Related Pairs Research Articles

Learning Social Spatio-Temporal Relation Graph in the Wild and a Video Benchmark.

Social relations are ubiquitous and form the basis of social structure in our daily life. However, existing studies mainly focus on recognizing social relations from still images and movie clips, which are different from real-world scenarios. For example, movie-based datasets define the task as the video classification, only recognizing one relation in the scene. In this article, we aim to study the problem of social relation recognition in an open environment. To close the gap, we provide the first video dataset collected from real-life scenarios, named social relation in the wild (SRIW), where the number of people can be huge and vary, and each pair of relations needs to be recognized. To overcome new challenges, we propose a spatio-temporal relation graph convolutional network (STRGCN) architecture, utilizing correlative visual features to recognize social relations intuitively. Our method decouples the task into two classification tasks: person-level and pair-level relation recognition. Specifically, we propose a person behavior and character module to encode moving and static features in two explicit ways. Then we take them as node features to build a relation graph with meaningful edges in a scene. Based on the relation graph, we introduce the graph convolutional network (GCN) and local GCN to encode social relation features which are used for both recognitions. Experimental results demonstrate the effectiveness of the proposed framework, achieving 83.1% and 40.8% mAP in person-level and pair-level classification. Moreover, the study also contributes to the practicality in this field.

Transfer learning based deep encoder decoder network for amyloid PET harmonization with small datasets

AbstractBackgroundThe existence of multiple amyloid tracers with varied characteristics poses a significant challenge to standardized interpretation and quantification of amyloid PET data. We previously demonstrated that a deep learning model using a residual inception encoder‐decoder network (RIED‐Net) architecture improved harmonization of florbetapir (FBP) and PiB PET. However, scarcity of head‐to‐head comparison datasets of other amyloid PET tracer may limit the ability to generalize this approach to the other amyloid PET tracers. In this research, we investigate the performance of RIED‐Net model trained on smaller datasets and explore transfer learning (TL) approaches which can leverage multiple different datasets and address the data scarcity issue.MethodWe previously trained our RIED‐Net model using PiB‐FBP data from Open Access Series of Imaging Studies (OASIS) (N = 92) and tested the performance in the independent Global Alzheimer’s Association Interactive Network (GAAIN) (N = 46) dataset. We now use the same procedure to train the RIED‐Net model using the smaller GAAIN dataset and evaluate the model performance. We then examine the feasibility of a TL approach by comparing the model performance between RIED‐Net model directly trained on the PiB‐florbetaben (FBB) from GAAIN (N = 35) and the TL approach in which we started with the optimal RIED‐Net model for PiB‐FBP data and fine‐tuned the network parameters using the GAAIN PiB‐FBB data. A cross‐validation (CV) approach was used to evaluate model performance based on difference before and after harmonization in terms of correlation between mean cortical SUVR (mcSUVR) measurements from different tracers.ResultRIED‐Net model trained using the smaller GAAIN PiB‐FBP data improved the agreement of mcSUVR measurements as measured by Pearson correlation from r = 0.93 to r = 0.96 (p<0.01) on test‐set. For PiB‐FBB harmonization, the TL approach provided numerically better agreement in mcSUVR from r = 0.96 (direct train) to r = 0.97 (with TL), demonstrating the potential benefits of applying TL based technique to address the issue of smaller sample size.ConclusionWe demonstrate our RIED‐Net model can achieve improved harmonization even when trained on a relatively small training set. We also demonstrate the potential of transferring knowledge learned from one tracer pairs to improve the harmonization model of other related tracer pairs.

In the Quest of a Parametric Relation Between Ice Sheet Model Inferred Weertman's Sliding‐Law Parameter and Airborne Radar‐Derived Basal Reflectivity Underneath Thwaites Glacier, Antarctica

AbstractNumerical ice sheet models use sliding laws to connect basal shear stress and ice velocity to simulate ice sliding. A sliding‐law parameter β2 is used to control Weertman's sliding law in numerical ice sheet models. Basal reflectivity derived from ice‐penetrating radar also provides information about frozen or thawed conditions underneath glaciers. To assess whether basal reflectivity can be used to constrain β2, we carry out statistical experiments between two recently published datasets: β2 inferred from three numerical ice sheet models (ISSM, Úa and STREAMICE) and airborne radar‐derived relative basal reflectivity from the AGASEA‐BBAS mission over Thwaites Glacier (TG). Our results show no robust correlation between the β2–relative reflectivity pair. Pearson's correlation coefficient, a test of linearity, ranges from −0.26 to −0.38. Spearman's correlation coefficient, which does not require a linear assumption, is also modest (∼−0.35). We conclude that β2 and relative basal reflectivity underneath TG do not infer similar basal conditions.

