Representation Scheme Research Articles

Neural models for semantic analysis of handwritten document images

Semantic analysis of handwritten document images offers a wide range of practical application scenarios. A sequential combination of handwritten text recognition (HTR) and a task-specific natural language processing system offers an intuitive solution in this domain. However, this HTR-based approach suffers from the problem of error propagation. An HTR-free model, which avoids explicit text recognition and solves the task end-to-end, tackles this problem, but often produces poor results. A possible reason for this is that it does not incorporate largely pre-trained semantic word embeddings, which turn out to be one of the most powerful advantages in the textual domain. In this work, we propose an HTR-based and an HTR-free model and compare them on a variety of segmentation-based handwritten document image benchmarks including semantic word spotting, named entity recognition, and question answering. Furthermore, we propose a cross-modal knowledge distillation approach to integrate semantic knowledge from textually pre-trained word embeddings into HTR-free models. In a series of experiments, we investigate optimization strategies for robust semantic word image representation. We show that the incorporation of semantic knowledge is beneficial for HTR-free approaches in achieving state-of-the-art results on a variety of benchmarks.

Dimension-Reduction Representation of Multivariate Mainshock-Aftershock Ground Motions Based on Measured Records

ABSTRACT This study proposes an integrated dimension-reduction representation strategy for simulating multivariate mainshock-aftershock stochastic ground motions based on measured motion records. The strategy includes a parameter identification method for modeling evolutionary power spectrum density (EPSD) of mainshock-aftershocks, a coherence function describing the spectrum correlation between mainshock and aftershock, and the EPSD function matrix establishment for mainshock-aftershock field combining with complex spatial coherence function. Furthermore, the dimension-reduction representation for simulating the ground motions considering the sequence and spatial variability simultaneously is presented with merely two elementary random variables. Numerical results effectively demonstrate the accuracy, efficiency, and engineering applicability of the proposed method.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BackgroundSurface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. ResultsHerein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SignificanceThe proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

The Role of Attachment Anxiety and Avoidance in Predicting Proximal Minority Stressors among Gay and Lesbian People in Italy.

As has been widely documented, minority stress affects the psychosocial well-being of gay and lesbian people. Recently, researchers have turned their attention to psychological factors that may influence the level of minority stress experienced, in order to explain individual differences in perceptions of proximal minority stressors. The present research aimed at assessing the effect of attachment avoidance and anxiety on levels of perceived stigma and internalized homonegativity. A total of 163 participants who self-identified as lesbian or gay (Mage = 32.56, SD = 10.87) were recruited and responded to the self-report questionnaires. Two multiple regression models were applied to assess the association between adult attachment and perceived stigma and internalized homonegativity. Results showed a positive association between attachment anxiety and avoidance and internalized homonegativity, as well as between attachment avoidance and perceived stigma. The emerging results demonstrate the impact of attachment anxiety and avoidance on proximal minority stressors and provide useful data for interventions addressing lesbian and gay people aimed at promoting security-based strategies of affect regulation and positive representations of self and others, which in turn may reduce the level of proximal minority stressors experienced and promote psychosocial well-being.

UFBSM: unmixing fusion and background sparse dictionary Model for hyperspectral anomaly detection

ABSTRACT In recent years, low-rank sparse representation (LRR) schemes have been applied to the field of hyperspectral image (HSI) anomaly detection (AD) and received attention. However, it is often accompanied by a small amount of noise in the AD, which leads to a high false alarm rate of detection results. In this paper, the HSI-AD algorithm is proposed to address this problem. First, the HSI is unmixed using the deep network to obtain the endmembers and abundance, and the abundance matrix is fused with the HSI to obtain the mixed data, which to a certain extent complements the spatial information of the HSI because the abundance matrix can reflect the subpixel-level information of the target image elements. Then, the mixed data are decomposed based on the model proposed in this paper. To overcome the situation that the sparse part of the traditional model is prone to mix with a small amount of noise, the sparse decomposition of the mixed data into anomaly and noise is used to remove the small amount of noise from the sparse part. A joint dictionary consisting of background and anomaly is used in the above model. In addition, to address the problem of information loss caused by the uniform accumulation of kernel norm over all singular values in the process of model decomposition, this paper designs the γ norm decomposition method to replace the general kernel norm decomposition for a more effective extraction of useful information.

