Explicit Pairs Research Articles

Darboux–Bäcklund transformations, generalized Bianchi’s permutability theorem and exact solutions of a novel coupled modified Korteweg–de Vries system

In our research, we explore a coupled mKdV system within the Yijima–Oikawa long-wave–short-wave hierarchy, involving both real and complex dynamical variables. By establishing an explicit Lax pair, Darboux–Bäcklund transformation, we proved the generalized Bianchi’s permutability theorem for the coupled system utilizing Riccati representation of the Lax pair. We then uncover new solutions in unique closed forms, showcasing the intricate relationship between the real and complex variables. These solutions encompass diverse forms such as one-soliton, stationary and moving breathers, and two-soliton solutions, each demonstrating distinct properties. Notably, the dynamics reveal intriguing behavior where the complex potential oscillates precisely on the unit circle, contingent on the initial seed solution, thus, a novel dynamical Pythagorean triple associated is detected.

On the components of the unstable set of isolated invariant sets

The aim of this note is to shed some light on the topological structure of the unstable set of an isolated invariant set K. We give a bound on the number of essential quasicomponents of the unstable set of K in terms of the homological Conley index of K. The proof relies on an explicit pairing between Čech homology classes and Alexander–Spanier cohomology classes that takes the form of an integral.

Dynamic model for implicit aspect detection in sentiment analysis using novel aspect pointer compendium

Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models.

A Consistent Dual-MRC Framework for Emotion-cause Pair Extraction

Emotion-cause pair extraction (ECPE) is a recently proposed task that aims to extract the potential clause pairs of emotions and its corresponding causes in a document. In this article, we propose a new paradigm for the ECPE task. We cast the task as a two-turn machine reading comprehension (MRC) task, i.e., the extraction of emotions and causes is transformed to the task of identifying answer clauses from the input document specific to a query. This two-turn MRC formalization brings several key advantages: First, the QA manner provides an explicit pairing way to identify causes specific to the target emotion; second, it provides a natural way of jointly modeling the emotion extraction, the cause extraction, and the pairing of emotion and cause; and third, it allows us to exploit the well-developed MRC models. Based on the two-turn MRC formalization, we propose a dual-MRC framework to extract emotion-cause pairs in a dual-direction way, which enables a more comprehensive coverage of all pairing cases. Furthermore, we propose a consistent training strategy for the second-turn query, so the model is able to filter the errors produced by the first turn at inference. Experiments on two benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at https://github.com/zifengcheng/CD-MRC .

Trace forms on the cyclotomic Hecke algebras and cocenters of the cyclotomic Schur algebras

We define a unified trace form τ on the cyclotomic Hecke algebras Hn,K of type A, which generalize both Malle-Mathas' trace form on the non-degenerate version (with Hecke parameter ξ≠1) and Brundan-Kleshchev's trace form on the degenerate version. We use seminormal basis theory to construct an explicit pair of dual bases for Hn,K with respect to the form. We also construct an explicit basis for the cocenter of the corresponding cyclotomic Schur algebra, which shows that the cocenter has dimension independent of the ground field K, the Hecke parameter ξ and the cyclotomic parameters Q1,⋯,Qℓ.

Adaptive multi-step Runge–Kutta–Nyström methods for general second-order ordinary differential equations

In this study, a variable step size formulation of multi-step general Runge–Kutta–Nyström (MSGN) methods to directly integrate general second-order initial value problems (IVPs) is considered. This formula is carried out using an embedded explicit pair where: the higher-order formula is an accurate and the lower-order formula uses to estimate the local error. Numerical results show that the new methods perform much better in terms of functions evaluations, implementation cost and execution time compared to other existing high quality embedded Runge–Kutta (ERK) methods in the literature.

