Implicit Manner Research Articles

PUTIN AND BIDEN’S SPEECHES ON THE RUSSIAN INVOLVEMENT IN UKRAINE: DIALOGUE AND POLEMICS

Throughout the world’s long tradition with war, it has often been raised as a question whether any war was worth waging, and if so, when and how? The reply to this philosophical maze of extreme convictions differs depending on whom you ask. In this present article, we tackled the ideological differences that lead to the discrepancies between answers given by Russia and the United States of America. For this, we took as primary sources the speeches of 24 February 2022 that Vladimir Putin and Joe Biden made on Russia’s immediate involvement in Ukraine. We tried to look for dialogic clash over the choice of diction as a major ideological stance, relying on Michael Bakhtin’s theory of dialogism and his notions of hidden polemics and parody to illustrate the condemnation of the war on one end and its justification on the other. The aim was to extrapolate the themes tackled and their sequence by referring to the just war theory in its three divisions jus ad bellum, jus in bello, and jus post bellum which together structure the ancient practice of the art of war in its distinct phases. Biden and Putin heavily bid on these notions to give their viewpoints legal and ethical legitimacy and convince the international community of their just causes. Through this study, we found that the presidents stylized their speeches to follow each other’s, giving responses in an implicit and tacit manner, which we uncovered as hidden polemics in their styles of diction. We have also discovered that both presidents evoked their nemesis’ violation of the just war theory and defended their own just cause and respect for international regulation. This study falls within the field of discourse analysis that is a key element in understanding politics on the levels of presidential speeches.

Incorporating Intracellular Processes in Virus Dynamics Models.

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

Relation Knowledge Distillation by Auxiliary Learning for Object Detection.

Balancing the trade-off between accuracy and speed for obtaining higher performance without sacrificing the inference time is a challenging topic for object detection task. Knowledge distillation, which serves as a kind of model compression techniques, provides a potential and feasible way to handle above efficiency and effectiveness issue through transferring the dark knowledge from the sophisticated teacher detector to the simple student one. Despite demonstrating promising solutions to make harmonies between accuracy and speed, current knowledge distillation for object detection methods still suffer from two limitations. Firstly, most of the methods are inherited or refereed from the frameworks in image classification task, and deploy an implicit manner by imitating or constraining the features from the intermediate layers or the output predictions between the teacher and student models. While little consideration has been raised to the intrinsic relevance of the classification and localization predictions in object detection task. Besides, these methods fail to investigate the relationship between detection and distillation tasks in knowledge distillation pipeline, and they train the whole network by simply integrating losses from these two different tasks through hand-crafted designation parameters. For addressing the aforementioned issues, we propose a novel Relation Knowledge Distillation by Auxiliary Learning for Object Detection (ReAL) method in this paper. Specifically, we first design a prediction relation distillation module which makes the student model directly mimic the output predictions from the teacher one, and conduct self and mutual relation distillation losses to excavate the relation information between teacher and student models. Moreover, for better devolving into the relationship between different tasks in distillation pipeline, we introduce the auxiliary learning into knowledge distillation for object detection and develop a dynamic weight adaptation strategy. Through regarding detection task as primary task and treating distillation task as auxiliary task in auxiliary learning framework, we dynamically adjust and regularize the corresponding weights of the losses for these tasks during the training process. Experiments on MS COCO dataset are conducted using various detector combinations of teacher and student models and the results show that our proposed ReAL can achieve obvious improvement on different distillation model configurations, while performing favorably against state-of-the-arts.

Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible.

Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.

Why Metametonymic Argot in the Publicity Texts of China’s National College Entrance Examination Scores?

After China’s National College Entrance Examination (NCEE) in recent years, some high schools tend to publicize their NCEE scores by QQ groups, WeChat groups, or posters in an implicit manner due to the inconformity of NCEE-score publicity with the present educational policy in China. NCEE-score publicity texts are now presented like a discussion of harvest events such as a harvest of a type of plant or animal. This paper studies how and why such texts occur. It is found that: 1) each text is a metaphorical one, mapping plants, animals or other things onto NCEE testees, 2) each text is a textual configuration by metaphor chains of a harvest of plants, animals or other things, 3) the metaphors in each text are also metonymies, hence metametonymies, 4) all expressions in the text make a metametonymic argot, 5) each text is rich in pragma-rhetorical values. Be that as it may, in the context of maintaining NCEE fairness, NCEE-score publicity texts, however implicit, should not be encouraged despite their cleverness in evading official punishment.

