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To improve outcomes of their citizens, it is essential for developing states to accurately measure outcomes and increase state capacity to deliver well-designed transfer-programs.Chapter 1 focuses on subsidies to improve housing in urban slums---a core sustainable development goal. Public programs require complementary private investments making their incidence potentially regressive. I study the incidence and impact of India's flagship house construction grant for slum dwellers to construct a house on their own land by combining a novel identification strategy and administrative and survey data. The opportunity cost of giving up their existing house to take up the benefits makes the incidence of the program progressive. In turn this leads to a large impact on the quality of house the slum dwellers live in. Using a theoretical framework we see that holding the budget fixed increasing grant size can increase welfare benefits.Chapter 2 focuses on independent measures of health outcomes during a pandemic. Death registries and death counts are unreliable in developing countries due to limited testing for viruses and low capacity to register deaths. All-cause excess deaths in India are estimated using the member rosters of a large, representative household panel survey. We estimate roughly 4.2 million excess deaths during the pandemic through February 2022. The timing and age structure of deaths is consistent with the COVID pandemic. Excess deaths were higher among higher-income persons.Chapter 3 focuses on improving access to health providers in remote districts. Distribution of high-skill providers is skewed towards larger urban areas. We study the possibility to train the local workforce in remote districts to become providers themselves. Using a two-way fixed-effect model we study the 5x growth in the number of medical colleges in India. One additional batch of students graduating from a medical college in a district is associated with a 6.3pp increase in the number

This dissertation consists of two main topics. In the first one, we talk about an equivariant reformulation of the plus part of the Main Conjecture of Iwasawa theory in terms of the abstract $p$-adic Tate module constructed by Greither and Popescu(\cite{GP}) and the cyclotomic units of $\Q.$ Then we discuss how the Selmer module, introduced by Burns, Kurihara and Sano(\cite{BKS}) could be an unconditional replacement for the $p$-adic Tate module. In the second part, we talk about how the Euler factors in equivariant $L$-functions of Drinfeld modules relate to Fitting ideals of certain modules. This discussion takes us through the theory of $t$-motives and helps in defining the first ``\'etale" and ``crystalline" cohomology groups of a Drinfeld module defined over a finite field.

Introduction: Perioperative opioid use has been associated with adverse clinical outcomes. Additionally, opioid disposal carries significant costs, due to the waste of pharmaceutical products and the time needed by skilled labor to report the waste. In this study, we aimed to estimate costs and predict factors of opioid-associated intraoperative product waste, as well as to evaluate whether higher intraoperative opioid doses are associated with increased risk of adverse postoperative outcomes. Methods: We included 170,607 patients undergoing general anesthesia and receiving intraoperative fentanyl, hydromorphone, or morphine at Beth Israel Deaconess Medical Center, Boston, MA, USA, between January 2010 and June 2020. We estimated product waste-associated costs based on various opioid syringe sizes and determined predictors of opioid waste. Further, we evaluated whether higher opioid doses were associated with postoperative adverse events according to the severity-indexed, incident report-based medication error-reporting program classification. The primary outcome included post-extubation desaturation, postoperative nausea or vomiting, or postoperative somnolence or sedation. Results: The use of the smallest syringe sizes (50 mcg for fentanyl, 0.2 mg for hydromorphone, and 2 mg for morphine) resulted in the lowest product waste-associated costs. The main predictor of opioid waste was the administration of more than one intraoperative opioid (adjusted odds ratio [aOR] = 7.64, 95% CI 7.40-7.89, P < 0.001). Intraoperative doses of fentanyl > 50-100 mcg (aOR = 1.17 [1.10-1.25], P < 0.001, adjusted risk difference [ARD] 2%) and > 100 mcg (aOR = 1.24 [1.16-1.33], P < 0.001, ARD 3%), hydromorphone > 1 mg (aOR = 1.13 [1.06-1.20], P < 0.001, ARD 2%), and morphine > 2-4 mg (aOR = 1.26 [1.02-1.56], P = 0.04, ARD 3%) and > 4 mg (aOR = 1.45 [1.18-1.77], P < 0.001, ARD 5%) were associated with higher risk of the primary outcom

The January 6 insurrection at the Capitol seemed to mark a new peak in extremist intimidation targeting public officials. But it was hardly the only act of political violence to break the period of relative stability that followed the assassinations of the 1960s. There was the 2017 shooting of U.S. Rep. Steve Scalise and colleagues by a Trump detractor. There was the hammer attack on U.S. Rep. Nancy Pelosi's husband by a right-wing conspiracy theorist who sought the then House speaker in her home. Then there were threats by Republican extremists against Republican members of Congress for refusing to support their preferred candidate for speaker. These acts grabbed headlines and spurred increases in security for federal officials.Yet over the same period, with far less attention and often little recourse, officeholders serving in local and state government across the country have faced a barrage of intimidating abuse. Threats and attacks constrain how freely officeholders interact with constituents, narrow the spectrum of policy positions they feel safe to support, and make them less willing to continue in public service. Unaddressed, the problem stands to endanger not just individual politicians but, more broadly, the free and fair functioning of representative democracy — at every level of government."Last fall was the last really serious death threat I got," one state legislator told the Brennan Center. "It was like date, time, location specific. They were going to kill me and then go to the police station and blow themselves up and take as many officers with them as possible."A series of national surveys completed in October 2023 — one of state legislators and four quarterly surveys of local officeholders — and three dozen in-depth interviews reveal how significantly abuse affects the tenure of these officeholders and shapes their decisions. Taken together, the data sets represent more than 1,700 officials from all 50 states and include a range of ages, party aff

