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Doping of a Mott insulator gives rise to a wide variety of exotic emergent states, from high-temperature superconductivity to charge, spin, and orbital orders. The physics underpinning their evolution is, however, poorly understood. A major challenge is the chemical complexity associated with traditional routes to doping. Here, we study the Mott insulating CrO2 layer of the delafossite PdCrO2, where an intrinsic polar catastrophe provides a clean route to doping of the surface. From scanning tunnelling microscopy and angle-resolved photoemission, we find that the surface stays insulating accompanied by a short-range ordered state. From density functional theory, we demonstrate how the formation of charge disproportionation results in an insulating ground state of the surface that is disparate from the hidden Mott insulator in the bulk. We demonstrate that voltage pulses induce local modifications to this state which relax over tens of minutes, pointing to a glassy nature of the charge order.

Stability issues in membrane-free coacervates have been addressed with coating strategies, but these approaches often compromise the permeability of the coacervate. Here we report a facile approach to maintain both stability and permeability using tannic acid and then demonstrate the value of this approach in enzyme-triggered drug release. First, we develop size-tunable coacervates via self-assembly of heparin glycosaminoglycan with tyrosine and arginine-based peptides. A thrombin-recognition site within the peptide building block results in heparin release upon thrombin proteolysis. Notably, polyphenols are integrated within the nano-coacervates to improve stability in biofluids. Phenolic crosslinking at the liquid-liquid interface enables nano-coacervates to maintain exceptional structural integrity across various environments. We discover a pivotal polyphenol threshold for preserving enzymatic activity alongside enhanced stability. The disassembly rate of the nano-coacervates increases as a function of thrombin activity, thus preventing a coagulation cascade. This polyphenol-based approach not only improves stability but also opens the way for applications in biomedicine, protease sensing, and bio-responsive drug delivery.

PURPOSE: Global health researchers have a responsibility to conduct ethical research in a manner that is culturally respectful and safe. The purpose of this work is to describe our experiences with recruitment and retention in Pakistan, a low-middle-income country. DESCRIPTION: We draw on two studies with a combined sample of 2161 low-risk pregnant women who participated in a pilot (n = 300) and a larger (n = 1861) prospective study of psychological distress and preterm birth at one of four centers (Garden, Hyderabad, Kharadar, Karimabad) of the Aga Khan University Hospital in Karachi, Pakistan. ASSESSMENT: Challenges we encountered include economic hardship and access to healthcare; womens position in the family; safety concerns and time commitment; misconceptions and mistrust in the research process; and concerns related to blood draws. To mitigate these challenges, we developed culturally acceptable study incentives, involved family members in the decision-making process about study participation, partnered with participants obstetrician-gynecologists, accommodated off site study visits, combined research visits with regular prenatal care visits, and modified research participation related to blood draws for some women. CONCLUSION: Implementation of these mitigation strategies improved recruitment and retention success, and we are confident that the solutions presented will support future scientists in addressing sociocultural challenges while embarking on collaborative research projects in Pakistan and other low-middle-income countries.

We examined whether selective attention, which is mainly theorized as the ability to focus on the category-relevant dimension, is a sole construct in understanding category learning. As the attention literature dissociates selective attention into focusing and filtering, we argue that filtering is another component that should be considered to fully understanding category learning. In the study, we provide an experimental paradigm that can dissociate filtering from focusing. By utilizing the paradigm along with collecting individual attention control measures, we show that filtering is related to the ability to inhibit irrelevant information. We also present that the current computational models that incorporate selective attention only as an ability to focus can not explain the results from the current study.

Nanostructured plasmonic surfaces allow for precise tailoring of electromagnetic modes within sub-diffraction mode volumes, boosting light–matter interactions. This study explores vibrational strong coupling (VSC) between molecular ensembles and subradiant “dark” cavities that support infrared quadrupolar plasmonic resonances (QPLs). The QPL mode exhibits a dispersion characteristic of bound states in the continuum (BIC). That is, the mode is subradiant or evanescent at normal incidence and acquires increasing “bright” dipole character with larger in-plane wavevectors. We deposited polymethyl methacrylate (PMMA) thin films on QPL substrates to induce VSC with the carbonyl stretch in PMMA and measured the resulting infrared (IR) spectra. Our computational analysis predicts the presence of “dark” subradiant polariton states within the near-field of the QPL mode, and “bright” collective molecular states. This finding is consistent with classical and quantum mechanical descriptions of VSC that predict hybrid polariton states with cavity-like modal character and N−1 collective molecular states with minimal cavity character. However, the behaviour is opposite of what is standardly observed in VSC experiments that use “bright” cavities, which results in “bright” polariton states that can be spectrally resolved as well as N−1 collective molecular states that are spectrally absent. Our experiments confirm a reduction of molecular absorption and other spectral signatures of VSC with the QPL mode. In comparison, our experiments promoting VSC with dipolar plasmonic resonances (DPLs) reproduce the conventional behavior. Our results highlight the significance of cavity mode symmetry in modifying the properties of the resultant states from VSC, while offering prospects for direct experimental probing of the N−1 molecule-like states that are usually spectrally “dark”.