Interpersonal prior information informs ensemble coding through the co-representation process.

Humans have the ability to rapidly extract summary statistics from object groupings through a specific capability known as ensemble coding. Previous literature has reported that this ability can become biased by prior perceptual experiences at the individual level. However, it remains unknown whether interpersonal prior information could also bias ensemble perception through a co-representation process. Experiment 1 found that participants' summary estimations were biased toward their co-actor's stimuli. Experiment 2 confirmed a causal relationship between the bias effect and the co-representation process by showing a reduction in biased estimation after pairing participants with an out-group partner. These findings extend the sources of prior information exploited by humans during perceptual average from individual-level information (i.e., self-tasks) to interpersonal-level information (i.e., co-actor's tasks). More specifically, interpersonal prior information is shown to act in a top-down and implicit manner, biasing ensemble perception.

A semi-implicit semi-Lagrangian method for simulating immersed boundary motion under high inertia and elasticity

In this paper, we present an efficient and stable fractional-step 2D immersed boundary (IB) method for solving the interaction problems between bulk fluid and elastic interface, in particular, when the fluid inertia and the interfacial elasticity are the significant factors affecting its dynamics. In myriads of real-world applications, the effects of high inertia and elasticity are dominant. So the complex fluid dynamics under such harsh conditions is an important topic in computational physics and is inherently challenging due to high computational complexity. In turn, it requires to solve the governing equations of elastic interfacial motion in an implicit manner so that more stable simulations can be performed by relaxing the Courant-Friedrichs-Lewy (CFL) condition. The contributions of the proposed approach are three folds. First, an iteration-free semi-Lagrangian method is employed in Navier-Stokes (NS) equations. Second, the elastic force acting along the interface is treated semi-implicitly in IB formulations. Both approaches improve the numerical stability associated with the high fluidic inertia and interfacial elasticity. Finally, to solve the resulting linear system, our novel idea is to transform the original 3-by-3 block matrix system into a reduced 2-by-2 block matrix system using a discrete projection operator in a staggered grid, and then explicitly represent the exact solution via the Schur complement of the Helmholtz operator. Owing to this feature, we refer to this proposed approach as reduced immersed boundary method (rIBM). We show that the two systems are equivalent in theory, whereas the conventional immersed boundary projection method (IBPM) modifies the discrete momentum equation in the original system. A series of numerical tests is conducted to confirm the stability of the rIBM using relatively larger time-step sizes, specifically with Reynolds number and inverse capillary number equal to or larger than approximately 1000. By estimating the computational time, the numerical efficiency of the proposed method is further verified in comparison with the conventional IBPM and the Crank-Nicolson scheme-based IB method. In conclusion, the proposed approach not only improves the numerical stability, but also increases the computational speed, suitable for solving more realistic problems.

Concurrent Implicit Adaptation to Multiple Opposite Perturbations.

Simultaneous adaptation to opposite visuomotor perturbations is known to be difficult. It has been shown to be possible only in situations where the two tasks are associated with different contexts, being either a different colored background, a different area of workspace, or a different follow-through movement. However, many of these elements evoke explicit mechanisms that could contribute to storing separate (modular) memories. It remains to be shown whether simultaneous adaptation to multiple perturbations is possible when they are introduced in a fully implicit manner. Here, we sought to test this possibility using a visuomotor perturbation small enough to eliminate explicit awareness. Participants (N = 25) performed center-out reaching movements with a joystick to five targets located 72° apart. Depending on the target, visual feedback of cursor position was either veridical (one target) or could be rotated by +5 or -5° (two targets each). After 300 trials of adaptation (60 to each target), results revealed that participants were able to fully compensate for each of the imposed rotations. Moreover, when veridical visual feedback was restored, participants exhibited after-effects that were consistent with the rotations applied at each target. Questionnaires collected immediately after the experiment confirmed that none of the participants were aware of the perturbations. These results speak for the existence of implicit processes that can smoothly handle small and opposite visual perturbations when these are associated with distinct target locations.