KPRLN: deep knowledge preference-aware reinforcement learning network for recommendation

AbstractUser preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity–relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature well, as affecting the performance of recommender systems. In this paper, we propose a deep knowledge preference-aware reinforcement learning network (KPRLN) for the recommendation, which builds paths between user’s historical interaction items in the knowledge graph, learns the preference features of each user–entity–relation and generates the weighted knowledge graph with fine-grained preference features. First, we proposed a hierarchical propagation path construction method to address the problems of the pendant entity and long path exploration in the knowledge graph. The method expands outward to form clusters centered on items and uses them to represent the starting and target states in reinforcement learning. With the iteration of clusters, we can better learn the pendant entity preference and explore farther paths. Besides, we design an attention graph convolutional network, which focuses on more influential entity–relation pairs, to aggregate user and item higher order representations that contain fine-grained preference features. Finally, extensive experiments on two real-world datasets demonstrate that our method outperforms other state-of-the-art baselines.

Interval-valued fuzzy discernibility pair approach for attribute reduction in incomplete interval-valued information systems

Interval-valued information systems provide rich semantic interpretation and greater flexibility compared to real-valued information systems. Meanwhile, incomplete interval-valued information systems containing missing values exist widely in reality. One of the key issues in handling incomplete interval-valued information systems is attribute reduction. Unfortunately, there are few studies on attribute reduction for incomplete interval-valued information systems. Moreover, most of the existing studies use fuzzy similarity relations to measure the similarity of objects. Due to the complexity of incomplete interval-valued information systems, traditional fuzzy similarity relations cannot adequately represent the information contained within incomplete interval-valued information systems. To address this issue, this study proposes interval-valued fuzzy min-max similarity relations for incomplete interval-valued information systems, along with two attribute reduction algorithms based on interval-valued fuzzy discernibility pairs model for incomplete interval-valued information systems. Firstly, this study defines interval-valued fuzzy min-max similarity relations using distance-based extended restricted equivalence functions. Secondly, this study presents the interval-valued fuzzy discernibility pairs and discerning ability measure for attribute sets. Furthermore, an attribute reduction algorithm is proposed for incomplete interval-valued information systems. Thirdly, taking into account decision attributes, the relative interval-valued fuzzy discernibility pairs and an attribute reduction algorithm for incomplete interval-valued decision information systems are proposed. Finally, the effectiveness of the proposed method is compared to four representative attribute reduction algorithms in experiments. The experimental results illustrate that our method is effective and has advantages over the compared algorithms.

Undergraduate Students’ Morphological Awareness and Difficulties in EFL Class

Students' vocabulary knowledge is related to their morphological awareness, which plays an essential role in word comprehension and vocabulary development. This case study is aimed at describing undergraduate students’ morphological awareness and difficulties in morpheme identification and word relationship. This study employed the MAT test in two analytical tests. The Morpheme Identification Test is used to measure how well first-year college students can analyze and break down complicated words into their smaller meanings. Meanwhile, the Word Relation Test is administered to measure undergraduate students’ awareness of relations among base or root words and their inflected or derived forms. An interview was used to support the results of the tests. The result showed that the students performed better in breaking down inflected forms than those in derived forms. As in the Word Relation Test, it was discovered that the students had done better in identifying morphologically related word pairs. There were three types of difficulties in the morpheme identification test. The first was in breaking down complex words into smaller morphemes; the second was in stating the meaning of each smaller morpheme, especially affixes; and the third was in categorizing the complex words into inflected or derived forms.

Biologic targets of prescription medications and risk of neurodegenerative disease in United States Medicare beneficiaries.