In response to the growing demand for large-scale training data, synthetic datasets have emerged as practical solutions. However, existing synthetic datasets often fall short of replicating the richness and diversity of real-world data. Synthetic Playground (SynPlay) is introduced as a new synthetic human dataset that aims to bring out the diversity of human appearance in the real world. In this thesis, We focus on two factors to achieve a level of diversity that has not yet been seen in previous works: i) realistic human motions and poses and ii) multiple camera viewpoints towards human instances. We first use a game engine and its library-provided elementary motions to create games where virtual players can take less-constrained and natural movements while following the game rules (i.e., rule-guided motion design as opposed to detail-guided design). We then augment the elementary motions with real human motions captured with a motion capture device. To render various human appearances in the games from multiple viewpoints, we use seven virtual cameras encompassing the ground and aerial views, capturing abundant aerial-vs-ground and dynamic-vs-static attributes of the scene. Through extensive and carefully-designed experiments, we show that using SynPlay in model training leads to enhanced accuracy over existing synthetic datasets for human detection and segmentation. Moreover, the benefit of SynPlay becomes even greater for tasks in the data-scarce regime, such as few-shot and cross-domain learning tasks. These results clearly demonstrate that SynPlay can be used as an essential dataset with rich attributes of complex human appearances and poses suitable for model pretraining.

Secretory proteins are critically dependent on the correct processing of their signal sequence by the signal peptidase complex (SPC). This step, which is essential for the proper folding and localization of proteins in eukaryotic cells, is still not fully understood. In eukaryotes, the SPC comprises four evolutionarily conserved membrane subunits (Spc1-3 and Sec11). Here, we investigated the role of Spc2, examining SPC cleavage efficiency on various models and natural signal sequences in yeast cells depleted of or with mutations in Spc2. Our data show that discrimination between substrates and identification of the cleavage site by SPC is compromised when Spc2 is absent or mutated. Molecular dynamics simulation of the yeast SPC AlphaFold2-Multimer model indicates that membrane thinning at the center of SPC is reduced without Spc2, suggesting a molecular explanation for the altered substrate recognition properties of SPC lacking Spc2. These results provide new insights into the molecular mechanisms by which SPC governs protein biogenesis.

Background: The impact of patient-reported outcome measures on patient outcomes in longitudinal clinical studies is poorly understood. This observational study explored longitudinal changes in distress and problems reported by cancer patients screened and managed in accordance with a clinical pathway for anxiety and depression (ADAPT CP), implemented over 12 months. Methods: Patients reported distress using the Distress Thermometer and indicated reasons for distress using the 39-item Problem List across five domains: practical, social, emotional, spiritual/religious, and physical. Repeat screening occurred on average 3 monthly (quarterly). Results: Six hundred sixty patients from 10 participating services completed 1,256 screening events over 12 months, reporting 8,645 problems. On average, more emotional (27-34%) and physical (19-22%) issues were reported across all quarters than practical (7-9%) and social (8-9%) issues. Distress and emotional, physical, practical, and social problems reduced from initial to follow-up screens, although the decrease in emotional problems over time was not significantly different than that of the other problems. Worry, fatigue, sleep difficulties, health of family members, and insurance/finances were more persistent problems. Conclusions: Although distress and the change in the number of emotional concerns over time did not differ from other problems, rescreening is recommended within oncology settings to allow patients to indicate new or persistent problems and hospital staff to monitor and assess needs. Emotional concerns are high in oncology patients, suggesting the need for the prioritization of psychosocial care. These problems can persist over time due to their clinically challenging nature or because access to, or implementation of, evidence-based interventions are not yet widespread.

Funding Agencies|National Research Foundation of Korea (NRF) - Korea government (MSIT) [RS-2023-00248157]; Korean Ministry of Food and Drug Safety in 2024 [21153MFDS601]

Engaging second language (L2) students in multimodal academic writing that leverages multiple semiotic resources has the potential to foster their awareness of audience, purpose, and other rhetorical features. This case study explores L2 students’ engagement in a multimodal digital storytelling (DST) project. The study reports on how DST was integrated into an undergraduate writing curriculum and how students’ engagement in multimodal composition fostered their rhetorical awareness and influenced their perceptions of academic writing. The findings suggest that the process of transmediation (a process of translating meaning from one sign system into another) facilitated students’ revision, directing their attention to rhetorical features of writing. Creating digital stories enabled students to make sophisticated and deliberate rhetorical choices to bolster their arguments and transmit their messages effectively. Students exhibited positive attitudes toward the integration of multimedia. The results suggest that thoughtful integration of DST and multimodality have unique affordances for L2 students’ writing development.

The parent-child relationship is a primary influence on child development and health. Interpersonal parent-child interactions are also important for parents’ psychological well-being. This dissertation was comprised of three studies which aimed to investigate two interpersonal factors in the parent-child context: mindful parenting and biobehavioral synchrony. Study 1a is the first to investigate the psychometric properties of the Interpersonal Mindfulness in Parenting (IM-P) scale in a sample that included mothers and fathers. An exploratory analysis suggested that a five-factor structure, comprised of 27-items of the IM-P, was internally reliable and showed concurrent and convergent validity. Some of the mindful parenting domains varied by gender and current meditation practice, which may inform future intervention work. Using this same sample of parents, Study 1b found that higher scores on mindful parenting subscales relating to emotional awareness and compassion/self-regulation were associated with greater use of approach-focused coping. Non-judgmental acceptance in the parenting context was associated with greater odds of having low avoidant-focused coping behaviors. Avoidant-focused coping and non-judgmental acceptance were associated with depressive and anxiety symptoms, but in opposite directions. Specifically, parents with low avoidant-focused coping or who had higher non-judgmental acceptance had greater odds of not having high depressive and anxiety symptoms. These results indicate the potential protective qualities of using less avoidant-focused-coping and more non-judgmental acceptance in the parent-child context. In Study 2, over the course of a laboratory visit, two distinct trajectories of salivary cortisol, representing “high” and “low” groups, emerged for mothers and their five-year old children. Maternal and child cortisol trajectories were dependent on each other, and cortisol concentrations were correlated at each sampling time indicating there