We study the SUPPORTED model of distributed computing introduced by Schmid and Suomela [Schmid and Suomela, 2013], which generalizes the LOCAL and CONGEST models. In this framework, multiple instances of the same problem, differing from each other by the subnetwork to which they apply. recur over time, and need to be solved efficiently online. To do that, one may rely on an initial preprocessing phase for computing some useful information. This preprocessing phase makes it possible, in some cases, to obtain improved distributed algorithms, overcoming locality-based time lower bounds. Our main contribution is to expand the class of problems to which the SUPPORTED model applies, by handling also multiple recurring instances of the same problem that differ from each other by some problem specific input, and not only the subnetwork to which they apply. We illustrate this by considering two extended problem classes. The first class, denoted PCS, concerns problems where client nodes of the network need to be served, and each recurring instance applies to some Partial Client Set. The second class, denoted PFO, concerns situations where each recurrent instance of the problem includes a partially fixed output, which needs to be completed to a full consistent solution. Specifically, we propose some natural recurrent variants of the dominating set problem and the coloring problem that are of interest particularly in the distributed setting. For these problems, we show that information about the topology can be used to overcome locality-based lower bounds. We also categorize the round complexity of Locally Checkable Labellings in the SUPPORTED model for the simple case of paths. Finally we present some interesting open problems and some partial results towards resolving them.

Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health., QC 20240307

Objective:Automated medical image segmentation (MIS) using deep learning has traditionally relied on models built and trained from scratch, or at least fine-tuned on a target dataset. The Segment Anything Model (SAM) by Meta challenges this paradigm by providing zero-shot generalisation capabilities. This study aims to develop and compare methods for refining traditional U-Net segmentations by repurposing them for automated SAM prompting.Approach:A 2D U-Net with EfficientNet-B4 encoder was trained using 4-fold cross-validation on an in-house brain metastases dataset. Segmentation predictions from each validation set were used for automatic sparse prompt generation via a bounding box prompting method (BBPM) and novel implementations of the point prompting method (PPM). The PPMs frequently produced poor slice predictions (PSPs) that required identification and substitution. A slice was identified as a PSP if it (1) contained multiple predicted regions per lesion or (2) possessed outlier foreground pixel counts relative to the patient's other slices. Each PSP was substituted with a corresponding initial U-Net or SAM BBPM prediction. The patients' mean volumetric dice similarity coefficient (DSC) was used to evaluate and compare the methods' performances.Main results:Relative to the initial U-Net segmentations, the BBPM improved mean patient DSC by 3.93 ± 1.48% to 0.847 ± 0.008 DSC. PSPs constituted 20.01-21.63% of PPMs' predictions and without substitution performance dropped by 82.94 ± 3.17% to 0.139 ± 0.023 DSC. Pairing the two PSP identification techniques yielded a sensitivity to PSPs of 92.95 ± 1.20%. By combining this approach with BBPM prediction substitution, the PPMs achieved segmentation accuracies on par with the BBPM, improving mean patient DSC by up to 4.17 ± 1.40% and reaching 0.849 ± 0.007 DSC.Significance:The proposed PSP identification and substitution techniques bridge the gap between PPM and BBPM performance for MIS. Additionally, the uniformity obse, QC 20240122

Khaled Saeed Yousef Al Baloushi,1 Annadurai Senthilkumar,1,2 Karthishwaran Kandhan,1 Radhakrishnan Subramanian,1 Jaleel Kizhakkayil,3 Tholkappiyan Ramachandran,4,5 Safa Shehab,6 Shyam Sreedhara Kurup,1 Mohammed Abdul Muhsen Alyafei,1 Ayesha Salem Al Dhaheri,3 Abdul Jaleel1 1Department of Integrative Agriculture, College of Agriculture and Veterinary Medicine, United Arab Emirates University, Al Ain, United Arab Emirates; 2PG and Research Department of Botany, Kandaswami Kandar’s College, Velur, TN, India; 3Department of Nutrition & Health Sciences, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates; 4Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; 5Department of Physics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Saveetha University, Chennai, TN, India; 6Department of Human Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesCorrespondence: Abdul Jaleel, Tel +971-3-713-4576, Email abdul.jaleel@uaeu.ac.aeIntroduction: The synthesis of nanoparticles using naturally occurring reagents such as vitamins, sugars, plant extracts, biodegradable polymers and microorganisms as reductants and capping agents could be considered attractive for nanotechnology. These syntheses have led to the fabrication of limited number of inorganic nanoparticles. Among the reagents mentioned above, plant-based materials seem to be the best candidates, and they are suitable for large-scale biosynthesis of nanoparticles.Methods: The aqueous extract of Moringa peregrina leaves was used to synthesize silver nanoparticles. The synthesized nanoparticles were characterized by various spectral studies including FT-IR, SEM, HR-TEM and XRD. In addition, the antioxidant activity of the silver nanoparticles was studied viz. DPPH, ABTS, hydroxyl radical scavenging, superoxide radical scavenging, ni

Ensuring robustness against adversarial attacks is imperative for Machine Learning (ML) systems within the critical infrastructures of the Industrial Internet of Things (IIoT). This paper addresses vulnerabilities in IIoT systems, particularly in distributed environments like Federated Learning (FL) by presenting a resilient framework - Secure Federated Learning (SFL) specifically designed to mitigate data and model poisoning, as well as Sybil attacks within these networks. Sybil attacks, involving the creation of multiple fake identities, and poisoning attacks significantly compromise the integrity and reliability of ML models in FL environments. Our SFL framework leverages a Digital Twin (DT) as a critical aggregation checkpoint to counteract data and model poisoning attacks in IIoT's distributed settings. The DT serves as a protective mechanism during the model update aggregation phase, substantially enhancing the system's resilience. To further secure IIoT infrastructures, SFL employs blockchain-based Non-Fungible Tokens (NFTs) to authenticate participant identities, effectively preventing Sybil attacks by ensuring traceability and accountability among distributed nodes. Experimental evaluation within IIoT scenarios demonstrates that SFL substantially enhances defensive capabilities, maintaining the integrity and robustness of model learning. Comparative results reveal that the SFL framework, when applied to IIoT federated environments, achieves a commendable 97% accuracy, outperforming conventional FL approaches. SFL also demonstrates a remarkable reduction in loss rate, recording just 0.07 compared to the 0.14 loss rate experienced by standard FL systems. These findings highlight the efficiency and applicability of the SFL framework in enhancing data security and traceability within the IIoT ecosystem.

