Flatten and conquer: a framework for efficient analysis of string constraints

ABSTRACTDiet‐related challenges in primary care and community settings increasingly reflect the convergence of health, social and environmental pressures. Although dietitians routinely navigate this complexity, their system‐level contributions often remain implicit. This paper proposes a conceptual framework that positions dietitians as system builders and synthesises insights from systems science, health promotion and ecological models to articulate this role. Three interconnected roles illustrate how dietitians influence system behaviour: as integrators, they embed nutrition within institutional routines, information flows and care pathways; as connectors, they strengthen relationships across health, social, education, and food system actors; and as advocates, they shape organisational priorities and policy agendas related to equity, sustainability, and health. The analysis identifies enabling conditions, including supportive organisational structures, intersectoral collaboration, relevant policy frameworks and enhanced competencies in systems thinking and equity. It also notes common constraints such as siloed systems, workload pressures and role overload. Several leverage points emerge where targeted action can exert significant system‐level impact (e.g. through changes in care‐pathway information flows or links to community food resources). Recognising and consolidating these system‐building roles can enhance the visibility, legitimacy and effectiveness of dietitians' contributions. A systems‐oriented professional identity, supported by education, practice environments and policy infrastructures, can strengthen the profession's ability to influence the conditions shaping dietary behaviour, equity and sustainability.

We present PIDGIN, a program analysis and understanding tool that enables the specification and enforcement of precise application-specific information security guarantees. PIDGIN also allows developers to interactively explore the information flows in their applications to develop policies and investigate counter-examples. PIDGIN combines program dependence graphs (PDGs), which precisely capture the information flows in a whole application, with a custom PDG query language. Queries express properties about the paths in the PDG; because paths in the PDG correspond to information flows in the application, queries can be used to specify global security policies. PIDGIN is scalable. Generating a PDG for a 330k line Java application takes 90 seconds, and checking a policy on that PDG takes under 14 seconds. The query language is expressive, supporting a large class of precise, application-specific security guarantees. Policies are separate from the code and do not interfere with testing or development, and can be used for security regression testing. We describe the design and implementation of PIDGIN and report on using it: (1) to explore information security guarantees in legacy programs; (2) to develop and modify security policies concurrently with application development; and (3) to develop policies based on known vulnerabilities.

Specialized Ribosomes: A New Frontier in Gene Regulation, Organismal Biology, & Evolution

The rapid evolution of the social media platforms has transformed the information flow in the virtual world radically. Viral contents, i.e. ones that reach the millions of people within several hours, are no longer a coincidence, but a symptom of extremely well established psychological mechanisms of human activity on the Internet. The paper will address psychological underpinnings of content virality on the social media with the idea of solving the mystery of why certain posts, videos, and campaigns go viral and others do not. The research applies the templates of STEPPS model (Berger, 2013), the Social Contagion Theory (Goldenberg and Gross, 2020) and the Optimal Distinctiveness Theory (Brewer, 1991) to the stimuli that trigger mass sharing behavior using the systematic review and the synthesis of the peer-reviewed articles and credible online researches. The findings reveal that the most effective factors that drive viral sharing are high-arousal emotions, awe in particular followed by humor and moral elevation, and social identity alignment and platform-native design are right behind them. The study does not only offer a multi-framework synthesis of articles about sharing behavior, but also offers strategic recommendations that digital marketing, brand managers, and platform developers can adhere to, so that they too could be able to design viral content in an effective and ethical manner in the already existing attention economy. The keywords that could be applied include Virality, Social Media Psychology, STEPPS Framework, Emotional Contagion, Digital Marketing Strategy, Social Sharing Behavior. 1 Introduction The social media has fundamentally altered its sources of information propagation that has produced the virality as content exits its target audience and spreads exponentially that in most instances reaches millions of viewers in hours. There are more than 5.2 billion social media users worldwide, however, the huge number of 500 million or so daily tweets, as well as the billion and one billion daily uploads of videos on Tik Tok, are not naturally trending (DataReportal, 2024). Viral content is interesting and thus, exposes sub and psychological needs and attracts to specific emotional drivers and motivators, and may not be only because of algorithm success. Business consequences are great. The ALS Ice Bucket trend reached a hundred and fifty-nine million dollars of donated money by sharing that was entirely user-driven in just eight weeks, which was entirely user-based (Pressgrove, McKeever & Jang, 2018). Spotify Wrapped generated 73.7 billion views on one hashtag on Tik Tok in 2023 ( Clemso University, 2025). Relying on the implementation of the sharing psychology, influencer marketing is a market worth 21 billion today, globally (Statista, 2024). In the wake of this business need, no uniform framework is behind success and failure of structurally analogous content going viral. Such gap is addressed in this paper through a systematic review of the literature of secondary research and a multi-framework analysis.