Background: The impact of patient-reported outcome measures on patient outcomes in longitudinal clinical studies is poorly understood. This observational study explored longitudinal changes in distress and problems reported by cancer patients screened and managed in accordance with a clinical pathway for anxiety and depression (ADAPT CP), implemented over 12 months. Methods: Patients reported distress using the Distress Thermometer and indicated reasons for distress using the 39-item Problem List across five domains: practical, social, emotional, spiritual/religious, and physical. Repeat screening occurred on average 3 monthly (quarterly). Results: Six hundred sixty patients from 10 participating services completed 1,256 screening events over 12 months, reporting 8,645 problems. On average, more emotional (27-34%) and physical (19-22%) issues were reported across all quarters than practical (7-9%) and social (8-9%) issues. Distress and emotional, physical, practical, and social problems reduced from initial to follow-up screens, although the decrease in emotional problems over time was not significantly different than that of the other problems. Worry, fatigue, sleep difficulties, health of family members, and insurance/finances were more persistent problems. Conclusions: Although distress and the change in the number of emotional concerns over time did not differ from other problems, rescreening is recommended within oncology settings to allow patients to indicate new or persistent problems and hospital staff to monitor and assess needs. Emotional concerns are high in oncology patients, suggesting the need for the prioritization of psychosocial care. These problems can persist over time due to their clinically challenging nature or because access to, or implementation of, evidence-based interventions are not yet widespread.

Background: Severe flares (ALT >= 10xULN) are a well-recognised adverse outcome after nucleos(t)ide analogue (NA) cessation and may lead to liver failure. Thus, identification of patients at risk for these flares is of major importance. Methods: Data were used from two prospective studies on NA cessation conducted in the Netherlands and Canada. Patients were eligible based on EASL criteria. HBcrAg and anti-HBc levels were measured at end of treatment (EOT) and week 6 (FUW6). Logistic regression was used to study the association with severe flares. Results: Seventy-eight patients were analysed with a mean age of 49 years, 16 (21%) Caucasian and a majority (65%) were treated with Tenofovir. Overall, 22 patients (28%) developed a severe flare, and 29 (37%) patients were retreated. At EOT, higher HBcrAg levels (aOR: 1.97, p = 0.05; >= 4log: 47% severe flare vs. < 3log: 19%, p = 0.036), lower anti-HBc (aOR: 0.29, p = 0.036; < 2log: 50% vs. >= 3log: 11%, p = 0.029) and higher HBcrAg/anti-HBc-ratio (aOR: 3.17, p = 0.015; >= 2: 58% vs. < 1.5: 14%, p < 0.001) were associated with an increased risk of severe flares, adjusted for HBsAg. At FUW6, higher HBcrAg (aOR: 2.91, p = 0.035; >= 5log: 83%, < 3log: 4%, p < 0.001), lower anti-HBc (aOR: 0.46, p = 0.29; < 2log: 50% vs. >= 3log: 0%, p = 0.003) and higher HBcrAg/anti-HBc-ratio (aOR: 2.19, p = 0.048; >= 1.75: 52% vs. < 1.75: 8%, p < 0.001) were associated with an increased risk of severe flares, adjusted for HBV DNA and ALT. Conclusion: Higher HBcrAg, lower anti-HBc and higher HBcrAg/anti-HBc ratio at EOT and during the first weeks of post-treatment follow-up are associated with an increased risk of hepatic flares after NA withdrawal and could therefore potentially be used to select patients eligible for therapy cessation and to identify patients requiring retreatment. Trial Registrati

Human Accelerated Regions (HARs) are highly conserved across species but exhibit a significant excess of human-specific sequence changes, suggesting they may have gained novel functions in human evolution. HARs include transcriptional enhancers with human-specific activity and have been implicated in the evolution of the human brain. However, our understanding of how HARs contributed to uniquely human features of the brain is hindered by a lack of insight into the genes and pathways that HARs regulate. It is unclear whether HARs acted by altering the expression of gene targets conserved between HARs and their chimpanzee orthologs or by gaining new gene targets in human, a mechanism termed enhancer hijacking. We generated a high-resolution map of chromatin interactions for 1,590 HARs and their orthologs in human and chimpanzee neural stem cells (NSCs) to comprehensively identify gene targets in both species. HARs and their chimpanzee orthologs targeted a conserved set of 2,963 genes enriched for neurodevelopmental processes including neurogenesis and synaptic transmission. Changes in HAR enhancer activity were correlated with changes in conserved gene target expression. Conserved targets were enriched among genes differentially expressed between human and chimpanzee NSCs or between human and non-human primate developing and adult brain. Species-specific HAR gene targets did not converge on known biological functions and were not significantly enriched among differentially expressed genes, suggesting that HARs did not alter gene expression via enhancer hijacking. HAR gene targets, including differentially expressed targets, also showed cell type-specific expression patterns in the developing human brain, including outer radial glia, which are hypothesized to contribute to human cortical expansion. Our findings support that HARs influenced human brain evolution by altering the expression of conserved gene targets and provide the means to functionally link HARs with nov

Background Genetic changes that modify the function of transcriptional enhancers have been linked to the evolution of biological diversity across species. Multiple studies have focused on the role of nucleotide substitutions, transposition, and insertions and deletions in altering enhancer function. CpG islands (CGIs) have recently been shown to influence enhancer activity, and here we test how their turnover across species contributes to enhancer evolution. Results We integrate maps of CGIs and enhancer activity-associated histone modifications obtained from multiple tissues in nine mammalian species and find that CGI content in enhancers is strongly associated with increased histone modification levels. CGIs show widespread turnover across species and species-specific CGIs are strongly enriched for enhancers exhibiting species-specific activity across all tissues and species. Genes associated with enhancers with species-specific CGIs show concordant biases in their expression, supporting that CGI turnover contributes to gene regulatory innovation. Our results also implicate CGI turnover in the evolution of Human Gain Enhancers (HGEs), which show increased activity in human embryonic development and may have contributed to the evolution of uniquely human traits. Using a humanized mouse model, we show that a highly conserved HGE with a large CGI absent from the mouse ortholog shows increased activity at the human CGI in the humanized mouse diencephalon. Conclusions Collectively, our results point to CGI turnover as a mechanism driving gene regulatory changes potentially underlying trait evolution in mammals.