LpHardy's identities and inequalities for Dunkl operators

AbstractThe main purpose of this article is to establish theLp{L}^{p}Hardy’s identities and inequalities for Dunkl operator on any finite balls and the entire spaceRN{{\mathbb{R}}}^{N}. We also prove Hardy’s identities and inequalities on certain domains with distance function to the boundary∂Ω\partial \Omega. In particular, we use the notion of Bessel pairs introduced in Ghoussoub and Moradifam to extend Hardy’s identities for the classical gradients obtained by Lam et al., Duy et al., Flynn et al. to Dunkl gradients introduced by Dunkl. Our Hardy’s identities with explicit Bessel pairs significantly improve many existing Hardy’s inequalities for Dunkl operators.

Koszul duality for topological E n ‐operads

We show that the Koszul dual of an E n $E_n$ -operad in spectra is O ( n ) $O(n)$ -equivariantly equivalent to its n $n$ -fold desuspension. To this purpose we introduce a new O ( n ) $O(n)$ -operad of Euclidean spaces R n $R_n$ , the barycentric operad, that is fibred over simplexes and has homeomorphisms as structure maps; we also introduce its suboperad of restricted little n $n$ -discs D n $D_n$ , that is an E n $E_n$ -operad. The duality is realised by an unstable explicit S-duality pairing ( F n ) + ∧ B D n → S ¯ n $(F_n)_+ \wedge BD_n \rightarrow \bar{S}_n$ , where B $B$ is the bar-cooperad construction, F n $F_n$ is the Fulton–MacPherson E n $E_n$ -operad, and the dualising object S ¯ n $\bar{S}_n$ is an operad of spheres that are one-point compactifications of star-shaped neighbourhoods in R n $R_n$ . We also identify the Koszul dual of the operad inclusion map E n → E n + m $E_n \rightarrow E_{n+m}$ as the ( n + m ) $(n+m)$ -fold desuspension of an unstable operad map E n + m → Σ m E n $E_{n+m} \rightarrow \Sigma ^m E_n$ defined by May.

Alcohol conditioned contexts enhance positive subjective alcohol effects and consumption

Associations between alcohol and the places it is consumed are important at all stages of alcohol abuse and addiction. However, it is not clear how the associations are formed in humans or how they influence drinking, and there are few effective strategies to prevent their pathological effects on alcohol use. We used a human laboratory model to study the effects of alcohol environments on alcohol consumption. Healthy regular binge drinkers completed conditioned place preference (CPP) with 0 vs. 80 mg/100 mL alcohol (Paired Group). Control participants (Unpaired Group) completed sessions without explicit alcohol-room pairings. After conditioning, participants completed alcohol self-administration in either the alcohol- or no alcohol-paired room. Paired group participants reported greater subjective stimulation and euphoria, and consumed more alcohol in the alcohol-paired room in comparison to the no alcohol-paired room, and controls tested in either room. Moreover, the strength of conditioning significantly predicted drinking; participants who exhibited the strongest CPP consumed the most alcohol in the alcohol-paired room. This is the first empirical evidence that laboratory-conditioned alcohol environments directly influence drinking. The results also confirm the viability of the model to examine the mechanisms by which alcohol environments stimulate drinking and to test strategies to counteract their influence on behavior.

A Trigonometrically Adapted Embedded Pair of Explicit Runge-Kutta-Nyström Methods to Solve Periodic Systems

In this paper a 5(3) pair of explicit trigonometrically adapted Runge-Kutta-Nystrom methods with four stages is derived based on an explicit pair appeared in the literature. The new adapted method is able to integrate exactly the usual test equation: y′′ = −w 2 y. The local truncation error of the new method is obtained, proving that the algebraic order of convergence is maintained. The stability interval of the new method is obtained, showing that the proposed method is absolutely stable. The numerical experiments performed demonstrate the robustness of the new embedded pair in comparison with some standard codes available in the literature.