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

Optimizing manufacturing processes is vital to maintain global competitiveness. Organizations strive to enhance operations through diverse continuous improvement philosophies, yet face challenges in determining context-specific effectiveness. This paper will investigate key manufacturing methodologies, including Synchronous Manufacturing, Theory of Constraints, Lean Manufacturing, Design for Manufacturing, Just-In-Time (JIT), and Computer Integrated Manufacturing (CIM). This research provides a comparative analysis of each methodology's frameworks, strengths, and limitations. The results show that no universal "best" methodology exists and advocates for a nuanced approach, to deepen the understanding of their interconnectedness. Considering the integral role of data information flows in manufacturing optimization, the need for new methodologies that apply similar efficiency frameworks and philosophies to data and information flows is imperative to bridge the gap from physical systems to advanced data-driven cyber-physical systems.

We study the problem of evaluating xpath queries over xml data that is stored in an rdbms via schema-based shredding. The interaction between recursion (descendants-axis) in xpath queries and recursion in dtds makes it challenging to answer xpath queries using rdbms. We present a new approach to translating xpath queries into sql queries based on a notion of extended XP ath expressions and a simple least fixpoint (lfp) operator. Extended xpath expressions are a mild extension of xpath, and the lfp operator takes a single input relation and is already supported by most commercial rdbms. We show that extended xpath expressions are capable of capturing both dtd recursion and xpath queries in a uniform framework. Furthermore, they can be translated into an equivalent sequence of sql queries with the lfp operator. We present algorithms for rewriting xpath queries over a (possibly recursive) dtd into extended xpath expressions and for translating extended xpath expressions to sql queries, as well as optimization techniques. The novelty of our approach consists in its capability to answer a large class of xpath queries by means of only low-end rdbms features already available in most rdbms, as well as its flexibility to accommodate existing relational query optimization techniques. In addition, these translation algorithms provide a solution to query answering for certain (possibly recursive) xml views of xml data. Our experimental results verify the effectiveness of our techniques.

This study attempts to explore learning style in Linguistic courses in which learners respond to and use different kinds of stimuli in their learning. Unlike other discipline, in linguistics learning is a development process in which learners use their existing knowledge to make sense of the flow of new information. The objectives of this study were Investigate both students’ and teachers’ feelings towards active learning methods, explore factors that hinder the application of ALMs in Language classes, identify the prevalent practices, opportunities and threats of ALMs Language classes, and seek remedial solutions for the identified problems in linguistics courses. The research design of this study was qualitative and quantitative. To collect the important information for the research the researcher was used the instruments such as questionnaire, and systematic observation. To mention some the findings most of the instructors used different active learning methods specially group discussion, class discussion, and presentation to deliver their lesson in linguistic courses. In addition, there are teachers who used Bus Stop, Gallery, Individual work and Gold Fish Bowl methods in addition to the above four stated methods of teaching to deliver their lesson as needed. Lack of willingness and commitment from teachers and students, Most students’ negative attitude towards ALMs, Large class size, Lack of resources and facilities, Students’ poor academic background, and the influence of old method of teaching. The university should prepare different workshop to develop the knowledge of teachers and students on ALMs in general and linguistic courses in particular.

In the early phases of using system dynamics models to support strategic decision-making, the emphasis is on expressing information and physical flows. These aspects appropriately dominate those managed systems that can be thought of as being mechanistic. We suggest, however, that such an emphasis, to the exclusion of equally important system attributes such as power, leverage, influence and control, is inappropriate for a large class of problems involving agents and groups of agents in the system definition. Such politicized systems are ubiquitous, particularly in the strategic context, and in managing them it is necessary to take the political aspects of power into account at an early stage in the analysis. We present an approach to this class of problems, using a qualitative procedure based on influence diagrams. This method has been extensively and successfully used in consultancy to study the motivations and powers of agents and thereby produces naturally an output directed at action planning at the strategic level. While it is complementary to numerical system dynamics approaches, it is more successful in deriving components of strategic action directly from analysis.