This study examined functional trajectories of subjects during the transition phase between ambulatory and non-ambulatory Duchenne muscular dystrophy (DMD) to inform clinical trial designs for new therapeutics. Ambulatory, pulmonary, and upper limb function leading up to loss of ambulation (LoA) and non-ambulatory measures following LoA were quantified; time ordering of pulmonary and upper limb milestones relative to LoA were determined; and the 10-second time threshold for 10-meter walk/run (10MWR) as a marker of approaching LOA was explored. Included in this analysis were 51 subjects aged between 7 and 18 years who experienced LoA during follow-up in the PRO-DMD-01 natural history study. Mean age at LoA was 12.7 (7.1-18.6) years. Mean annual rates of decline in forced vital capacity (FVC) <80%-predicted and performance of upper limb (PUL) 1.2 total score were smaller before than after LoA, but not significantly (FVC %-predicted: 5.6% vs. 10.1%, p = 0.21; PUL 1.2 total score: 2.3 vs. 3.8 units, p = 0.20). More than half of patients experienced clinically significant deficits in FVC %-predicted and PUL 1.2 before experiencing LoA. Among subjects with baseline 10MWR >10 s, those with <1 year to LoA had similar mean ages but significantly worse mean ambulatory function at baseline compared to those with ≥1 year to LoA. Enriching DMD clinical trials for patients with declining pulmonary or upper limb function is achievable without restricting enrollment to non-ambulatory patients. The sequencing of LoA and initial deficits in pulmonary and upper limb function varied across patients and highlights the potential for composite outcomes or multi-outcome trial designs to assess disease-modifying therapies more comprehensively.

Understanding the dynamics of hot, highly energetic electrons resulting from nonradiative plasmon decay is crucial for optimizing applications in photocatalysis and energy conversion. This study presents an analysis of electron kinetics within plasmonic metals, focusing on the steady-state behavior during continuous-wave (CW) illumination. Using an inelastic spectroscopy technique, we quantify the temperature and lifetimes of distinct carrier populations during excitation. A significant finding is the monotonic increase in hot electron lifetime with decreases in electronic temperature. We also observe a 1.22× increase in hot electron temperature during intraband excitation compared to interband excitation and a corresponding 2.34× increase in carrier lifetime. The shorter lifetimes during interband excitation are hypothesized to result from direct recombination of nonthermal holes and hot electrons, highlighting steady-state kinetics. Our results help bridge the knowledge gap between ultrafast and steady-state spectroscopies, offering critical insights for optimizing plasmonic applications.

Self-rated health (SRH) is a robust indicator of mortality, morbidity, and health behaviors. Despite consisting of a single item, SRH is a comprehensive measure that captures overall health status and encompasses biological, social, and psychological factors that influence health. Predictors of SRH across these domains are not well understood, particularly among young healthy individuals. Using a biopsychosocial approach, this dissertation explored in three separate studies biological markers of systems implicated in stress physiology (i.e., immune, autonomic, and endocrine), social determinants of health, and psychological factors that are relevant to SRH. Study 1 (N = 99) showed that higher levels of salivary (p = .011) and serum (p = .013) inflammatory markers predicted higher odds of reporting poor SRH, highlighting that inflammation is associated with worse SRH. Study 2 (N = 148) explored whether cardiovascular reactivity to stress, indexing activity of the sympathetic nervous system, and discrimination, a social determinant of health, were related to SRH. The study showed that increases in pre-ejection period (PEP) reactivity were associated with a 2% greater chance of reporting better SRH. Findings for the association between discrimination and SRH differed based on the participants’ ethnicity. Study 3 (N = 50) investigated endocrine markers in relation to SRH and documented that higher levels of placental corticotropin releasing hormone (pCRH) were associated with increased odds of poor SRH (p = .046) among a sample of pregnant Latinas. Everyday discrimination (p = .005), familism (p = .021) and positive trait emotion (p = .003) predicted lower odds of poor SRH. Findings from this dissertation provide direction for further examining SRH through a biopsychosocial lens. Implications for understanding the utility of SRH as a screening tool to identify individuals at risk for poor health outcomes are discussed.