The manufacturing industry is facing a major challenge in balancing economic performance with environmental sustainability. To address this challenge, extant research has suggested that servitization could help align these objectives. However, the current evidence base is too scattered for policymakers to act on and support servitization in a targeted manner. Therefore, we conduct a systematic literature review to analyze over 120 research papers to establish the current understanding of the impact of servitization on the environmental and economic performance of a firm, and identify the contextual variables affecting this impact. The study identifies and critically appraises the body of literature that provides the current evidence base on the impact of servitization, the core areas of impact investigated and the methods that are used to establish this impact. Additionally, we conduct a thematic analysis of variables of impact to explore the theoretical perspectives that are used to explain the impact of servitization. Building on these theoretical perspectives we offer concrete propositions to further develop the research on the impact of servitization on environmental and economic performance.

Adult-onset testosterone deficiency (TD) in men is diagnosed by the finding of low serum testosterone levels and recognised, associated symptoms. The condition has high prevalence in men over 50 years of age, particularly those with type 2 diabetes (T2DM). Accumulating data show adult-onset TD is associated with increased mortality risk. We review the literature and consider the evidence suggesting testosterone therapy (TTh) reduces mortality, especially in men with T2DM. We previously reported that in the Burntwood Lichfield Atherstone Sutton Coldfield Tamworth (BLAST) study screened cohort of men with adult-onset TD and T2DM adult-onset TD was associated with increased mortality with TTh decreasing this higher mortality. The data hinted that the effect was greater in older men. We confirmed this observation with statistical analyses to study the effect of age on the association between adult-onset TD and mortality; Cox regression analysis demonstrated that the reduced risk (hazard ratio: 0.61, 95% CI: 0.38–0.96) following TTh was restricted to men above the median age of 65.89 years. Finally, we speculate on putative mechanisms that may mediate these associations. Heterogeneity in men with adult-onset TD is expected in view of its definition of low testosterone levels together with associated clinical phenotypes that are not always directly related. Many of these classifying phenotypes are associated with increased mortality. Thus, it is perhaps possible that mechanism(s) of all-cause mortality reduction following TTh is via the impact on these associated phenotypes such as the metabolic syndrome (MetS), hyperglycaemia, hypertension, dyslipidaemia, low haematocrit, sex hormone binding levels, erectile dysfunction, etc. We propose that further research studying the effect of TTh takes heterogeneity into account.

Adult-onset testosterone deficiency (TD) in men is diagnosed by the finding of low serum testosterone levels and recognised, associated symptoms. The condition has high prevalence in men over 50 years of age, particularly those with type 2 diabetes (T2DM). Accumulating data show adult-onset TD is associated with increased mortality risk. We review the literature and consider the evidence suggesting testosterone therapy (TTh) reduces mortality, especially in men with T2DM. We previously reported that in the Burntwood Lichfield Atherstone Sutton Coldfield Tamworth (BLAST) study screened cohort of men with adult-onset TD and T2DM adult-onset TD was associated with increased mortality with TTh decreasing this higher mortality. The data hinted that the effect was greater in older men. We confirmed this observation with statistical analyses to study the effect of age on the association between adult-onset TD and mortality; Cox regression analysis demonstrated that the reduced risk (hazard ratio: 0.61, 95% CI: 0.38–0.96) following TTh was restricted to men above the median age of 65.89 years. Finally, we speculate on putative mechanisms that may mediate these associations. Heterogeneity in men with adult-onset TD is expected in view of its definition of low testosterone levels together with associated clinical phenotypes that are not always directly related. Many of these classifying phenotypes are associated with increased mortality. Thus, it is perhaps possible that mechanism(s) of all-cause mortality reduction following TTh is via the impact on these associated phenotypes such as the metabolic syndrome (MetS), hyperglycaemia, hypertension, dyslipidaemia, low haematocrit, sex hormone binding levels, erectile dysfunction, etc. We propose that further research studying the effect of TTh takes heterogeneity into account.

The manufacturing industry is facing a major challenge in balancing economic performance with environmental sustainability. To address this challenge, extant research has suggested that servitization could help align these objectives. However, the current evidence base is too scattered for policymakers to act on and support servitization in a targeted manner. Therefore, we conduct a systematic literature review to analyze over 120 research papers to establish the current understanding of the impact of servitization on the environmental and economic performance of a firm, and identify the contextual variables affecting this impact. The study identifies and critically appraises the body of literature that provides the current evidence base on the impact of servitization, the core areas of impact investigated and the methods that are used to establish this impact. Additionally, we conduct a thematic analysis of variables of impact to explore the theoretical perspectives that are used to explain the impact of servitization. Building on these theoretical perspectives we offer concrete propositions to further develop the research on the impact of servitization on environmental and economic performance.