To identify prescription medications associated with a lower risk of three neurodegenerative diseases: Parkinson disease, Alzheimer disease, and amyotrophic lateral sclerosis. We conducted a population-based, case-control study of U.S. Medicare beneficiaries in 2009 (42,885 incident neurodegenerative disease cases, 334,387 randomly selected controls). Using medication data from 2006-2007, we categorized all filled medications according to their biological targets and mechanisms of action on those targets. We used multinomial logistic regression models, while accounting for demographics, indicators of smoking, and health care utilization, to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for 141 target-action pairs and each neurodegenerative disease. For target-action pairs inversely associated with all three diseases, we attempted replication in a cohort study that included an active comparator group. We constructed the cohort by following controls forward for incident neurodegenerative disease from the beginning of 2010 until death or end of 2014, i.e., up to five years after the two-year exposure lag. We used Cox proportional hazards regression while accounting for the same covariates. The most consistent inverse association across both studies and all three neurodegenerative diseases was for xanthine dehydrogenase/oxidase blockers, represented by the gout medication, allopurinol. Allopurinol was associated with a 13-34% lower risk for each neurodegenerative disease group in multinomial regression, and a mean reduction of 23% overall, as compared to individuals who did not use allopurinol. In the replication cohort we observed a significant 23% reduction for neurodegenerative disease in the fifth year of follow-up, when comparing allopurinol users to non-users, and more marked associations with an active comparator group. We observed parallel associations for a related target-action pair unique to carvedilol. Xanthine dehydrogenase/oxidase blockade might reduce risk of neurodegenerative disease. However, further research will be necessary to confirm that the associations related to this pathway are causal or to examine whether this mechanism slows progression.

Descent distribution on Catalan words avoiding ordered pairs of relations

This work is a continuation of some recent articles presenting enumerative results for Catalan words avoiding one or a pair of consecutive or classical patterns of length 3. More precisely, we provide systematically the bivariate generating function for the number of Catalan words avoiding a given pair of relations with respect to the length and the number of descents. We also present several constructive bijections preserving the number of descents. As a byproduct, we deduce the generating function for the total number of descents on all Catalan words of a given length and avoiding a pair of ordered relations.

Judgments of learning enhance recall for category-cued but not letter-cued items.

Making immediate judgments of learning (JOLs) during study can influence later memory performance, with a common outcome being that JOLs improve cued-recall performance for related word pairs (i.e., positive reactivity) and do not impact memory for unrelated pairs (i.e., no reactivity). The cue-strengthening hypothesis proposes that JOL reactivity will be observed when a criterion test is sensitive to the cues used to inform JOLs (Soderstrom et al., Journal of Experimental Psychology: Learning, Memory, and Cognition, 41 (2), 553-558, 2015). Across four experiments, we evaluated this hypothesis with category pairs (e.g., A type of gem - Jade) and letter pairs (e.g., Ja - Jade). Participants studied a list comprised of both pair types, made (or did not make) JOLs, and completed a cued-recall test (Experiments 1a/b). The cue-strengthening hypothesis predicts greater positive reactivity for category pairs than for letter pairs, because making a JOL strengthens the relationship between the cue and target, which is more beneficial for material with an a priori semantic relationship. Outcomes were consistent with this hypothesis. We also evaluated and ruled out alternative explanations for this pattern of effects: (a) that they arose due to overall differences in recall performance for the two pair types (Experiment 2); (b) that they would also occur even when the criterion test is not sensitive to the cues used to inform JOLs (Experiment 3); and (c) that JOLs only increased memory strength for the targets (Experiment 4). Thus, the current experiments rule out plausible accounts of reactivity effects and provide further, converging evidence for the cue-strengthening hypothesis.

Familial Co-Aggregation of Idiopathic Inflammatory Myopathies and Cancer: A Swedish Population-Based Study.