PUF Based Authenticated Key Exchange Protocol for IoT Without Verifiers and Explicit CRPs

A Physical Unclonable Function (PUF) provides a physical device a unique output for a given input, which can be regarded as the device’s digital fingerprint. Thus, PUFs can provide unique identities for billions of connected devices in Internet of Things (IoT) architectures. Plenty of PUF based authenticated key exchange (AKE) protocols have been proposed. However, most of them are designed for the authentication between an IoT node and the specific server/verifier, whom the IoT node registered with. Only a few of them are designed for the authentication between IoT nodes, and all these protocols need verifiers or explicit Challenge-Response Pairs (CRPs). In this paper, we propose the first PUF based AKE protocol for IoT without verifiers and explicit CRPs, which IoT nodes can freely authenticate each other and create a session key on their own without the help of any server or verifier. We compare the proposed protocol with 27 relevant PUF based AKE protocols to show the superiority, and analyze the computational cost of each entity in the proposed protocol to show the efficiency. We define the adversarial model of a PUF based AKE protocol for IoT and formally prove the security of the proposed protocol in random oracle model. The security of the proposed protocol is based on the Elliptic Curve Discrete Logarithm (ECDL), Elliptic Curve Computational Diffie-Hellman (ECCDH), and Decisional Bilinear Diffie-Hellman (DBDH) assumptions.

Particle shape tunes fragility in hard polyhedron glass-formers.

We demonstrate that fragility, a technologically relevant characteristic of glass formation, depends on particle shape for glass-formers comprised of hard polyhedral particles. We find that hard polyhedron glass-formers become stronger (less fragile) as particle shape becomes increasingly tetrahedral. We correlate fragility with local structure, and show that stronger systems display a stronger preference for a pairwise face-to-face motif that frustrates global periodic ordering and gives rise in most systems studied to bond angle distributions that are peaked around the ideal tetrahedral bond angle. We demonstrate through mean-field-like simulations of explicit particle pairs and surrounding baths of "ghost" particles that the prevalence of this pairwise configuration can be explained via free volume exchange and emergent entropic force arguments. Our study provides a clear and direct link between the local geometry of fluid structure and the properties of glass formation, independent of interaction potential or other non-geometric tuning parameters. We ultimately demonstrate that the engineering of fragility in colloidal systems via slight changes to particle shape is possible.

The extent of the neurocognitive impairment in elderly survivors of war suffering from PTSD: meta-analysis and literature review.

ObjectivesWe performed a meta-analysis and systematic review on elderly survivors of war suffering from PTSD to estimate the variability in their cognitive impairment based on individual neuropsychological tests.MethodsWe included case control studies that explored the association of cognitive deficits in elderly PTSD civilian survivor of wars (age >60 years), using MEDLINE, Embase and PsycINFO from the inception to January 2018. We compared the cognitive performances in three comparisons i) PTSD+ vs. PTSD− civilian survivors of war; ii) PTSD+ vs. Control and iii) PTSD− vs. Control. The risk of bias was assessed using the Newcastle-Ottawa Scale for case-control studies.ResultsOut of 2939 titles and abstracts, 13 studies were eligible for data extraction. As compared to PTSD− civilian survivors of war, PTSD+ civilian survivors of war demonstrated significant deficits on TMT-A, TMT-B, Digit span backward, explicit memory low pair associate, CVLT recognition, WAIS-verbal and non-verbal tests. As compared to health controls, PTSD+ survivors demonstrated significantly lower performance on explicit memory low pair and high associate, RAVLT immediate and delayed recall, CVLT delayed and short cued recall. Performance on the neuropsychological test between PTSD− survivors of war and controls was not significant for all tests.ConclusionThe pattern suggests that PTSD+ survivors of war had poorer performance in tasks requiring processing speed, executive function, attention, working memory and learning. The magnitude of the cognitive deficits in our pooled analysis was small to moderate depending on the neuropsychological test. Most of our pooled analysis suffered from a high risk of bias, which lowered the confidence in our results.