Protein tyrosine phosphatases (PTPs) play major roles in cancer and are emerging as therapeutic targets. Recent reports suggest low-molecular weight PTP (LMPTP)-encoded by the ACP1 gene-is overexpressed in prostate tumors. We found ACP1 up-regulated in human prostate tumors and ACP1 expression inversely correlated with overall survival. Using CRISPR-Cas9-generated LMPTP knockout C4-2B and MyC-CaP cells, we identified LMPTP as a critical promoter of prostate cancer (PCa) growth and bone metastasis. Through metabolomics, we found that LMPTP promotes PCa cell glutathione synthesis by dephosphorylating glutathione synthetase on inhibitory Tyr270. PCa cells lacking LMPTP showed reduced glutathione, enhanced activation of eukaryotic initiation factor 2-mediated stress response, and enhanced reactive oxygen species after exposure to taxane drugs. LMPTP inhibition slowed primary and bone metastatic prostate tumor growth in mice. These findings reveal a role for LMPTP as a critical promoter of PCa growth and metastasis and validate LMPTP inhibition as a therapeutic strategy for treating PCa through sensitization to oxidative stress.

Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Vitamin B12 is critical for hematopoiesis and myelination. Deficiency can cause neurologic deficits including loss of coordination and cognitive decline. However, diagnosis relies on measurement of vitamin B12 in the blood, which may not accurately reflect the concentration in the brain. Using programmable phage display, we identified an autoantibody targeting the transcobalamin receptor (CD320) in a patient with progressive tremor, ataxia, and scanning speech. Anti-CD320 impaired cellular uptake of cobalamin (B12) in vitro by depleting its target from the cell surface. Despite a normal serum concentration, B12 was nearly undetectable in her cerebrospinal fluid (CSF). Immunosuppressive treatment and high-dose systemic B12 supplementation were associated with increased B12 in the CSF and clinical improvement. Optofluidic screening enabled isolation of a patient-derived monoclonal antibody that impaired B12 transport across an in vitro model of the blood-brain barrier (BBB). Autoantibodies targeting the same epitope of CD320 were identified in seven other patients with neurologic deficits of unknown etiology, 6% of healthy controls, and 21.4% of a cohort of patients with neuropsychiatric lupus. In 132 paired serum and CSF samples, detection of anti-CD320 in the blood predicted B12 deficiency in the brain. However, these individuals did not display any hematologic signs of B12 deficiency despite systemic CD320 impairment. Using a genome-wide CRISPR screen, we found that the low-density lipoprotein receptor serves as an alternative B12 uptake pathway in hematopoietic cells. These findings dissect the tissue specificity of B12 transport and elucidate an autoimmune neurologic condition that may be amenable to immunomodulatory treatment and nutritional supplementation.

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium $\beta$-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of $m_ u^{2} = {-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_ u < {0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two.

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide a distributed control strategy (DCS) based on a leader-follower framework for the coordination of multiple DERs in NMGs. A fuzzy optimized Recurrent Hopfield Neural Network (F-HNN) designed self-adaptive fractional order proportional integral derivative (FOPID) controller is proposed for distributed frequency control of NMGs. Initially, a Lyapunov-based objective function is derived for weight updation of the proposed network. The fuzzy approach is used to optimize the output of the HNN based on its gradients. The proposed F-HNN based DCS is implemented in MATLAB/Simulink and validated for frequency regulation of NMGs through hardware-in-the-loop simulation (HIL) using OPAL-RT. The results obtained are compared with the conventional FOPID HNN tuned and other classical controls. The self-adaptiveness of the controller is demonstrated for change in renewable power generation. Furthermore, the resiliency of the controller is tested with communication failures and, plug and play operation of MGs. The results obtained showed that the frequency of the NMG system is well regulated within the band of ±0.2 Hz. Also, the transient and steady state performance reveals that the proposed DCS is more significant than other techniques.

Introduction: This study aims to determine the prognostic factors and survival outcomes of patients that underwent this surgery. Method: A retrospective review of 43 patients who underwent exenteration for periocular malignancies over a 14-year period was carried out. Patient demographics, tumour histology, treatment details, surgical margins’ status and post-operative survival were recorded. The survival outcome examined was the overall survival (OS) rate. Cox regression and Kaplan-Meier analysis were used to evaluate post-exenteration survival. Results: In total, 20 females and 23 males with a median age of 62 ± 17.3 years were identified. The most common indication for exenteration was basal cell carcinoma (20.9), followed by squamous cell carcinoma (18.6), adenocystic carcinoma (14), malignant melanoma (14) and sebaceous gland carcinoma (11.6). The independent predictors for worse OS on multivariate analysis were Chinese ethnicity (adjusted hazard ratio aHR 4.95, p =0.017), sebaceous gland carcinoma (aHR 57.61 p=0.006), adenocystic carcinoma (aHR 45.87, p=0.008), clear surgical margins (aHR 5.41, p=0.025), receiving only chemotherapy (aHR 169.13, p=0.004), and receiving both adjuvant chemotherapy and radiotherapy (aHR 41.51, p=0.010). Conclusion: We recommend targeted public health initiatives for Chinese patients due to their increased mortality risk from peri-ocular malignancies. In addition, we advise comprehensive adjuvant therapy for all patients regardless of whether a clear surgical margin is achieved. Basal cell carcinoma and adenocystic carcinoma may also benefit from genetic research. We advocate more training for ophthalmologists to identify periocular malignancies earlier for better treatment options and increased chances of survival.