To examine if idiopathic inflammatory myopathies (IIMs) share familial susceptibility with cancer, we estimated the familial co-aggregation of these diseases. This Swedish population-based family study with data from national registers included 8,460 first-degree relatives of patients with IIM and 41,127 relatives of matched individuals without IIM. We modeled the adjusted odds ratios (ORs) of familial co-aggregation of IIM and cancer using conditional logistic regressions and adjusted for sex and birth year of index individuals and their first-degree relatives. We examined the associations for cancer overall and stratified by several factors of interest. We also performed exploratory analyses for specific cancer types. We observed no statistically significant familial associations between IIM and cancer overall. However, there was a familial association in male relative pairs of patients with dermatomyositis (adjusted OR for familial association 1.39 [95% confidence interval (95% CI) 1.15-1.68]). The association remained statistically significant after controlling for multiple testing. Moreover, this finding was consistent between kinships. Familial co-aggregation of IIM and cancer diagnosed before 50 years of age was only observed in offspring. In exploratory analyses, only the familial associations for myeloid malignancies (adjusted OR 2.27 [95% CI 1.43-3.60]) and liver cancer (adjusted OR 2.01 [95% CI 1.21-3.33]) in male relative pairs remained significant after controlling for multiple testing. We found little evidence of shared familial susceptibility as a major pathologic mechanism contributing to the co-occurrence of IIM and cancer overall. There could be subsets of patients and cancer types for which familial factors including genetics and shared environments are of more importance, but these findings need replication.

Does the reactivity effect of judgments of learning transfer to learning of new information?

ABSTRACT Making judgments of learning (JOLs) can reactively change memory, a phenomenon termed the reactivity effect. The current study was designed to explore whether the reactivity effect transfers to subsequent learning of new information. Participants studied two blocks of words (Experiment 1) or related word pairs (Experiments 2 & 3). In Block 1, participants in the experimental (JOL) group made a JOL while studying each item, whereas the control (no-JOL) group did not make item-by-item JOLs. Then both groups studied Block 2, in which they did not make JOLs, and finally, they took a test on Blocks 1 and 2. Across Experiments 1 −3, the results showed superior Block 1 test performance in the JOL than in the no-JOL group, demonstrating a positive reactivity effect. Critically, there was minimal difference in Block 2 test performance between the two groups, implying little transfer of the positive reactivity effect to subsequent learning of new information. Furthermore, Experiment 3 demonstrated that the reactivity effect still failed to transfer even when participants explicitly appreciated the benefits of making JOLs. Educational implications are discussed.

은닉 마르코프 모델을 이용한 적응형 학습경로 생성에 관한 연구

Due to the global impact of COVID-19, the use of non-face-to-face learning is increasing. For non-face-to-face learning, it is important to create a learning path based on efficiency. This study introduces the hidden Markov model (HMM) as a method of creating a learning path known as a network in which knowledge concepts are arranged in order, that is, the path of experience that students may encounter in class. and it aims to improve the accuracy of path prediction by using a variable selection technique that includes least absolute shrinkage and selection operator (LASSO), and random forest (RF) before performing HMM. In addition, this study aims to show that the learning path based on higher-order concepts made of precedence relationships from HMM is more accurate than other candidate paths. As a result of using data shared by AI-hub (https://aihub.or.kr/), the performance of HMM when selecting relational pairs using LASSO, and RF was improved significantly, and the case of using HMM when evaluating the learning path consisting of higher concepts was excellent in terms of model goodness of fit compared to other models.

Establishing semantic relatedness through ratings, reaction times, and semantic vectors: A database in Polish.

This study presents a Polish semantic priming dataset and semantic similarity ratings for word pairs obtained with native Polish speakers, as well as a range of semantic spaces. The word pairs include strongly related, weakly related, and semantically unrelated word pairs. The rating study (Experiment 1) confirmed that the three conditions differed in semantic relatedness. The semantic priming lexical decision study with a carefully matched subset of the stimuli (Experiment 2), revealed strong semantic priming effects for strongly related word pairs, whereas weakly related word pairs showed a smaller but still significant priming effect relative to semantically unrelated word pairs. The datasets of both experiments and those of SimLex-999 for Polish were then used in a robust semantic model selection from existing and newly trained semantic spaces. This database of semantic vectors, semantic relatedness ratings, and behavioral data collected for all word pairs enable future researchers to benchmark new vectors against this dataset. Furthermore, the new vectors are made freely available for researchers. Although similar semantically strongly and weakly related word pairs are available in other languages, this is the first freely available database for Polish, that combines measures of semantic distance and human data.

