Inauguration in January 2017 but not afterwards. The finding is confirmed by further robustness checks based on filtered tweets with policy keywords and policy-sensitive industries. Abstract This study proposes a new measure of firm-level uncertainty exposure around important political events. More specifically, we construct a degree of (dis)agreement among social media users who jointly mention firms and politicians. We study a sample of over 23 million tweets mentioning both a firm from the S&P 500 composite and ‘Trump’ from October 2016 to May 2017. We then analyze the relationship between the (dis)agreement measure and individual stock features. The results suggest that increased disagreement among such tweets is associated with heightened stock price volatility and trading volume. This link is observed before the US Presidential Inauguration in January 2017 but not afterwards. The finding is confirmed by further robustness checks based on filtered tweets with policy keywords and policy-sensitive industries. findings show that the agreement measure derived from such tweets is associated with stock price volatility and trading volume after controlling for message sentiment and the size of information flows. The impact is statistically significant before Trump’s Inauguration 2017. We perform similar analysis for Twitter messages with ‘Trump’, a firm name, and a policy keyword. There are significant relations between the agreement measure and stock price volatility as well as trading volume. We also perform an analysis based on companies from policy-sensitive industries and obtain even stronger results.
Langevin Diffusion Variational Inference

Many methods that build powerful variational distributions based on unadjusted Langevin transitions exist. Most of these were developed using a wide range of different approaches and techniques. Unfortunately, the lack of a unified analysis and derivation makes developing new methods and reasoning about existing ones a challenging task. We address this giving a single analysis that unifies and generalizes these existing techniques. The main idea is to augment the target and variational by numerically simulating the underdamped Langevin diffusion process and its time reversal. The benefits of this approach are twofold: it provides a unified formulation for many existing methods, and it simplifies the development of new ones. In fact, using our formulation we propose a new method that combines the strengths of previously existing algorithms; it uses underdamped Langevin transitions and powerful augmentations parameterized by a score network. Our empirical evaluation shows that our proposed method consistently outperforms relevant baselines in a wide range of tasks.
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathological Images

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.
Achieving Heisenberg limit in the phase measurement through three-qubit graph states

We study the reciprocal of the mean quantum Fisher information (RMQFI), $\chi^2$ for general three qubit states, having graph and hypergraph states as special cases, for identifying genuine multi party entanglement characterized by $\chi^2<1$. We demonstrate that the most symmetric graph state and the GHZ state have the lowest RMQFI values leading to the highest statistical speed showing that both these states attain the Heisenberg limit in phase sensitivity. Unlike the GHZ state, graph states have the same RMQFI values for measurement through different parameters, a property shared by the hypergraph states. Three qubit graph and hypergraph states can violate Bell's inequality as $F_Q>N$. Both the GHZ state and the most symmetric graph state have the highest concurrence equalling 3 and the maximum QFI values.
Score-Based Diffusion meets Annealed Importance Sampling

More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a sequence of distributions interpolating between a tractable initial distribution and the target distribution of interest which we simulate from approximately using a non-homogeneous Markov chain. To obtain an importance sampling estimate of the marginal likelihood, AIS introduces an extended target distribution to reweight the Markov chain proposal. While much effort has been devoted to improving the proposal distribution used by AIS, an underappreciated issue is that AIS uses a convenient but suboptimal extended target distribution. We here leverage recent progress in score-based generative modeling (SGM) to approximate the optimal extended target distribution minimizing the variance of the marginal likelihood estimate for AIS proposals corresponding to the discretization of Langevin and Hamiltonian dynamics. We demonstrate these novel, differentiable, AIS procedures on a number of synthetic benchmark distributions and variational auto-encoders.
Manual-Guided Dialogue for Flexible Conversational Agents

How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In this paper, we propose a novel manual-guided dialogue scheme to alleviate these problems, where the agent learns the tasks from both dialogue and manuals. The manual is an unstructured textual document that guides the agent in interacting with users and the database during the conversation. Our proposed scheme reduces the dependence of dialogue models on fine-grained domain ontology, and makes them more flexible to adapt to various domains. We then contribute a fully-annotated multi-domain dataset MagDial to support our scheme. It introduces three dialogue modeling subtasks: instruction matching, argument filling, and response generation. Modeling these subtasks is consistent with the human agent's behavior patterns. Experiments demonstrate that the manual-guided dialogue scheme improves data efficiency and domain scalability in building dialogue systems. The dataset and benchmark will be publicly available for promoting future research.
Canopy elastic turbulence: Spontaneous formation of waves in beds of slender microposts

In a viscoelastic flow over a microfluidic canopy of polymeric pillars, we report the spontaneous emergence of waves in the form of propagating regions of low flow velocity compared to the surrounding flow. The occurrence of the wave is chaotic and shows characteristics of elastic turbulence. We systematically study the coupling between the low velocity wave and the microfluidic canopy by combining flow velocimetry experiments and high speed tracking of the pillars. The waves form an angle $\beta$ with the primary flow direction that depends on the geometry of the pillar array. If the canopy is composed of flexible structures, the passage of a wave deflects the structures locally in a manner reminiscent of the emergence of the Monami waves observed in inertial turbulence over canopies of vegetation. Due to the analogies with classical (inertial) canopy turbulence, we name our newly-observed phenomenon as canopy elastic turbulence.
Language-guided Semantic Style Transfer of 3D Indoor Scenes