Bacterial leaf blight (BLB) is one of the major rice diseases in Malaysia. This disease causes substantial yield loss as high as 70. Development of rice varieties which inherited BLB resistant traits is a crucial approach to promote and sustain rice industry in Malaysia. Hence, this study aims were to enhance BLB disease resistant characters of high yielding commercial variety MR219 through backcross breeding approach with supporting tool of marker-assisted selection (MAS). Broad spectrum BLB resistance gene, Xa7 from donor parent IRBB7 were introgressed into the susceptible MR219 (recurrent parent) using two flanking markers ID7 and ID15. At BC3 F4, we managed to generate 19 introgressed lines with homozygous Xa7 gene and showed resistant characteristics as donor parent when it was challenged with Xanthomonas oryzae pv. oryzae through artificial inoculation. Recurrent parent MR219 and control variety, MR263 were found to be severely infected by the disease. The improved lines exhibited similar morphological and yield performance characters as to the elite variety, MR219. Two lines, PB-2-107 and PB-2-34 were chosen to be potential lines because of their outstanding performances compared to parent, MR219. This study demonstrates a success story of MAS application in development of improved disease resistance lines of rice against BLB disease.

A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitatio

Introduction: This study aims to determine the prognostic factors and survival outcomes of patients that underwent this surgery. Method: A retrospective review of 43 patients who underwent exenteration for periocular malignancies over a 14-year period was carried out. Patient demographics, tumour histology, treatment details, surgical margins’ status and post-operative survival were recorded. The survival outcome examined was the overall survival (OS) rate. Cox regression and Kaplan-Meier analysis were used to evaluate post-exenteration survival. Results: In total, 20 females and 23 males with a median age of 62 ± 17.3 years were identified. The most common indication for exenteration was basal cell carcinoma (20.9), followed by squamous cell carcinoma (18.6), adenocystic carcinoma (14), malignant melanoma (14) and sebaceous gland carcinoma (11.6). The independent predictors for worse OS on multivariate analysis were Chinese ethnicity (adjusted hazard ratio aHR 4.95, p =0.017), sebaceous gland carcinoma (aHR 57.61 p=0.006), adenocystic carcinoma (aHR 45.87, p=0.008), clear surgical margins (aHR 5.41, p=0.025), receiving only chemotherapy (aHR 169.13, p=0.004), and receiving both adjuvant chemotherapy and radiotherapy (aHR 41.51, p=0.010). Conclusion: We recommend targeted public health initiatives for Chinese patients due to their increased mortality risk from peri-ocular malignancies. In addition, we advise comprehensive adjuvant therapy for all patients regardless of whether a clear surgical margin is achieved. Basal cell carcinoma and adenocystic carcinoma may also benefit from genetic research. We advocate more training for ophthalmologists to identify periocular malignancies earlier for better treatment options and increased chances of survival.

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhance its performance. However, the coordinated operation of the system with integration of a large number of DERs is major challenge. Therefore, this article aims to provide a distributed control strategy (DCS) based on a leader-follower framework for the coordination of multiple DERs in NMGs. A fuzzy optimized Recurrent Hopfield Neural Network (F-HNN) designed self-adaptive fractional order proportional integral derivative (FOPID) controller is proposed for distributed frequency control of NMGs. Initially, a Lyapunov-based objective function is derived for weight updation of the proposed network. The fuzzy approach is used to optimize the output of the HNN based on its gradients. The proposed F-HNN based DCS is implemented in MATLAB/Simulink and validated for frequency regulation of NMGs through hardware-in-the-loop simulation (HIL) using OPAL-RT. The results obtained are compared with the conventional FOPID HNN tuned and other classical controls. The self-adaptiveness of the controller is demonstrated for change in renewable power generation. Furthermore, the resiliency of the controller is tested with communication failures and, plug and play operation of MGs. The results obtained showed that the frequency of the NMG system is well regulated within the band of ±0.2 Hz. Also, the transient and steady state performance reveals that the proposed DCS is more significant than other techniques.

A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitatio

Bacterial leaf blight (BLB) is one of the major rice diseases in Malaysia. This disease causes substantial yield loss as high as 70. Development of rice varieties which inherited BLB resistant traits is a crucial approach to promote and sustain rice industry in Malaysia. Hence, this study aims were to enhance BLB disease resistant characters of high yielding commercial variety MR219 through backcross breeding approach with supporting tool of marker-assisted selection (MAS). Broad spectrum BLB resistance gene, Xa7 from donor parent IRBB7 were introgressed into the susceptible MR219 (recurrent parent) using two flanking markers ID7 and ID15. At BC3 F4, we managed to generate 19 introgressed lines with homozygous Xa7 gene and showed resistant characteristics as donor parent when it was challenged with Xanthomonas oryzae pv. oryzae through artificial inoculation. Recurrent parent MR219 and control variety, MR263 were found to be severely infected by the disease. The improved lines exhibited similar morphological and yield performance characters as to the elite variety, MR219. Two lines, PB-2-107 and PB-2-34 were chosen to be potential lines because of their outstanding performances compared to parent, MR219. This study demonstrates a success story of MAS application in development of improved disease resistance lines of rice against BLB disease.

Self-adaptivity is mainly used to address uncertainties, unpredicted events, as well as to automate administration tasks. It allows systems to change themselves while executing in order to address expected or unexpected changes and to adapt as much as possible to the current execution context. Self-adaptivity is particularly meaningful for dynamic application domains such as Internet of Things (IoT), Cyber-Physical Systems (CPS), service oriented based solutions (SOA), cloud computing, robotics, among many others. There are various available solutions in these domains that exploit self-adaptivity. The question is how can we analyze them to understand how self-adaptivity is implemented and exploited in order to use and re-use, as well as to adapt existing solutions to new or other systems? In this paper, we propose a first step in this direction, by analyzing available self-adaptive systems (and especially their self-adaptive mechanisms) in various application domains using the Understand tool - widely used for software development, analysis, and quality assessment.