The Formation of Biaoquan and Zhequan as a Pair of Philosophical Concepts in Chinese Buddhism

The general consensus in the field of Buddhist studies is that the terms “biaoquan” and “zhequan” are a pair of Buddhist philosophical concepts often used to designate two diametrically opposed forms of rhetoric. The former term constitutes its affirmative statement, while the latter defines a fact in negative terms—known in Christian theology as cataphatic and apophatic uses of language, respectively. Looking at the terms for which biaoquan and zhequan initially served as translations, especially in Xuanzang’s works, it would seem that these two concepts have not always appeared as a related pair representing the above-mentioned affirmative–negative dichotomy. The former could designate both affirmation (*vidhi) as well as the general activity of speech, syllables, and words (nāma). In the case of zhequan, it corresponds, in different texts, to the three Indian Buddhist concepts of negation (*pratiṣedha, *vyāvṛtti, *nivṛtti), implicative negation (paryudāsa), and exclusion of others (anyāpoha), with each use of the term “zhequan” carrying a different set of meanings and associated doctrines. Indeed, in various texts, the concept of zhequan might be opposed to the concept of biaoquan (*vidhi *sadhana) or opposed to pure negation (prasajya), or it might be applied on its own with no opposing concept. However, as Chinese Buddhism continued to develop throughout the Tang, biaoquan and zhequan came to be firmly associated and popularized as a pair of opposites. Looking at the doctrinal as well as the translation history of these two terms, this paper focuses on how they were used as a pair of opposing philosophical concepts, followed by an analysis of the profound influence of these two concepts on Chinese Buddhism.

Why do judgments of learning modify memory? Evidence from identical pairs and relatedness judgments.

Research has observed that monitoring one's own learning modifies memory for some materials but not for others. Specifically, making judgments of learning (JOLs) while learning word pairs improves subsequent cued-recall memory performance for related word pairs but not for unrelated word pairs. Theories that have attempted to explain this pattern of results assume that people attend to and process cue-target relatedness during learning more when making JOLs than they spontaneously do when not making JOLs. The present research directly tested this relatedness-processing assumption with unrelated and related word pairs as well as with hitherto unexamined materials: identical word pairs. In three experiments, participants studied word pairs while either making or not making JOLs. Results revealed that making JOLs improved memory for related word pairs as well as for identical word pairs, but not for unrelated word pairs. Importantly, in two of the experiments, participants were further asked to judge at test whether each cue appeared with an unrelated, related, or identical target before attempting to recall it. Results revealed that making JOLs improved the accuracy of these relatedness judgments independently from its effect on recall, thus providing direct evidence that people process cue-target relatedness when making JOLs more than they spontaneously do when not making JOLs. These findings contribute to a more comprehensive understanding of why judging one's own learning can modify memory and, more broadly, suggest that instructions to monitor learning can direct people's attention to information that is not or less processed otherwise. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Genetic Associations Between Stress-Related Disorders and Autoimmune Disease.

Objective: Emerging evidence supports a bidirectional phenotypic association between stress-related disorders and autoimmune disease. However, the biological underpinnings remain unclear. Here, the authors examined whether and how shared genetics contribute to the observed phenotypic associations. Methods: Based on data from 4,123,631 individuals identified from Swedish nationwide registers, familial coaggregation of stress-related disorders (any disorder or posttraumatic stress disorder [PTSD]) and autoimmune disease were initially estimated in seven cohorts with different degrees of kinship. Polygenic risk score (PRS) analyses were then performed with individual-level genotyping data from 376,871 participants in the UK Biobank study. Finally, genetic correlation analyses and enrichment analyses were performed with genome-wide association study (GWAS) summary statistics. Results: Familial coaggregation analyses revealed decreasing odds of concurrence of stress-related disorders and autoimmune disease with descending kinship or genetic relatedness between pairs of relatives; adjusted odds ratios were 1.51 (95% CI=1.09–2.07), 1.28 (95% CI=0.97–1.68), 1.16 (95% CI=1.14–1.18), and 1.01 (95% CI=0.98–1.03) for monozygotic twins, dizygotic twins, full siblings, and half cousins, respectively. Statistically significant positive associations were observed between PRSs of stress-related disorders and autoimmune disease, as well as between PRSs of autoimmune disease and stress-related disorders. GWAS summary statistics revealed a genetic correlation of 0.26 (95% CI=0.14–0.38) between these two phenotypes and identified 10 common genes and five shared functional modules, including one module related to G-protein–coupled receptor pathways. Similar analyses performed for PTSD and specific autoimmune diseases (e.g., autoimmune thyroid disease) largely recapitulated the results of the main analyses. Conclusions: This study demonstrated familial coaggregation, genetic correlation, and common biological pathways between stress-related disorders and autoimmune disease.