This study examined functional trajectories of subjects during the transition phase between ambulatory and non-ambulatory Duchenne muscular dystrophy (DMD) to inform clinical trial designs for new therapeutics. Ambulatory, pulmonary, and upper limb function leading up to loss of ambulation (LoA) and non-ambulatory measures following LoA were quantified; time ordering of pulmonary and upper limb milestones relative to LoA were determined; and the 10-second time threshold for 10-meter walk/run (10MWR) as a marker of approaching LOA was explored. Included in this analysis were 51 subjects aged between 7 and 18 years who experienced LoA during follow-up in the PRO-DMD-01 natural history study. Mean age at LoA was 12.7 (7.1–18.6) years. Mean annual rates of decline in forced vital capacity (FVC) <80%-predicted and performance of upper limb (PUL) 1.2 total score were smaller before than after LoA, but not significantly (FVC %-predicted: 5.6% vs. 10.1%, p = 0.21; PUL 1.2 total score: 2.3 vs. 3.8 units, p = 0.20). More than half of patients experienced clinically significant deficits in FVC %-predicted and PUL 1.2 before experiencing LoA. Among subjects with baseline 10MWR >10 s, those with <1 year to LoA had similar mean ages but significantly worse mean ambulatory function at baseline compared to those with 1 year to LoA. Enriching DMD clinical trials for patients with declining pulmonary or upper limb function is achievable without restricting enrollment to non-ambulatory patients. The sequencing of LoA and initial deficits in pulmonary and upper limb function varied across patients and highlights the potential for composite outcomes or multi-outcome trial designs to assess disease-modifying therapies more comprehensively., SCOPUS: ar.j, info:eu-repo/semantics/published

Background: The impact of patient-reported outcome measures on patient outcomes in longitudinal clinical studies is poorly understood. This observational study explored longitudinal changes in distress and problems reported by cancer patients screened and managed in accordance with a clinical pathway for anxiety and depression (ADAPT CP), implemented over 12 months. Methods: Patients reported distress using the Distress Thermometer and indicated reasons for distress using the 39-item Problem List across five domains: practical, social, emotional, spiritual/religious, and physical. Repeat screening occurred on average 3 monthly (quarterly). Results: Six hundred sixty patients from 10 participating services completed 1,256 screening events over 12 months, reporting 8,645 problems. On average, more emotional (27–34%) and physical (19–22%) issues were reported across all quarters than practical (7–9%) and social (8–9%) issues. Distress and emotional, physical, practical, and social problems reduced from initial to follow-up screens, although the decrease in emotional problems over time was not significantly different than that of the other problems. Worry, fatigue, sleep difficulties, health of family members, and insurance/finances were more persistent problems. Conclusions: Although distress and the change in the number of emotional concerns over time did not differ from other problems, rescreening is recommended within oncology settings to allow patients to indicate new or persistent problems and hospital staff to monitor and assess needs. Emotional concerns are high in oncology patients, suggesting the need for the prioritization of psychosocial care. These problems can persist over time due to their clinically challenging nature or because access to, or implementation of, evidence-based interventions are not yet widespread.

Doping of a Mott insulator gives rise to a wide variety of exotic emergent states, from high-temperature superconductivity to charge, spin, and orbital orders. The physics underpinning their evolution is, however, poorly understood. A major challenge is the chemical complexity associated with traditional routes to doping. Here, we study the Mott insulating CrO2 layer of the delafossite PdCrO2, where an intrinsic polar catastrophe provides a clean route to doping of the surface. From scanning tunnelling microscopy and angle-resolved photoemission, we find that the surface stays insulating accompanied by a short-range ordered state. From density functional theory, we demonstrate how the formation of charge disproportionation results in an insulating ground state of the surface that is disparate from the hidden Mott insulator in the bulk. We demonstrate that voltage pulses induce local modifications to this state which relax over tens of minutes, pointing to a glassy nature of the charge order.

Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.

Diagnosis of eutrophication requires evidence of disturbance to the balance of organisms. We describe a tool, the Plankton Community Integrity Index (PCII), derived from the Plankton Index (PI) for tracking change in the seasonal patterns of abundance of diatom and dinoflagellate lifeforms when plotted in state space. The tool uses a nutrient-minimum reference period to interpret PCII values as status indicators, with values close to 1 indicating “High” status and 0.6 a Biological Water Quality Criterion (BioWQC) target set at the “Fair”/“Good” status boundary. It has been applied to Hong Kong marine waters, using data from monthly samples from 1995 through 2021. A preliminary analysis, required for the PI method, confirmed monsoonal seasonality in the diatom lifeform. In 5 of the 9 water bodies examined, PCII time series correlated with those of Total Inorganic Nitrogen (TIN). Since 2020, all Water Control Zones met the operationally defined BioWQC target. © 2024 Elsevier Ltd

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85–0.96 and 0.62–0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies. © 2023 The Authors

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant–microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

BACKGROUND: Migrants from hepatitis B virus (HBV) endemic regions are at high risk of having chronic infection. Despite this, HBV knowledge and awareness programming, and low-barrier screening methods such as point of care (POC) testing, among this group have yet to become routine. METHODS: We conducted a mass HBV POC screening and knowledge and awareness campaign for individuals of Chinese descent in Toronto, Canada. POC screening was administered, then participants completed a knowledge questionnaire. Logistic regression identified associations between demographic factors and participants’ level of HBV knowledge. RESULTS: From 2015 to 2018, 33 outreach events resulted in 891 individuals completing testing and the knowledge questionnaire. Individuals averaged 64.4 years old. Most, 62% (N = 552), were female, and 73.6% (N = 656) have been in Canada for <30 years. The average questionnaire score was 70.7% correct, with 65.2% (N = 581) demonstrating a high level of HBV knowledge. Post-secondary education (OR: 2.19, 95% CI: 1.41, 3.39), income of $50,000 to <$75,000 (OR: 2.74, 95% CI: 1.39, 5.43), and having familial history of HBV (OR: 1.72, 95% CI: 1.06, 2.78) were associated with high knowledge. The observed prevalence of HBV was 1.5%, with 13 individuals testing positive on the POC test and confirmatory laboratory testing. CONCLUSIONS: Improving knowledge and awareness of HBV is critical to empowering people, especially migrants who experience barriers to care, to pursue vaccination, testing, and treatment. Combining knowledge outreach and POC test campaigns, enabled discussion and screening for HBV with large numbers of people, and can be tailored for optimal effectiveness for specific groups.