Artificial Intelligence has revolutionized the manufacturing sector, enabling predictive maintenance, quality control, supply chain management, and the development of collaborative robots. AI powered vision systems and detection algorithms improve precision and consistency in manufacturing processes. AI powered vision systems enhance supply chain management, while collaborative robots increase productivity and safety. However, challenges like data privacy, infrastructure investments, and workforce upskilling are still present. Case studies of leading companies provide practical insights into the benefits and challenges of AI adoption.

We investigate the problem of secure multi-terminal strong coordination aided by a multiple-access wiretap channel (MAC-WT). In this setup, independent and identically distributed (i.i.d.) copies of correlated sources are observed by two transmitters who encode the channel inputs to the MAC-WT. The legitimate receiver on observing the channel output must produce approximately i.i.d. copies of an output random variable jointly distributed with the two sources. Furthermore, we demand that an external eavesdropper learns essentially nothing about the sources and the simulated output sequence by observing its corresponding MAC-WT output. This is aided by the presence of independent pairwise shared randomness between each encoder and the legitimate decoder. The shared randomness rate tuples which permit such channel simulation with strong secrecy are of interest. We derive an achievable rate region based on a combination of coordination coding and wiretap coding, along with an outer bound. The inner bound is shown to be tight and a complete characterization is derived for the special case when the sources are independent and the legitimate receiver's channel is composed of deterministic links., Part of ISBN 9798350382846QC 20240919

joint message communication and input reconstruction problem over a two-user state-dependent degraded discrete-memoryless broadcast channel is considered. The state process is assumed to be independent and identically distributed (i.i.d.), and known non-causally at the transmitter as well as the non-degraded receiver. The two receivers have to decode the messages from the transmitter, while the degraded receiver also needs to estimate the channel input codeword to meet a prescribed distortion limit. A complete characterization of the optimal rates versus distortion performance is provided. The tight characterization is also illustrated by means of an example of an additive binary broadcast channel with a Hamming distortion constraint for the input reconstruction at the degraded receiver., QC 20240617

Serious adverse events following vaccination include medical complications that require hospitalisation. The live varicella vaccine that was approved by the Food and Drug Administration in the United States in 1995 has an excellent safety record. Since the vaccine is a live virus, adverse events are more common in immunocompromised children who are vaccinated inadvertently. This review includes only serious adverse events in children considered to be immunocompetent. The serious adverse event called varicella vaccine meningitis was first reported in a hospitalised immunocompetent child in 2008. When we carried out a literature search, we found 15 cases of immunocompetent children and adolescents with varicella vaccine meningitis; the median age was 11 years. Eight of the children had received two varicella vaccinations. Most of the children also had a concomitant herpes zoster rash, although three did not. The children lived in the United States, Greece, Germany, Switzerland, and Japan. During our literature search, we found five additional cases of serious neurological events in immunocompetent children; these included 4 cases of progressive herpes zoster and one case of acute retinitis. Pulses of enteral corticosteroids as well as a lack of herpes simplex virus antibody may be risk factors for reactivation in immunocompetent children. All 20 children with adverse events were treated with acyclovir and recovered; 19 were hospitalised and one child was managed as an outpatient. Even though the number of neurological adverse events remains exceedingly low following varicella vaccination, we recommend documentation of those caused by the vaccine virus.

Artificial Intelligence has revolutionized the manufacturing sector, enabling predictive maintenance, quality control, supply chain management, and the development of collaborative robots. AI powered vision systems and detection algorithms improve precision and consistency in manufacturing processes. AI powered vision systems enhance supply chain management, while collaborative robots increase productivity and safety. However, challenges like data privacy, infrastructure investments, and workforce upskilling are still present. Case studies of leading companies provide practical insights into the benefits and challenges of AI adoption.

We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ polarized muons stored in a $7.1$-m-radius storage ring with a $1.45$ T uniform magnetic field. The value of $ a_{\mu}$ is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using Nuclear Magnetic Resonance (NMR). The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_\mu = 116 592 057 (25) \times 10^{-11}$ (0.21 ppm). This is the world's most precise measurement of this quantity and represents a factor of $2.2$ improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_\mu(\text{FNAL}) = 116 592 055 (24) \times 10^{-11}$ (0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_\mu$(exp) $ = 116 592 059 (22) \times 10^{-11}$ (0.19 ppm)., Comment: 48 pages, 29 figures; 4 pages of Supplement Material; version accepted for publication in Physical Review D

Keerthana Raghu,1,&ast; Tamilselvi S,2,&ast; Chitralekha S Devishamani,1 Suchetha M,2 Ramachandran Rajalakshmi,3 Rajiv Raman1 1Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India; 2Centre for Health Care Advancement, Innovation, and Research Department, Vellore Institute of Technology, Chennai, Tamil Nadu, India; 3Department of Diabetology, Ophthalmology and Epidemiology, Madras Diabetes Research Foundation & Dr. Mohan’s Diabetes Specialities Centre, Chennai, Tamil Nadu, India&ast;These authors contributed equally to this workCorrespondence: Rajiv Raman, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India, Email rajivpgraman@gmail.com

A state-wide prospective longitudinal investigation of the genomic surveillance of the omicron B.1.1.529 SARS-CoV-2 variant and its sublineages in Tamil Nadu, India, was conducted between December 2021 and March 2023. The study aimed to elucidate their mutational patterns and their genetic interrelationship in the Indian population. The study identified several unique mutations at different time-points, which likely could attribute to the changing disease characteristics, transmission, and pathogenicity attributes of omicron variants. The study found that the omicron variant is highly competent in its mutating potentials, and that it continues to evolve in the general population, likely escaping from natural as well as vaccine-induced immune responses. Our findings suggest that continuous surveillance of viral variants at the global scenario is warranted to undertake intervention measures against potentially precarious SARS-CoV-2 variants and their evolution.