Guilt by Association: Inflammation and Shared Genetic Risk Between Stress-Related and Immune Disorders

Guilt by Association: Inflammation and Shared Genetic Risk Between Stress-Related and Immune Disorders

Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans

Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3’s patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., “lime–lemon”) word pairs than in other-related (e.g., “sour–lemon”) or unrelated (e.g., “tourist–lemon”) word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3’s semantic activation is better predicted by similarity in words’ meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3’s semantic network is organized around word meaning rather than their co-occurrence in text.

Differentiation of the Contribution of Familiarity and Recollection to the Old/New Effects in Associative Recognition: Insight from Semantic Relation.

Previous research has revealed two different old/new effects, the early mid-frontal old/new effect (a.k.a., FN400) and the late parietal old/new effect (a.k.a., LPC), which relate to familiarity and recollection processes, respectively. Although associative recognition is thought to be more based on recollection, recent studies have confirmed that familiarity can make a great contribution when the items of a pair are unitized. However, it remains unclear whether the old/new effects are sensitive to the nature of different semantic relations. The current ERP (event-related potentials) study aimed to address this, where picture pairs of thematic, taxonomic, and unrelated relations served as stimuli and participants were required to discriminate the pair type: intact, rearranged, "old + new", or new. We confirmed both FN400 and LPC. Our findings, by comparing the occurrence and the amplitudes of these two components, implicate that the neural activity of associative recognition is sensitive to the semantic relation of stimuli and depends more on stimulus properties, that the familiarity of a single item can impact the neural activities in discriminating associative pairs, and that the interval length between encoding and test modulates the familiarity of unrelated pairs. In addition, the dissociation between FN400 and LPC reinforces the dual-process models.

Quaternary Crustal Shortening of the Houyanshan Structure in the Eastern Chinese Tian Shan: Constrained from Geological and Geomorphological Analyses

The Tian Shan is one of the most active intracontinental orogenic belts in the world. It has undergone complex deformation that has resulted in the formation of several fold-and-thrust belts (FTBs) in the piedmonts and intermontane basins. Investigating the deformation histories of these FTBs is important for understanding the tectonic propagation processes of the Tian Shan. Here, we gain insight into these crustal shortening processes by deciphering the Houyanshan structure, a typical fold-thrust belt in the eastern Chinese Tian Shan. We first describe a curved thrust ramp and related fold pairs of the structure using high-resolution remote sensing photography, deformation of fluvial terraces, and field-based geological cross-section. Combined with deformed terrace records and optically stimulated luminescence (OSL) dating results, the kinematic style allows us to yield a geologic shortening rate of 1.6 ± 0.2 mm/a since ~52 ka. Second, to reduce uncertainty in the seismic interpretation and quantify the amount and time of crustal shortening, we interpret three seismic reflection profiles by using the theory of quantitative fault-related fold, area-depth-strain (ADS), and reverse modeling analyses. These profiles provide direct evidence that this structure connects by means of a listric thrust ramp to a shallow detachment level. ADS analysis reveals that the maximum shortening of the Huoyanshan structure is ~4.5 km, which is consistent with the result of quantitative inverse modeling. Each of the structural analysis methods gives similar parameters, and the high consistency of results greatly improves the soundness of a given geologic interpretation. Finally, the shortening rate and total shortening amount suggest that the structure may have formed at 1.8–3.7 Ma, which is nearly synchronous around the Tibetan Plateau. Together, these results indicate that this combined geological and geomorphological analysis provides greater insight into deformation information than can be achieved by any individual technique in studying fold-and-thrust belts worldwide.