Controlling biases and diversity in diverse image-to-image translation

The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances. We consider here diverse image translation (DIT), an even more challenging setting in which an image can have multiple plausible translations. This is normally achieved by explicitly disentangling content and style in the latent representation and sampling different styles codes while maintaining the image content. Despite the success of current DIT models, they are prone to suffer from bias. In this paper, we study the problem of bias in image-to-image translation. Biased datasets may add undesired changes (e.g. change gender or race in face images) to the output translations as a consequence of the particular underlying visual distribution in the target domain. In order to alleviate the effects of this problem we propose the use of semantic constraints that enforce the preservation of desired image properties. Our proposed model is a step towards unbiased diverse image-to-image translation (UDIT), and results in less unwanted changes in the translated images while still performing the wanted transformation. Experiments on several heavily biased datasets show the effectiveness of the proposed techniques in different domains such as faces, objects, and scenes.

Skeleton-aware networks for deep motion retargeting

We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any explicit pairing between the motions in the training set. We leverage the fact that different homeomorphic skeletons may be reduced to a common primal skeleton by a sequence of edge merging operations, which we refer to as skeletal pooling. Thus, our main technical contribution is the introduction of novel differentiable convolution, pooling, and unpooling operators. These operators are skeleton-aware , meaning that they explicitly account for the skeleton's hierarchical structure and joint adjacency, and together they serve to transform the original motion into a collection of deep temporal features associated with the joints of the primal skeleton. In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons. Thus, retargeting can be achieved simply by encoding to, and decoding from this latent space. Our experiments show the effectiveness of our framework for motion retargeting, as well as motion processing in general, compared to existing approaches. Our approach is also quantitatively evaluated on a synthetic dataset that contains pairs of motions applied to different skeletons. To the best of our knowledge, our method is the first to perform retargeting between skeletons with differently sampled kinematic chains, without any paired examples.

Mix and Match Networks: Cross-Modal Alignment for Zero-Pair Image-to-Image Translation

This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities.

Generating pairs of projective special linear groups that fail to lift

AbstractThe following problem was originally posed by B. H. Neumann and H. Neumann. Suppose that a group G can be generated by n elements and that H is a homomorphic image of G. Does there exist, for every generating n‐tuple of H, a homomorphism and a generating n‐tuple of G such that ?M. J. Dunwoody gave a negative answer to this question, by means of a carefully engineered construction of an explicit pair of soluble groups. Via a new approach we produce, for , infinitely many pairs of groups that are negative examples to Neumanns' problem. These new examples are easily described: G is a free product of two suitable finite cyclic groups, such as , and H is a suitable finite projective special linear group, such as for a prime . A small modification yields the first negative examples with H infinite.

MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints.

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer's post-processing without access to the pre-processed data. MimickNet post-processing achieves a 0.940 ± 0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937 ± 0.025 SSIM on a prospectively acquired dataset, and 0.928 ± 0.003 SSIM on an out-of-distribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ouwen/MimickNet.

Open manifolds with non-homeomorphic positively curved souls

AbstractWe extend two known existence results to simply connected manifolds with positive sectional curvature: we show that there exist pairs of simply connected positively-curved manifolds that are tangentially homotopy equivalent but not homeomorphic, and we deduce that an open manifold may admit a pair of non-homeomorphic simply connected and positively-curved souls. Examples of such pairs are given by explicit pairs of Eschenburg spaces. To deduce the second statement from the first, we extend our earlier work on the stable converse soul question and show that it has a positive answer for a class of spaces that includes all Eschenburg spaces.

Quantification of emotional values for attributes from sentiment analysis based on mobile phone reviews

E-commerce online reviews are commonly short in length, lack of contextual semantics, and are mixed with explicit emotions and implicit emotions. The subjects of implicit emotions are not easy to be extracted and be defined. Now product-oriented attributes are used to define implicit and fine-grained emotions with the help of the domain emotion dictionary that is constructed by extracted explicit attribute-emotion pairs through using a variety of part-of-speech rules. And implicit emotion and attribute pairs for product “mobile phone” are mined with the help of explicit attribute-emotion pairs and probability calculation method. At last, explicit and implicit emotion values are calculated by considering degree and negative modifiers. The experimental results show that proposed method is effective and has certain commercial value. And the process method is easy to extend to other product fields.