Unveiling Otherness: Gendered Memories and Identity in Octavia Butler's Dawn

In recent years, American Studies have shifted their focus towards the political realm. However, despite the undermining of many moral-philosophical dogmas of the Western metaphysical tradition by poststructuralism and deconstruction, a significant portion of the political claims made in this revisionist turn still rely, albeit tacitly, on these moral and ethical assumptions. As these assumptions often clash with the theoretical foundations of revisionist works, some proponents resort to what could be termed as the "pathos of marginality" and rely on vague concepts of "otherness." Furthermore, these political-ideological interpretations often neglect aesthetic aspects, suspecting them of carrying implicit and hegemonic strategies of representation. This abstract delves into the exploration of gendered memories and identity within Octavia Butler's seminal work, Dawn. The narrative unfolds in a world fundamentally transformed by alien intervention, where the protagonist, Lilith, grapples with the complexities of her own identity amidst encounters with enigmatic extraterrestrial beings. Through an analysis of key characters and narrative themes, this study elucidates the nuanced portrayal of otherness and the role of gendered memories in shaping individual and collective identities. Butler's visionary narrative offers a thought-provoking examination of the intersections between memory, gender, and the construction of the self in a post-apocalyptic world. By delving into the philosophical underpinnings and ethical implications of otherness, this research aims to shed light on the intricate dynamics of identity formation and the quest for belonging in speculative fiction.

Was Shaw a Victorian? We Need to Ask Another Question

ABSTRACT Shaw scholars have long hesitated to label Shaw a Victorian writer. Although almost half of his life overlapped Queen Victoria’s reign, Shaw’s challenges to nineteenth-century morals and mores tend to mark him as an outlier. This article advocates a fresh approach: perhaps we have been taking the “Victorian” question too literally. Postmodern critics have long urged us to look for diverse and contradictory elements in literary works that seem to be staid and classical. In this article, the author looks for commonalities between Shaw’s Getting Married and three nineteenth-century novels (Eight Cousins, Great Expectations, and Jane Eyre). Do theatrical strategies and fictional writing techniques ever change places? Do the characters in Victorian novels ever explore Shavian ideas? And can we use those commonalities to rethink some of our assumptions about the Victorian era?

DIDS: Double Indices and Double Summarizations for Fast Similarity Search

Data series has been one of the significant data forms in various applications. It becomes imperative to devise a data series index that supports both approximate and exact similarity searches for large data series collections in high-dimensional metric spaces. The state-of-the-art works employ summarizations and indices to reduce the accesses to the data series. However, we discover two significant flaws that severely limit performance enhancement. Firstly, the state-of-the-art works often employ segment-based summarizations, whose lower bound distances decrease significantly when representing a data series collection, resulting in numerous invalid accesses. Secondly, the disk-based indices for the exact search mainly rely on tree-based indices, which results in low-quality approximate answers, consequently impacting the exact search. To address these problems, we propose a novel solution, Double Indices and Double Summarizations (DIDS). Besides segment-based summarizations, DIDS introduces reference-point-based summarizations to improve the pruning rate by the sorted-based representation strategy. Moreover, DIDS employs reference points and a cost model to cluster similar data series, and uses a graph-based approach to interconnect various regions, enhancing approximate search capabilities. We conduct experiments on extensive datasets, validating the superior search performance of DIDS.

Probing the content of affective semantic memory following caregiving-related early adversity.