Gallium arsenide (GaAs) semiconductor wires have emerged as potent candidates for nonlinear optical devices, necessitating bandgap engineering for an expanded operational wavelength range. We report the successful growth of strain-mediated GaAs microwires (MWs) with an average diameter of 1.1 mu m. The axial tensile strain in these wires, as measured by X-ray diffraction and Raman scattering, ranges from 1.61 % to 1.95 % and from 1.44 % to 2.03 %, respectively. This strain condition significantly reduces the bandgap of GaAs MWs compared to bulk GaAs, enabling a response wavelength extension up to 1.1 mu m. Open aperture Z-scan measurements reveal a nonlinear absorption coefficient of -15.9 cm/MW and a third-order magnetic susceptibility of -2.8 x 10(-8) esu at 800 nm for these MWs. I-scan measurements further show that the GaAs saturable absorber has a modulation depth of 7.9 % and a nonsaturation loss of 3.3 % at 1050 nm. In laser applications, GaAs MWs have been effectively used as saturable absorbers for achieving Q-switched and dual-wavelength synchronous mode-locking operations in Yb-bulk lasers. These results not only offer new insights into the use of large diameter semiconductor wires but also expand the potential for applications requiring bandgap tuning.

Soldiers in the field often need to cross negative obstacles, such as rivers or canyons, to reach goals or safety. Military gap crossing involves on-site temporary bridges construction. However, this procedure is conducted with dangerous, time and labor intensive operations, and specialized machinery. We envision a scalable robotic solution inspired by advancements in force-controlled and Cable Driven Parallel Robots (CDPRs); this solution can address the challenges inherent in this transportation problem, achieving fast, efficient, and safe deployment and field operations. We introduce the embodied vision in Co3MaNDR, a solution to the military gap crossing problem, a distributed robot consisting of several modules simultaneously pulling on a central payload, controlling the cables' tensions to achieve complex objectives, such as precise trajectory tracking or force amplification. Hardware experiments demonstrate teleoperation of a payload, trajectory following, and the sensing and amplification of operators' applied physical forces during slow operations. An operator was shown to manipulate a 27.2 kg (60 lb) payload with an average force utilization of 14.5\% of its weight. Results indicate that the system can be scaled up to heavier payloads without compromising performance or introducing superfluous complexity. This research lays a foundation to expand CDPR technology to uncoordinated and unstable mobile platforms in unknown environments., Comment: 8 pages, 9 figures. Submit to IROS 2024

Cybersecurity information is often technically complex and relayed through unstructured text, making automation of cyber threat intelligence highly challenging. For such text domains that involve high levels of expertise, pretraining on in-domain corpora has been a popular method for language models to obtain domain expertise. However, cybersecurity texts often contain non-linguistic elements (such as URLs and hash values) that could be unsuitable with the established pretraining methodologies. Previous work in other domains have removed or filtered such text as noise, but the effectiveness of these methods have not been investigated, especially in the cybersecurity domain. We propose different pretraining methodologies and evaluate their effectiveness through downstream tasks and probing tasks. Our proposed strategy (selective MLM and jointly training NLE token classification) outperforms the commonly taken approach of replacing non-linguistic elements (NLEs). We use our domain-customized methodology to train CyBERTuned, a cybersecurity domain language model that outperforms other cybersecurity PLMs on most tasks., Comment: To appear in NAACL Findings 2024

Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processing quantum information. Here, we propose and experimentally demonstrate a programmable metasurface capable of performing quantum algorithms using both classical and quantum light with single photons. Our approach encodes multiple programmable quantum algorithms and operations, such as Grover's search algorithm and the quantum Fourier transform, onto the same metalens array on a metasurface. A spatial light modulator selectively excites different sets of metalenses to carry out the quantum algorithms, while the interference patterns captured by a single-photon camera are used to extract information about the output state at the selected output directions. Our programmable quantum metasurface approach holds promising potential as a cost-effective means of miniaturizing components for quantum computing and information processing.

Text classification is the task of assigning a document to a predefined class. However, it is expensive to acquire enough labeled documents or to label them. In this paper, we study the regularization methods' effects on various classification models when only a few labeled data are available. We compare a simple word embedding-based model, which is simple but effective, with complex models (CNN and BiLSTM). In supervised learning, adversarial training can further regularize the model. When an unlabeled dataset is available, we can regularize the model using semi-supervised learning methods such as the Pi model and virtual adversarial training. We evaluate the regularization effects on four text classification datasets (AG news, DBpedia, Yahoo! Answers, Yelp Polarity), using only 0.1% to 0.5% of the original labeled training documents. The simple model performs relatively well in fully supervised learning, but with the help of adversarial training and semi-supervised learning, both simple and complex models can be regularized, showing better results for complex models. Although the simple model is robust to overfitting, a complex model with well-designed prior beliefs can be also robust to overfitting., Comment: 13 pages, 2 figures

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously., Comment: 13 pages, 7 figures

Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provides the missing link in enabling flow-based generative models to be applied to multimodal continuous and discrete data problems. Our key insight is that the discrete equivalent of continuous space flow matching can be realized using Continuous Time Markov Chains. DFMs benefit from a simple derivation that includes discrete diffusion models as a specific instance while allowing improved performance over existing diffusion-based approaches. We utilize our DFMs method to build a multimodal flow-based modeling framework. We apply this capability to the task of protein co-design, wherein we learn a model for jointly generating protein structure and sequence. Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure., Comment: 60 pages, 11 figures, 6 tables; ICML 2024