We address the new problem of language-guided semantic style transfer of 3D indoor scenes. The input is a 3D indoor scene mesh and several phrases that describe the target scene. Firstly, 3D vertex coordinates are mapped to RGB residues by a multi-layer perceptron. Secondly, colored 3D meshes are differentiablly rendered into 2D images, via a viewpoint sampling strategy tailored for indoor scenes. Thirdly, rendered 2D images are compared to phrases, via pre-trained vision-language models. Lastly, errors are back-propagated to the multi-layer perceptron to update vertex colors corresponding to certain semantic categories. We did large-scale qualitative analyses and A/B user tests, with the public ScanNet and SceneNN datasets. We demonstrate: (1) visually pleasing results that are potentially useful for multimedia applications. (2) rendering 3D indoor scenes from viewpoints consistent with human priors is important. (3) incorporating semantics significantly improve style transfer quality. (4) an HSV regularization term leads to results that are more consistent with inputs and generally rated better. Codes and user study toolbox are available at https://github.com/AIR-DISCOVER/LASST.
The Roots of Misalignment: Insights from a System Dynamics Perspective

Aligning IT strategy with business strategy has repeatedly been shown to yield organizational performance benefits, and is consistently among executives' most important goals. However, as organizations pursue strategic alignment, IT and business strategies often evolve in unintended ways, thereby limiting alignment. In this essay, we review literature to identify reasons why IT and business strategies are often not realized in the manner in which they were originally intended, resulting in misalignment. To model the processes of strategy formation and strategic change, including processes at multiple levels within the organization, we utilize the causal loop diagramming approach of systems dynamics. Our model explains that key reasons for the differences between intended and realized strategies include the limitations of senior managers' communication, the ability of individuals and groups within organizational hierarchies to modify plans developed by senior management, and the reality of environmental dynamism. The primary intended contribution of this paper is the enumeration and modeling of both the top-down processes that can promote or hinder strategic alignment, as well as the less-studied bottom-up processes, ultimately yielding a more nuanced, dynamic, process-oriented understanding of strategic alignment.
Signal detection with dynamic programming

—This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained likelihood optimization problem, and design a computationally efﬁcient dynamic program that attains its optimal solution. Our proposed framework is scalable, simple to implement, and robust to model uncertainties. We show by extensive numerical experiments that our algorithm accurately estimates the locations in dense and noisy environments, and outperforms alternative methods
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax

Circular trading is a form of tax evasion in Goods and Services Tax where a group of fraudulent taxpayers (traders) aims to mask illegal transactions by superimposing several fictitious transactions ( where no value is added to the goods or service) among themselves in a short period. Due to the vast database of taxpayers, it is infeasible for authorities to manually identify groups of circular traders and the illegitimate transactions they are involved in. This work uses big data analytics and graph representation learning techniques to propose a framework to identify communities of circular traders and isolate the illegitimate transactions in the respective communities. Our approach is tested on real-life data provided by the Department of Commercial Taxes, Government of Telangana, India, where we uncovered several communities of circular traders.
A QUANTITATIVE ANALYSIS OF INSTRUCTOR-STUDENT VERBAL INTERACTION IN A TWO-WAY AUDIO TWO-WAY VIDEO DISTANCE EDUCATION SETTING

The purpose of this study was to establish a quantitative baseline analysis and description of the verbal interaction between instructors and students in simultaneous two-way audio and video distance education settings. Additionally, the nature of the interactions of students and instructors based upon their physical location in this distance learning environment was investigated The design was causal-comparative with data collected using observational methods, distinguished by a lack of random assignment of subjects and nonequivalent groups. The sample was purposefully drawn from classes taught over a videoconferencing network during the spring semester of 1995. The classroom observation instrument used was an expanded version of Johnson’s (I986) Cognitive Interaction Analysis System. Descriptive statistics are reportedfor all IO categories in the CIAS. The levels of classroom interaction found in these settings were not fundamentally different than those found in traditional classroom settings described in the literature. Significantly different levels of both total and cognitive student talk were found The interaction between the instructor and the location of the student was significant in determining the total amount of student talk and the amount of cognitive student talk. Diff erences among instructors contributed to both amount and type of student talk at local and remote sites. Students at remote sites contributed significantly higher levels of non-cognitive student talk.
CD4 T-Cell Suppression by Cells from Toxoplasma gondii -Infected Retinas Is Mediated by Surface Protein PD-L1 (cid:1)

In the inﬂamed retina, CD4 (cid:1) T cells can cause retinal damage when they are not properly regulated. Since tissue expression of major histocompatibility complex (MHC) class II and costimulatory molecules is a key mechanism for regulating effector T cells, we tested the hypothesis that upregulation of these proteins in the retina contributes to the regulation of CD4 T cells. Here we report that in retinas infected with the protozoan parasite Toxoplasma gondii , MHC class II is upregulated on inﬁltrating leukocytes as well as on resident retinal cells, including photoreceptors. Flow cytometric analysis indicated that B7 costimulatory family members (CD80, CD86, ICOS-L, and programmed death ligand 2 [PD-L2]) were not expressed on class II (cid:1) cells. In contrast, PD-L1 (also named B7-H1 or CD274) was expressed on the majority of both hematopoietic and resident retinal MHC class II-expressing cells. Retinal cells from Toxoplasma -infected animals were able to suppress T-cell activation in a PD-L1-dependent manner. Finally, we demonstrate that the expression of MHC class II and PD-L1 was critically dependent on gamma interferon (IFN- (cid:2) ) expression. These data suggest that retinal MHC class II and PD-L1 expression is a novel mechanism by which the retina protects itself from CD4 T-cell-mediated immune damage in ocular toxoplasmosis and other types of retinal immune responses.