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving-related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal-affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later-occurring positive experiences, child-perceived attachment security will inform their content. The sample comprised 160 children (ages 6-12 at Visit 1; 87F/73M), 66% of whom experienced crEAs (n=105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental-reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent-child separation); while child-reports assessed concurrent attachment security. A false memory task was administered online ∼2.5 years later (Visit 2) to probe the content of affective semantic memories-specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema-based false recognition. Findings suggest that interpersonal-affective semantic schemas include representations of parent-child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment-relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework. RESEARCH HIGHLIGHTS: Affective semantic memories representing insecure schema knowledge (child needs + needs-not-met) may be more salient, elaborated, and persistent among youths exposed to early caregiving adversity. All youths, irrespective of early caregiving adversity exposure, may possess affective semantic memories that represent knowledge of secure schemas (child needs + needs-met). Establishing secure relationships with parents following early-occurring caregiving adversity may attenuate the expression of insecure semantic memories, suggesting potential malleability. Affective semantic memories include schema representations of parent-child interactions that may capture the quality of one's own attachment experiences and shape how youths remember attachment-relevant events.

Ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning

RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.

Shared struggles, divergent paths: a comparison of grassroots and professional feminist advocates’ communication for social change in Argentina and the United States

Abstract Contemporary activism media research, largely focused on digital media's technological and discursive aspects, often lacks comparative studies and tends to overlook institutional or cultural factors in communication for social change (CSC). This study addresses these gaps by examining advocates’ sensemaking and communication praxis in contexts shaped by different advocacy traditions and sociocultural understandings of inequality. Through an analysis of 52 semi-structured interviews with Argentine grassroots advocates and U.S. professional ones, this study reveals similar media assessments across cases yet with different intersectional emphases: class in Argentina and race in the United States. Cross-case divergencies emerge in advocates’ positionality toward institutions and in their representational strategies: While grassroots advocates act as pragmatic agents of change and adopt a flexible communication style described as a “pedagogy of patience,” professional ones act as epistemic experts who offer a feminist critique of journalistic objectivity. I reflect on how these findings attend overlooked factors in CSC.

Modeling of limit order book data with ordered fuzzy numbers

This paper presents a novel approach to representing the Limit Order Book data at a given timestamp using the Ordered Fuzzy Numbers concept. The limit order book contains all buy and sell orders placed by investors, updated in real-time, for the most liquid securities, even several hundred times a minute. Due to its irregular nature (different and dynamic changes in the number of buy and sell orders), direct calculations on the order book data are not feasible without transforming it into feature vectors. Currently, most studies use a price level-based data representation scheme when applying deep learning models on limit order book data. However, this scheme has limitations, particularly its sensitivity to subtle perturbations that can negatively impact model performance. On the other hand, the ordered fuzzy number is a mathematical object (a pair of two functions) used to process imprecise and uncertain data. Ordered Fuzzy Numbers possess well-defined arithmetic properties. Converting the limit order book data to ordered fuzzy numbers allows the creation of a time series of ordered fuzzy numbers (order books) and use them for further calculations, e.g., to represent input data for deep learning models or employing the concept of fuzzy time series in various domains, such as defining liquidity measures based on limit order book data. In this paper, the proposed approach is tested using one-year market data from the Polish Stock Exchange for the five biggest companies. The DeepLOB model is employed to predict mid-price movement using different input data representations. The proposed representation of Limit Order Book data demonstrated remarkably stable out-of-sample prediction accuracy, even when subjected to data perturbation.