To ensure the proper functioning of the current and future electrical grid, it is necessary for Transmission System Operators (TSOs) to verify that energy providers comply with the grid code and specifications provided by TSOs. A lot of energy production are conntected to the grid through a power electronic inverter. Grid Forming (GFM) and Grid Following (GFL) are the two types of operating modes used to control power electronic converters. The choice of control mode by TSOs to avoid impacting the stability of the grid is crucial, as is the commitment to these choices by energy suppliers. This article proposes a comparison between commonplace machine learning algorithms for converter control mode classification: GFL or GFM. The classification is based on frequency-domain admittance obtained by external measurement methods. Most algorithms are able to classify accurately when the control structure belongs to the training data, but they fail to classify modified control structures with the exception of the random forest algorithm.

Protein design often begins with knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a diverse range of motifs. However, the generated scaffolds tend to lack structural diversity, which can hinder success in wet-lab validation. In this work, we extend FrameFlow, an SE(3) flow matching model for protein backbone generation, to perform motif-scaffolding with two complementary approaches. The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy. The second is motif guidance, which performs scaffolding using an estimate of the conditional score from FrameFlow, and requires no additional training. Both approaches achieve an equivalent or higher success rate than previous state-of-the-art methods, with 2.5 times more structurally diverse scaffolds. Code: https://github.com/ microsoft/frame-flow., Comment: Preprint. Code: https://github.com/ microsoft/frame-flow

Task-oriented queries (e.g., one-shot queries to play videos, order food, or call a taxi) are crucial for assessing the quality of virtual assistants, chatbots, and other large language model (LLM)-based services. However, a standard benchmark for task-oriented queries is not yet available, as existing benchmarks in the relevant NLP (Natural Language Processing) fields have primarily focused on task-oriented dialogues. Thus, we present a new methodology for efficiently generating the Task-oriented Queries Benchmark (ToQB) using existing task-oriented dialogue datasets and an LLM service. Our methodology involves formulating the underlying NLP task to summarize the original intent of a speaker in each dialogue, detailing the key steps to perform the devised NLP task using an LLM service, and outlining a framework for automating a major part of the benchmark generation process. Through a case study encompassing three domains (i.e., two single-task domains and one multi-task domain), we demonstrate how to customize the LLM prompts (e.g., omitting system utterances or speaker labels) for those three domains and characterize the generated task-oriented queries. The generated ToQB dataset is made available to the public. We further discuss new domains that can be added to ToQB by community contributors and its practical applications., Comment: Data available on GitHub, https://github.com/google/task-oriented-queries

Training large language models (LLMs) is known to be challenging because of the huge computational and memory capacity requirements. To address these issues, it is common to use a cluster of GPUs with 3D parallelism, which splits a model along the data batch, pipeline stage, and intra-layer tensor dimensions. However, the use of 3D parallelism produces the additional challenge of finding the optimal number of ways on each dimension and mapping the split models onto the GPUs. Several previous studies have attempted to automatically find the optimal configuration, but many of these lacked several important aspects. For instance, the heterogeneous nature of the interconnect speeds is often ignored. While the peak bandwidths for the interconnects are usually made equal, the actual attained bandwidth varies per link in real-world clusters. Combined with the critical path modeling that does not properly consider the communication, they easily fall into sub-optimal configurations. In addition, they often fail to consider the memory requirement per GPU, often recommending solutions that could not be executed. To address these challenges, we propose Pipette, which is an automatic fine-grained LLM training configurator for real-world clusters. By devising better performance models along with the memory estimator and fine-grained individual GPU assignment, Pipette achieves faster configurations that satisfy the memory constraints. We evaluated Pipette on large clusters to show that it provides a significant speedup over the prior art. The implementation of Pipette is available at https://github.com/yimjinkyu1/date2024_pipette., Comment: published at DATE 2024

This paper presents a framework that selectively triggers security reviews for incoming source code changes. Functioning as a review bot within a code review service, the framework can automatically request additional security reviews at pre-submit time before the code changes are submitted to a source code repository. Because performing such secure code reviews add cost, the framework employs a classifier trained to identify code changes with a high likelihood of vulnerabilities. The online classifier leverages various types of input features to analyze the review patterns, track the software engineering process, and mine specific text patterns within given code changes. The classifier and its features are meticulously chosen and optimized using data from the submitted code changes and reported vulnerabilities in Android Open Source Project (AOSP). The evaluation results demonstrate that our Vulnerability Prevention (VP) framework identifies approximately 80% of the vulnerability-inducing code changes in the dataset with a precision ratio of around 98% and a false positive rate of around 1.7%. We discuss the implications of deploying the VP framework in multi-project settings and future directions for Android security research. This paper explores and validates our approach to code change-granularity vulnerability prediction, offering a preventive technique for software security by preemptively detecting vulnerable code changes before submission., Comment: This is a preprint of an article that has been submitted to a journal for publication

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images and images of facial line segments and structured data - coordinates of facial landmarks and features of facial expressions), the feed-forward neural network using features of facial expression achieved the highest precision of 76.22 while the ResNet-based model using images of facial line segments achieved the highest recall of 83.47. When we leveraged both images of facial line segments and features of facial expressions, our multimodal fusion-based deep learning model slightly improved the precision score to 77.05 at the expense of a decrease in the recall score.