‘God Protects Us’: Nationalism versus Religious representation in the US and Indonesian Presidential Speeches

Presidential speech may reflect the cultural identity of the nation. This corpus-based research investigates the religiosity reflection in the U.S and Indonesian presidential speeches to reveal the underlying values. It built the corpus from the US presidential speeches (321,508 words) and the Indonesia presidential speeches (93,419 words) It applies Wordsmith 4.0 for the data mining. The findings show that both presidents used religious-related words, particularly ‘god’. The US president has used ‘god’ in fixed expressions ‘God bless you all’ and ‘May God protect our troops’, while the Indonesian president uses the word ‘god’ including its synonym in Arabic ‘Alloh’ in a wider variety of expressions such as ‘May god bless us all’, ‘May God protect us’, ‘the almighty God’, and ‘God’s help’ and ‘Bismillah’ (by the name of God), ‘Alhamdulillah’ (Praise be to Alloh), and ‘Insyaalloh’ (God willing). However, the use of ‘god’ in presidential speeches has different underlying values: the American president demonstrates strong nationalism, while the Indonesian president shows the strong religious side of the Indonesian people. The research concludes that the US president expresses religiosity in a more implicit manner, being in line with the self-reliant attitude of American society. On the contrary, the Indonesian president expresses religiosity in a very explicit manner, reflecting a god-dependent attitude in Indonesian society.

Gradient-Based Graph Attention for Scene Text Image Super-resolution

Scene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and exploit these as prior knowledge to guide model convergence. In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. We introduce a non-local group-wise attention module to extract text features which are then enhanced by a cascaded channel attention module and a novel gradient-based graph attention module in order to obtain more effective representations by exploring correlations of regional and local patch-wise text layout properties. Extensive experiments on the benchmark TextZoom dataset convincingly demonstrate that our method supports excellent text recognition and outperforms the current state-of-the-art in STISR. The source code is available at https://github.com/xyzhu1/TSAN.

VIDM: Video Implicit Diffusion Models

Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in an implicit condition manner, i.e. one can sample plausible video motions according to the latent feature of frames. We improve the quality of the generated videos by proposing multiple strategies such as sampling space truncation, robustness penalty, and positional group normalization. Various experiments are conducted on datasets consisting of videos with different resolutions and different number of frames. Results show that the proposed method outperforms the state-of-the-art generative adversarial network-based methods by a significant margin in terms of FVD scores as well as perceptible visual quality.

Knowledge-enhanced Prompt-tuning for Stance Detection

Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Existing methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and background knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first propose a topic model for learning hashtag representation and introduce ConceptGraph as the supplement of the target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of KEprompt over state-of-the-art methods.

Transparency as a Contested Fundamental in the Law of International Organizations

Abstract Against the backdrop of a recent turn to theory in the field of international organizations law this short article, part of the Special Forum on Contested Fundamentals of the Law of International Organizations, brings to the fore a characteristic of the international organization that should not be missing in the canon of fundamentals in international organizations scholarship. This is the transparency of international organizations as a legal entity and as a legal actor. ‘Transparency’ here refers to the phenomenon that member states and other institutional components are to some extent legally visible. ‘Legally visible’ means that the component parts of an organization, notably the member states, are addressed from, and involved in, the general international plane – a condition which is dynamic and context-dependent. In the words of the ila, organizations are layered creatures, ‘conducting … multilevel operations’. The article sets out how the transparency of organizations is a fundamental in two ways: as a legal-ontological claim, and as an analytical lens. Moreover the transparency of international organizations is subject to systemic and political contestation, albeit often in an implicit manner. The article concludes by arguing that the lens of transparency has lasting relevance and analytical value, as it helps to lay bare an elusive and continual dynamic in the legal manifestation of international organizations.