Singapore Convention in Light of China’s Changing Mediation Scene

Visual attention is widely considered to be first and foremost a means of controlling the flow of information between different levels of processing. Stimuli selected by attention are thought to gain access to higher levels of processing, including awareness, recognition, memory, and the generation of voluntary responses, while stimuli rejected by attention are deemed to be denied such access. However, visual attention remains poorly understood in many aspects. In this study, we try to investigate some part of the problem. Several behavioral methods have been devised for the study of visual attention, one of them - the concurrent task paradigm - has gained more and more recognition and is widely used in different studies. In the first part of the thesis, we use this paradigm to investigate an important question: whether attentional capacity is differentiated or not. In non-human primates, neuronal sensitivities to stimulus dimensions such as form, color, or motion are concentrated in different visual cortical areas (Felleman and Van Essen, 1991; Van Essen and Gallant 1994), and functional imaging studies show similar functional specializations in the visual cortex of humans (Corbetta et al., 1990, 1995; Van Essen and Drury, 1997). It has been proposed that attentional capacity is "differentiated" (Sperling and Dosher, 1986; Pashler, 1997) in that discriminations concerning different dimensions draw at least in part on different attentional capacities and are thus less liable to interfere than discriminations concerning the same stimulus dimension. However, Duncan and colleagues have argued at length for a contrary point of view (Desimone and Duncan, 1995; Duncan et al., 1997). In their view, due to the strong interdependence of the subsystems that process different stimulus dimensions attentional capacity is "undifferentiated". By conducting experiments on the concurrent discrimination of form, color and motion attributes, we test which hypothesis is true. We quantify interference between concurrent discriminations by establishing the attention-operating characteristic. Interference is indistinguishable for similar and dissimilar task combinations (form-form, color-color, motion-motion, and color-form, color-motion, motion-color, motionform, respectively) for both demanding and less-demanding tasks. These results suggest strongly that different visual discriminations draw on the same attentional capacity, in other words, that the capacity of visual attention is undifferentiated. After establishing that a task is equally attentional demanding no matter what the stimulus dimension the second task is based on, we use the concurrent task paradigm to investigate how attention affects spatial vision. We measure thresholds for discriminating the contrast, orientation, and spatial frequency of simple patterns that are either fully or poorly attended. We observe differences of about 20% in contrast detection thresholds, 40-50% in contrast discrimination thresholds (and appearance of the “dipper”), 60-70% in orientation and spatial frequency discrimination thresholds, and up to 50% in contrast masking thresholds. These observations tightly constrain any effect attention may have on the visual filters and/or the interactions amongst filters that are thought to underlie basic spatial vision. Comparison with a computational model due to Laurent Itti shows that the observed effects of attention are consistent with stronger interactions amongst filters, but not with a change in noise parameters, as is sometimes thought (Bonnel and Miller, 1994; Lu and Dosher, 1998. Essentially, the effects of attention on different thresholds are too disparate to be accommodated by a single change in noise parameters. In the framework of our model, the strength of the interaction amongst filters is controlled by the exponents of a power law, γ and δ. The larger exponents activate what is best described as a winner-take-all competition amongst visual filters. Attention accentuates existing differences between filter responses, boosting filters that respond relatively well to a given stimulus, while suppressing filters that respond relatively poorly. This explains the perceptual advantage conferred by attention: attention enhances the sensory representation by restricting responses to the filters tuned best to the stimulus at hand. Our experimental and modeling results show that the activation of a winner-take-all competition amongst overlapping visual filters explains many basic perceptual consequences of attention.

Systems for deductive software verification model the semantics of their target programming language with full precision. On the other hand, abstraction based approaches work with approximations of the semantics in order to be fully automatic. In this paper we aim at providing a uniform framework for both fully precise and approximate reasoning about programs. We present a sound dynamic logic calculus that integrates abstraction in the sense of abstract interpretation theory. In the second part of the paper, we apply the approach to the analysis of secure information flow.

Information flow or causal interaction between neuronal populations of the brain is a critical issue in describing the dynamics of such a complex network, which can be best described by the illustrative features of graphical modeling. In this paper, we exploit the information-theoretic based causality detection measures to propose a uniform framework to derive a graphical model for the statistical analysis of multivariate processes from observed time series. Here, our main focus is on the efficient calculation of the measures for link estimation through searching for the most informative variables that drastically reduces the estimation dimension. We demonstrate the performance of our method for stationary processes using numerical simulations of nonlinear processes.

Text-based person retrieval (TBPR) aims to search for target person images from large-scale video clips or image databases based on textual descriptions. The quality of benchmarks is critical to accurately evaluating TBPR models for their ability in relation to cross-modal matching. However, we find that existing TBPR benchmarks have a common problem, which often leads to ambiguities where multiple images of persons with different identities have very similar or even identical textual descriptions. As a consequence, although TBPR models correctly retrieve the images corresponding to a given description, such matches may be erroneously evaluated as mismatches due to the above annotation problem. We argue that the main cause of this problem is that each person image is annotated individually without reference to other similar images, making it challenging to provide distinctive descriptions for each image. To address this problem, we propose an effective and efficient annotation refinement framework to improve the annotation quality of TBPR benchmarks and thereby mitigate annotation-induced mismatches. Firstly, sets of images prone to mismatches are automatically identified by TBPR models. Then, by leveraging multimodal large language models (MLLMs), multiple images are simultaneously processed and distinctive descriptions are generated for each image. Finally, the original descriptions are replaced to improve the annotation quality. Extensive experiments on three popular TBPR benchmarks (CUHK-PEDES, RSTPReid and ICFG-PEDES) validate the effectiveness of our proposed method for improving the quality of annotations, and demonstrate that the resulting more discriminative captions can truly benefit the mainstream TBPR models. The improved annotations of these benchmarks will be released publicly.

Complex systems are typically represented by large ensembles of observations. Correlation matrices provide an efficient formal framework to extract information from such multivariate ensembles and identify in a quantifiable way patterns of activity that are reproducible with statistically significant frequency compared to a reference chance probability, usually provided by random matrices as fundamental reference. The character of the problem and especially the symmetries involved must guide the choice of random matrices to be used for the definition of a baseline reference. For standard correlation matrices this is the Wishart ensemble of symmetric random matrices. The real world complexity however often shows asymmetric information flows and therefore more general correlation matrices are required to adequately capture the asymmetry. Here we first summarize the relevant theoretical concepts. We then present some examples of human brain activity where asymmetric time-lagged correlations are evident and hence highlight the need for further theoretical developments.