Communication and position sensing are among the most important capabilities for swarm robots to interact with their peers and perform tasks collaboratively. However, the hardware required to facilitate communication and position sensing is often too complicated, expensive, and bulky to be carried on swarm robots. Here we present Maneuverable Piccolissimo 3 (MP3), a minimalist, single motor drone capable of executing inter-robot communication via infrared light and triangulation-based sensing of relative bearing, distance, and elevation using message arrival time. Thanks to its novel design, MP3 can communicate with peers and localize itself using simple components, keeping its size and mass small and making it inherently safe for human interaction. We present the hardware and software design of MP3 and demonstrate its capability to localize itself, fly stably, and maneuver in the environment using peer-to-peer communication and sensing.

The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application. To tackle this challenge, recent research focuses on methods to reduce sampling steps, such as Latent Consistency Model (LCM), and on employing architectural optimizations, including pruning and knowledge distillation. Diverging from existing approaches, we uniquely start with a compact SD variant, BK-SDM. We observe that directly applying LCM to BK-SDM with commonly used crawled datasets yields unsatisfactory results. It leads us to develop two strategies: (1) leveraging high-quality image-text pairs from leading generative models and (2) designing an advanced distillation process tailored for LCM. Through our thorough exploration of quantization, profiling, and on-device deployment, we achieve rapid generation of photo-realistic, text-aligned images in just two steps, with latency under one second on resource-limited edge devices., Comment: 4 pages, accepted to CVPR24 First Workshop on Efficient and On-Device Generation (EDGE)

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms which allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints such as batteries. Robots generate a path through a shape such that robots cycle in and out of the shape. Robots inside the shape react to human initiated changes and adapt the path through the shape accordingly. Robots outside the shape recharge and return to the shape so that the shape can persist indefinitely. The presented algorithms communicate shape changes throughout the swarm using message passing and robot motion. These algorithms enable the swarm to persist through any arbitrary changes to the shape. We describe these algorithms in detail and present their performance in simulation and on a swarm of mobile robots. The result is a swarm behavior more suitable for extended duration, dynamic shape-based tasks in applications such as agriculture and emergency response., Comment: 20 pages, 17 figures

Molecular datasets often suffer from a lack of data. It is well-known that gathering data is difficult due to the complexity of experimentation or simulation involved. Here, we leverage mutual information across different tasks in molecular data to address this issue. We extend an algorithm that utilizes the geometric characteristics of the encoding space, known as the Geometrically Aligned Transfer Encoder (GATE), to a multi-task setup. Thus, we connect multiple molecular tasks by aligning the curved coordinates onto locally flat coordinates, ensuring the flow of information from source tasks to support performance on target data., Comment: 7 pages, 3 figures, 2 tables

In this study, we formulate an OCR-free sequence generation model for visual document understanding (VDU). Our model not only parses text from document images but also extracts the spatial coordinates of the text based on the multi-head architecture. Named as Coordinate-aware End-to-end Document Parser (CREPE), our method uniquely integrates these capabilities by introducing a special token for OCR text, and token-triggered coordinate decoding. We also proposed a weakly-supervised framework for cost-efficient training, requiring only parsing annotations without high-cost coordinate annotations. Our experimental evaluations demonstrate CREPE's state-of-the-art performances on document parsing tasks. Beyond that, CREPE's adaptability is further highlighted by its successful usage in other document understanding tasks such as layout analysis, document visual question answering, and so one. CREPE's abilities including OCR and semantic parsing not only mitigate error propagation issues in existing OCR-dependent methods, it also significantly enhance the functionality of sequence generation models, ushering in a new era for document understanding studies., Comment: Accepted at the International Conference on Document Analysis and Recognition (ICDAR 2024) main conference

In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. It is important to note that existing datasets have not incorporated images or QA pairs, which are two crucial components of TableVQA. As such, the primary objective of this paper is to obtain these necessary components. Specifically, images are sourced either through the application of a \textit{stylesheet} or by employing the proposed table rendering system. QA pairs are generated by exploiting the large language model (LLM) where the input is a text-formatted table. Ultimately, the completed TableVQA-Bench comprises 1,500 QA pairs. We comprehensively compare the performance of various multi-modal large language models (MLLMs) on TableVQA-Bench. GPT-4V achieves the highest accuracy among commercial and open-sourced MLLMs from our experiments. Moreover, we discover that the number of vision queries plays a significant role in TableVQA performance. To further analyze the capabilities of MLLMs in comparison to their LLM backbones, we investigate by presenting image-formatted tables to MLLMs and text-formatted tables to LLMs, respectively. Our findings suggest that processing visual inputs is more challenging than text inputs, as evidenced by the lower performance of MLLMs, despite generally requiring higher computational costs than LLMs. The proposed TableVQA-Bench and evaluation codes are available at \href{https://github.com/naver-ai/tablevqabench}{https://github.com/naver-ai/tablevqabench}., Comment: Technical Report

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that directly replicate information from the text, limiting their applicability in broader contexts, as text-to-table generation in real-life scenarios necessitates information extraction, reasoning, and integration. However, there is a lack of both datasets and methodologies towards this task. In this paper, we introduce LiveSum, a new benchmark dataset created for generating summary tables of competitions based on real-time commentary texts. We evaluate the performances of state-of-the-art LLMs on this task in both fine-tuning and zero-shot settings, and additionally propose a novel pipeline called $T^3$(Text-Tuple-Table) to improve their performances. Extensive experimental results demonstrate that LLMs still struggle with this task even after fine-tuning, while our approach can offer substantial performance gains without explicit training. Further analyses demonstrate that our method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. Our code and data can be found at https://github.com/HKUST-KnowComp/LiveSum-TTT.