A family of well-balanced WENO and TENO schemes for atmospheric flows

We herein present a novel methodology to construct very high order well-balanced schemes for the computation of the Euler equations with gravitational source term, with application to numerical weather prediction (NWP). The proposed method is based on augmented Riemann solvers, which allow preserving the exact equilibrium between fluxes and source terms at cell interfaces. In particular, the augmented HLL solver (HLLS) is considered. Different spatial reconstruction methods can be used to ensure a high order of accuracy in space (e.g. WENO, TENO, linear reconstruction), being the TENO reconstruction the preferred method in this work. To the knowledge of the authors, the TENO method has not been applied to NWP before, although it has been extensively used by the computational fluid dynamics community in recent years. Therefore, we offer a thorough assessment of the TENO method to evidence its suitability for NWP considering some benchmark cases which involve inertia and gravity waves as well as convective processes. The TENO method offers an enhanced behavior when dealing with turbulent flows and underresolved solutions, where the traditional WENO scheme proves to be more diffusive. The proposed methodology, based on the HLLS solver in combination with a very high-order discretization, allows carrying out the simulation of meso- and micro-scale atmospheric flows in an implicit Large Eddy Simulation manner. Due to the HLLS solver, the isothermal, adiabatic and constant Brunt-Väisälä frequency hydrostatic equilibrium states are preserved with machine accuracy.

Evaluation of Nature of Science Representations in Biology School Textbooks Based on a Differentiated Family Resemblance Approach

Studies on the quality of nature of science (NOS) representations in school science textbooks report them being mostly of implicit manner and not fully adequate. However, the often underlying NOS framework of the consensus list in these studies is criticized as undifferentiated and inadequate. The family resemblance approach (FRA) to NOS shows potential to give differentiated insights into the appropriateness of NOS representations with avoidance of specifying certain philosophical directions. Based on a fine-grained differentiated FRA category system (11 main categories, e.g., “knowledge”; 52 subcategories, e.g., “hypotheses”), the quality of cognitive-epistemic NOS representations identified in seven biology school textbooks from Germany was analyzed. For this, a category system was developed. Cognitive-epistemic NOS representations in four chapters of each of the seven textbooks were evaluated regarding manner (implicit, explicit) and adequacy (adequate, (partly) not adequate). Results indicate, among others, that explicit representations of the cognitive-epistemic system of science were mainly placed in the introduction chapters, whereas subject-related chapters include mostly implicit representations. In this article, we present the evaluation of the quality of cognitive-epistemic NOS representations and discuss implications for science education.

Can repeated intranasal oxytocin administration affect reduced neural sensitivity towards expressive faces in autism? A randomized controlled trial.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication and interaction. Crucial for efficient social interaction is the ability to quickly and accurately extract information from a person's face. Frequency-tagging electroencephalography (EEG) is a novel tool to quantify face-processing sensitivity in a robust and implicit manner. In terms of intervention approaches, intranasal administration of oxytocin (OT) is increasingly considered as a potential pharmacological approach for improving socio-communicative difficulties in ASD, through enhancing social salience and/or reducing (social) stress and anxiety. In this randomized, double-blind, placebo-controlled, mechanistic pharmaco-neuroimaging clinical trial, we implemented frequency-tagging EEG to conduct an exploratory investigation into the impact of repeated OT administration (4 weeks, 12 IU, twice daily) on neural sensitivity towards happy and fearful facial expressions in children with ASD (8-12 years old; OT: n = 29; placebo: n = 32). Neural effects were assessed at baseline, post-nasal spray (24 hr after the last nasal spray) and at a follow-up session, 4 weeks after the OT administration period. At baseline, neural assessments of children with ASD were compared with those of an age- and gender-matched cohort of neurotypical (NT) children (n = 39). Children with ASD demonstrated reduced neural sensitivity towards expressive faces, as compared to NT children. Upon nasal spray administration, children with ASD displayed a significant increase in neural sensitivity at the post- and follow-up sessions, but only in the placebo group, likely reflecting an implicit learning effect. Strikingly, in the OT group, neural sensitivity remained unaffected from the baseline to the post-session, likely reflecting a dampening of an otherwise typically occurring implicit learning effect. First, we validated the robustness of the frequency-tagging EEG approach to assess reduced neural sensitivity towards expressive faces in children with ASD. Furthermore, in contrast to social salience effects observed after single-dose administrations, repeated OT administration dampened typically occurring learning effects in neural sensitivity. In line with OT's social anxiolytic account, these observations possibly reflect a predominant (social) stress regulatory effect towards emotionally evocative faces after repeated OT administration.

Implicit solvent approach based on generalized Born and transferable graph neural networks for molecular dynamics simulations.