Deterministic and stochastic tendency adjustments derived from data assimilation and nudging

AbstractWe develop and compare model‐error representation schemes derived from data assimilation increments and nudging tendencies in multidecadal simulations of the Community Atmosphere Model, version 6. Each scheme applies a bias correction during simulation runtime to the zonal and meridional winds. We quantify the extent to which such online adjustment schemes improve the model climatology and variability on daily to seasonal timescales. Generally, we observe about a 30% improvement to annual upper‐level zonal winds, with largest improvements in boreal spring (around 35%) and winter (around 47%). Despite only adjusting the wind fields, we additionally observe around 20% improvement to annual precipitation over land, with the largest improvements in boreal fall (around 36%) and winter (around 25%), and around 50% improvement to annual sea‐level pressure, globally. With mean‐state adjustments alone, the dominant pattern of boreal low‐frequency variability over the Atlantic (the North Atlantic Oscillation) is significantly improved. Additional stochasticity increases the modal explained variances further, which brings the variability closer to the observed value. A streamfunction tendency decomposition reveals that the improvement is due to an adjustment to the high‐ and low‐frequency eddy–eddy interaction terms. In the Pacific, the mean‐state adjustment alone led to an erroneous deepening of the Aleutian low, but this was remedied with the addition of stochastically selected tendencies. Finally, from a practical standpoint, we discuss the performance of using data assimilation increments versus nudging tendencies for an online model‐error representation.

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.

Performing exemplarity: the “two faces” of a village cadre in North China

This article investigates the practice of exemplarity in rural China through Secretary Xu’s oral narratives to provincial leaders and local villagers in a northern model village. It applies a performative narrative analysis to reveal how Xu constructs his persona, utilizing narrative structures, chronotopes, and the interplay of events to forge exemplary images suited to each audience. In presentations to leaders, Xu highlights rural development achievements, while to villagers, he emphasizes common prosperity and community service. This strategic narrative manipulation not only showcases Xu’s skill in aligning with both state and local values but also fosters cultural intimacy and community cohesion despite narrative discrepancies. The case study expands the discourse on narrative techniques and representational strategies within rural revitalization and social governance, offering a nuanced perspective on the intersection of narrative, identity, and governance.

Impact of recognition on employees` performance in the Manufacturing Industries in Tanzania: A Case of Tanga Cement Company

Motivation works differently among employees in an organisation. While it is generally acceptable that monetary incentive is preferable among employees but there is a debate regarding non–financial incentives such as recognition on whether they impact employee performance. This study examined the impact of recognition on employees` performance in the manufacturing industries in Tanzania by using a case of Tanga Cement Company. In conducting this study, the researchers employed a descriptive research design. Furthermore, a simple random sampling method was used to select 50 respondents which enabled the collection of quantitative data through the use of questionnaires. In analysis, the study employed descriptive and inferential statistics to analyse the data with the aid of the Statistical Package of Social Science (SPSS). The study found that media representation boosts employees' desire to perform better, highlighting the importance of visibility and validation. Personal recognition fosters enhanced dedication, aligning with motivation theories. Receipt of recognition letters validates hard work, increasing job satisfaction and loyalty. Recognition programmes also lift employees' morale, influencing productivity and teamwork. Public recognition fosters commitment, and positive perceptions of work, workplace, and colleagues are crucial for a positive work environment. Furthermore, verbal appreciation from supervisors and co-workers is the most impactful form of recognition, as it is immediate and personal. Receiving a recognition certificate is also significant, as formal acknowledgement can enhance employees' work performance. The study recommends that implementing media representation strategies, establishing recognition programmes, and utilising tangible tokens of appreciation can be the best strategies to boost employee performance. This creates a positive work environment, enhances dedication, and boosts productivity by reinforcing appreciation and acknowledgement.

ISoLIM: a similarity-based spatial prediction software for the big data era

ABSTRACT Spatial prediction software is a valuable tool for predicting the spatial distribution of geographic variables and have a wide applicability with numerous non-expert users. However, as the input data to spatial prediction tasks gets more various in sources and larger in volume in the big data era, existing software are faced with challenges when processing such data on personal computers, which is one of the main platforms for running the software. This study uses the design and implementation of a similarity-based spatial prediction desktop software, the intelligent Soil Land Inference Model (iSoLIM), as an example to enable existing software to easily execute spatial prediction tasks with multi-source and large-volume data. The implemented similarity-based method was originally designed for digital soil mapping and has been applied to many other geographic variables. In the presented software, an expandable knowledge representation schema is adopted to intelligently manage multi-source knowledge data and enable knowledge integration; block division-based parallelization is adopted for intelligent computation arrangement on large-volume data. Experiments attest that, compared to existing software, iSoLIM can achieve higher accuracy from multi-source knowledge data by knowledge integration; and shows higher efficiency when processing data in large volume.