Molecular dynamics simulations enable the study of the motion of small and large (bio)molecules and the estimation of their conformational ensembles. The description of the environment (solvent) has, therefore, a large impact. Implicit solvent representations are efficient but, in many cases, not accurate enough (especially for polar solvents, such as water). More accurate but also computationally more expensive is the explicit treatment of the solvent molecules. Recently, machine learning has been proposed to bridge the gap and simulate, in an implicit manner, explicit solvation effects. However, the current approaches rely on prior knowledge of the entire conformational space, limiting their application in practice. Here, we introduce a graph neural network based implicit solvent that is capable of describing explicit solvent effects for peptides with different compositions than those contained in the training set.

Attraction and compromise effects under social exclusion

PurposeIntegrating conceptual perspectives from social exclusion, thinking style and context effects, this study aims to examine how different types of social exclusion influence attraction and compromise effects.Design/methodology/approachEight studies were conducted. To establish the causal relationship between social exclusion types and context effects, this study uses experimental designs in all studies.FindingsThe attraction effect is stronger when consumers feel rejected than ignored, whereas the compromise effect is stronger when they feel ignored than rejected. Consumers who feel rejected increase their propensity to think holistically, which in turn leads to their choice preferences for asymmetrically dominant options. Conversely, those who feel ignored increase their propensity to think analytically, which in turn leads to their choice preferences for compromise options.Research limitations/implicationsThe findings show that consumer preferences for one option over the other alternatives in choice contexts are susceptible to subtle differences in the manner that exclusion is communicated. The studies are limited to recall tasks and scenarios that previous research has shown to be effective. Future research may use actual exclusion to corroborate this study’s findings.Practical implicationsMarketing practitioners may benefit from this study’s findings when it comes to an increase in the relative share of their target brand against their competitor brands by introducing a third option.Originality/valueTo the best of the authors’ knowledge, this research is the first to provide evidence that exclusion communicated in an explicit manner produces the attraction effect, whereas exclusion communicated in an implicit manner produces the compromise effect. Given that threatening situations often influence individuals’ preferences and choices, how social exclusion shapes cognitive processes is an empirical question worthy of investigation.

RelationTrack: Relation-Aware Multiple Object Tracking With Decoupled Representation

Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID). In order to enhance the inference speed and reduce the complexity, current methods commonly integrate these double subtasks into a unified framework. Nevertheless, detection and ReID demand diverse features. This issue results in an optimization contradiction during the training procedure. With the target of alleviating this contradiction, we devise a module named Global Context Disentangling (GCD) that decouples the learned representation into detection-specific and ReID-specific embeddings. As such, this module provides an implicit manner to balance the different requirements of these two subtasks. Moreover, we observe that preceding MOT methods typically leverage local information to associate the detected targets and neglect to consider the global semantic relation. To resolve this limitation, we develop a module, referred to as Guided Transformer Encoder (GTE), by combining the powerful reasoning ability of Transformer encoder and deformable attention. Unlike previous works, GTE avoids analyzing all the pixels and only attends to capture the relation between query nodes and a few self-adaptively selected key samples. Therefore, it is computationally efficient. Extensive experiments have been conducted on the MOT16, MOT17 and MOT20 benchmarks to demonstrate the superiority of the proposed MOT framework, namely RelationTrack. The experimental results indicate that RelationTrack has surpassed preceding methods significantly and established a new state-of-the-art performance, e.g., IDF1 of 70.5% and MOTA of 67.2% on MOT20.

Local-global coordination with transformers for referring image segmentation

Referring image segmentation has sprung up benefiting from the outstanding performance of deep neural networks. However, most existing methods explore either local details or the global context of the scene without sufficiently modelling the coordination between them, leading to sub-optimal results. In this paper, we propose a transformer-based method to enforce the in-depth coordination between short- and long-range dependencies in both explicit and implicit fusion processes. Specifically, we design a Cross Modality Transformer (CMT) module with two successive blocks for explicitly integrating linguistic and visual features, which first locates the related visual region in a global view before concentrating on local patterns. Besides, a Hybrid Transformer Architecture (HTA) is utilized as a feature extractor in the encoding stage to capture global relationships and retain local cues. It can further aggregate the multi-modal features in an implicit manner. In the decoding stage, a Cross-level Information Integration module (CI2) is developed to gather information from adjacent levels by dual top-down paths, including a guided filtration path and a residual reservation path. Experimental results show that the proposed method outperforms the state-of-the-art methods on four RIS benchmarks.