Disentangling Cerebellar and Parietal Contributions to Gait and Body Schema: A Repetitive Transcranial Magnetic Stimulation Study

The overlap between motor and cognitive signs resulting from posterior parietal cortex (PPC) and cerebellar lesions can mask their relative contribution in the sensorimotor integration process. This study aimed to identify distinguishing motor and cognitive features to disentangle PPC and cerebellar involvement in two sensorimotor-related functions: gait and body schema representation. Thirty healthy volunteers were enrolled and randomly assigned to PPC or cerebellar stimulation. Sham stimulation and 1 Hz-repetitive-Transcranial-Magnetic-Stimulation were delivered over P3 or cerebellum before a balance and a walking distance estimation task. Each trial was repeated with eyes open (EO) and closed (EC). Eight inertial measurement units recorded spatiotemporal and kinematic variables of gait. Instability increased in both groups after real stimulation: PPC inhibition resulted in increased instability in EC conditions, as evidenced by increased ellipse area and range of movement in medio-lateral and anterior–posterior (ROMap) directions. Cerebellar inhibition affected both EC (increased ROMap) and EO stability (greater displacement of the center of mass). Inhibitory stimulation (EC vs. EO) affected also gait spatiotemporal variability, with a high variability of ankle and knee angles plus different patterns in the two groups (cerebellar vs parietal). Lastly, PPC group overestimates distances after real stimulation (EC condition) compared to the cerebellar group. Stability, gait variability, and distance estimation parameters may be useful clinical parameters to disentangle cerebellar and PPC sensorimotor integration deficits. Clinical differential diagnosis efficiency can benefit from this methodological approach.

Modeling competences in enterprise architecture: from knowledge, skills, and attitudes to organizational capabilities

Competence-based approaches have received increased attention, as the demand for qualified people with the right combination of competences establishes itself as a major factor of organizational performance. This paper examines how competences can be incorporated into Enterprise Architecture modeling: (i) we identify a key set of competence-related concepts such as knowledge, skills, and attitudes, (ii) analyze and relate them using a reference ontology (grounded on the Unified Foundational Ontology), and (iii) propose a representation strategy for modeling competences and their constituent elements leveraging the ArchiMate language, discussing how the proposed models can fit in enterprise competence-based practices. Our approach is intended to cover two tasks relevant to the combined application of Enterprise Architecture and Competence Modeling: ‘zooming in’ on competences, revealing the relations between competences, knowledge, skills, attitudes and other personal characteristics that matter in organizational performance, and ‘zooming out’ of competences, placing them in the wider context of other personal competences and overall organizational capabilities. An assessment of the representation is offered in the form of an empirical survey.

More Than Tortillas

The Chicano-centric reboot of Party of Five (2020) exemplifies a postracial, postnetwork representational strategy to diversify mainstream media programming. While only ten episodes were produced and aired, several transgressive elements of Party of Five elevate the visibility and discursive representations of Chicanas/os/xs: the show problematizes dominant ideologies, complicates gender roles, offers nuanced constructions of immigrants, and depicts experiences of racialization and acculturation. Although the Chicano-centric storylines move beyond simply recasting main characters as Chicanas/ os/xs, the reboot was released during a racially dissonant television era that limited its run to a single season, curtailing the impact of its atypical representations. While the show’s ruptures upend television storytelling conventions that have excluded Chicanas/os/xs, its continuities and hasty cancellation reveal entrenched modes of storytelling that still uphold white centrality.