The mechanochemical GTPase dynamin-related protein 1 (Drp1) catalyzes mitochondrial and peroxisomal fission, but the regulatory mechanisms remain ambiguous. Here we find that a conserved, intrinsically disordered, six-residue Short Linear Motif at the extreme Drp1 C-terminus, named CT-SLiM, constitutes a critical allosteric site that controls Drp1 structure and function in vitro and in vivo. Extension of the CT-SLiM by non-native residues, or its interaction with the protein partner GIPC-1, constrains Drp1 subunit conformational dynamics, alters self-assembly properties, and limits cooperative GTP hydrolysis, surprisingly leading to the fission of model membranes in vitro. In vivo, the involvement of the native CT-SLiM is critical for productive mitochondrial and peroxisomal fission, as both deletion and non-native extension of the CT-SLiM severely impair their progression. Thus, contrary to prevailing models, Drp1-catalyzed membrane fission relies on allosteric communication mediated by the CT-SLiM, deceleration of GTPase activity, and coupled changes in subunit architecture and assembly-disassembly dynamics.

Using the medium resolution spectrograph X-shooter, spectra of 235 OB and Wolf-Rayet (WR) stars in sub-solar metallicity environments have been secured. [...]This second paper focuses on the optical observations of 232 Magellanic Clouds targets. It describes the uniform reduction of the UVB (300 - 560 nm) and VIS (550 - 1020 nm) XShootU data as well as the preparation of advanced data products [...] . The data reduction of the raw data is based on the ESO CPL X-shooter pipeline. We paid particular attention to the determination of the response curves [...] We implemented slit-loss correction, absolute flux calibration, (semi-)automatic rectification to the continuum, and a correction for telluric lines. The spectra of individual epochs were corrected for the barycentric motion, re-sampled and co-added, and the spectra from the two arms were merged into a single flux calibrated spectrum covering the entire optical range with maximum signal-to-noise ratio. [...] We provide three types of data products: (i) two-dimensional spectra for each UVB and VIS exposure; (ii) one-dimensional UVB and VIS spectra before and after response-correction, as well as after applying various processing, including absolute flux calibration, telluric removal, normalisation and barycentric correction; and (iii) co-added flux-calibrated and rectified spectra over the full optical range, for which all available XShootU exposures were combined. For many of the targets, the final signal-to-noise ratio per resolution element is above 200 in both the UVB and the VIS co-added spectra. The reduced data and advanced scientific data products will be made available to the community upon publication of this paper. [...], Comment: 22 pages, 19 figures; accepted for publication in Astronomy & Astrophysics. Links to online tables and databases will be included upon publication

This paper studies hidden convexity properties associated with constrained optimization problems over the set of rotation matrices SO(n). Such problems are nonconvex due to the constraint X∈SO(n). Nonetheless, we show that certain linear images of SO(n) are convex, opening up the possibility for convex optimization algorithms with provable guarantees for these problems. Our main technical contributions show that any two-dimensional image of SO(n) is convex and that the projection of SO(n) onto its strict upper triangular entries is convex. These results allow us to construct exact convex reformulations for constrained optimization problems over SO(n) with a single constraint or with constraints defined by low-rank matrices. Both of these results are optimal in a formal sense.

Over the past few decades, the use of non-renewable energy has progressively expanded, harming the environment. In this investigation, 4-stroke single-cylinder Reactivity Controlled Compression Ignition (RCCI) engine performance and emission behaviour are reduced with the help of running fuel. 20% Juliflora biodiesel and 80% diesel are used as high-reactive fuel (HRF) and Ethanol is used as the low-reactive fuel (LRF). The RCCI engine is evaluated at different input conditions by varying engine load from 0 to 100 (0, 25, 50, 75, and 100%) and LRF percentage from 30 to 60 (30, 40, 50 and 60%). Additionally Exhaust Gas Recirculation (EGR) is used to enhance the RCCI engine emission behaviour and performance.The studied output performance of RCCI engine are cylinder pressure (CP), brake thermal efficiency (BTE), heat release rate (HRR), and brake-specific fuel consumption (BSFC) respectively. Also, unburned hydrocarbon (HC), carbon monoxide (CO), nitrogen oxides (NOX), and smoke opacity (SO) are calculated on the RCCI engine for all input condition. The test results are further optimized with the help of hybrid deep belief neural network based Aquila optimization method. The proposed hybrid DBN-AO has performed better than conventional DBN method.The predicted optimal value is obtained from the regression and average regression coefficients of 0.99961. The predicted optimum values are load 80%, LRF60%, and EGR 15%, respectively. The confirmatory error analysis has shown BTE (3.7%), BSFC (4%), SO (4.7%), HC (7.775%), CO (3.44%) and NOx (3.46%) respectively. The EGR application reduces the RCCI engine emission behaviour in loading condition.

Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that remain largely unstructured. This unstructured narrative must be converted into a structured semantic representation to facilitate secondary applications such as retrospective analyses or clinical decision support. Here, we introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which includes 609 annotated radiology reports from three imaging modality types: Computed Tomography, Magnetic Resonance Imaging, and Positron Emission Tomography-Computed Tomography. Reports were annotated using an event-based schema that captures clinical indications, lesions, and medical problems. Each event consists of a trigger and multiple arguments, and a majority of the argument types, including anatomy, normalize the spans to pre-defined concepts to facilitate secondary use. CAMIR uniquely combines a granular event structure and concept normalization. To extract CAMIR events, we explored two BERT (Bi-directional Encoder Representation from Transformers)-based architectures, including an existing architecture (mSpERT) that jointly extracts all event information and a multi-step approach (PL-Marker++) that we augmented for the CAMIR schema., Comment: Accepted at LREC-COLING 2024

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states without access to domain knowledge, underperform when the training corpus is limited/noisy, or have difficulty when test dialogs exhibit distributional shifts from the training domain. This work explores a neural-symbolic approach as a potential solution to these problems. We introduce Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a principled approach that injects symbolic knowledge into the latent space of a generative neural model. We conduct a thorough empirical investigation on the effect of NEUPSL DSI learning on hidden representation quality, few-shot learning, and out-of-domain generalization performance. Over three dialog structure induction datasets and across unsupervised and semi-supervised settings for standard and cross-domain generalization, the injection of symbolic knowledge using NEUPSL DSI provides a consistent boost in performance over the canonical baselines.

Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training. In this study, we introduce Logarithmic Posits (LP), an adaptive, hardware-friendly data type inspired by posits that dynamically adapts to DNN weight/activation distributions by parameterizing LP bit fields. We also develop a novel genetic-algorithm based framework, LP Quantization (LPQ), to find optimal layer-wise LP parameters while reducing representational divergence between quantized and full-precision models through a novel global-local contrastive objective. Additionally, we design a unified mixed-precision LP accelerator (LPA) architecture comprising of processing elements (PEs) incorporating LP in the computational datapath. Our algorithm-hardware co-design demonstrates on average <1% drop in top-1 accuracy across various CNN and ViT models. It also achieves ~ 2x improvements in performance per unit area and 2.2x gains in energy efficiency compared to state-of-the-art quantization accelerators using different data types., Comment: 2024 61st IEEE/ACM Design Automation Conference (DAC)

Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff of such numerical simulations. In this work, we lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH - a Smoothed Particle Hydrodynamics (SPH) framework implemented in JAX. JAX-SPH builds on the code for dataset generation from the LagrangeBench project (Toshev et al., 2023) and extends this code in multiple ways: (a) integration of further key SPH algorithms, (b) restructuring the code toward a Python library, (c) verification of the gradients through the solver, and (d) demonstration of the utility of the gradients for solving inverse problems as well as a Solver-in-the-Loop application. Our code is available at https://github.com/tumaer/jax-sph., Comment: Accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science

Image generation models are poised to become ubiquitous in a range of applications. These models are often fine-tuned and evaluated using human quality judgments that assume a universal standard, failing to consider the subjectivity of such tasks. To investigate how to quantify subjectivity, and the scale of its impact, we measure how assessments differ among human annotators across different use cases. Simulating the effects of ordinarily latent elements of annotators subjectivity, we contrive a set of motivations (t-shirt graphics, presentation visuals, and phone background images) to contextualize a set of crowdsourcing tasks. Our results show that human evaluations of images vary within individual contexts and across combinations of contexts. Three key factors affecting this subjectivity are image appearance, image alignment with text, and representation of objects mentioned in the text. Our study highlights the importance of taking individual users and contexts into account, both when building and evaluating generative models

We define and study a variant of QMA (Quantum Merlin Arthur) in which Arthur can make multiple non-collapsing measurements to Merlin's witness state, in addition to ordinary collapsing measurements. By analogy to the class PDQP defined by Aaronson, Bouland, Fitzsimons, and Lee (2014), we call this class PDQMA. Our main result is that PDQMA = NEXP; this result builds on the MIP = NEXP Theorem and complements the result of Aaronson (2018) that PDQP/qpoly = ALL. While the result has little to do with quantum mechanics, we also show a more "quantum" result: namely, that QMA with the ability to inspect the entire history of a hidden variable is equal to NEXP, under mild assumptions on the hidden-variable theory. We also observe that a quantum computer, augmented with quantum advice and the ability to inspect the history of a hidden variable, can solve any decision problem in polynomial time., Comment: 15 pages

Each LLM serving request goes through two phases. The first is prefill which processes the entire input prompt to produce one output token and the second is decode which generates the rest of output tokens, one-at-a-time. Prefill iterations have high latency but saturate GPU compute due to parallel processing of the input prompt. In contrast, decode iterations have low latency but also low compute utilization because a decode iteration processes only a single token per request. This makes batching highly effective for decodes and consequently for overall throughput. However, batching multiple requests leads to an interleaving of prefill and decode iterations which makes it challenging to achieve both high throughput and low latency. We introduce an efficient LLM inference scheduler Sarathi-Serve inspired by the techniques we originally proposed for optimizing throughput in Sarathi. Sarathi-Serve leverages chunked-prefills from Sarathi to create stall-free schedules that can add new requests in a batch without pausing ongoing decodes. Stall-free scheduling unlocks the opportunity to improve throughput with large batch sizes while minimizing the effect of batching on latency. Our evaluation shows that Sarathi-Serve improves serving throughput within desired latency SLOs of Mistral-7B by up to 2.6x on a single A100 GPU and up to 6.9x for Falcon-180B on 8 A100 GPUs over Orca and vLLM.

Current approaches to prosthetic control are limited by their reliance on traditional methods, which lack real-time adaptability and intuitive responsiveness. These limitations are particularly pronounced in assistive technologies designed for individuals with diverse cognitive states and motor intentions. In this paper, we introduce a framework that leverages Reinforcement Learning (RL) with Deep Q-Networks (DQN) for classification tasks. Additionally, we present a preprocessing technique using the Common Spatial Pattern (CSP) for multiclass motor imagery (MI) classification in a One-Versus-The-Rest (OVR) manner. The subsequent 'csp space' transformation retains the temporal dimension of EEG signals, crucial for extracting discriminative features. The integration of DQN with a 1D-CNN-LSTM architecture optimizes the decision-making process in real-time, thereby enhancing the system's adaptability to the user's evolving needs and intentions. We elaborate on the data processing methods for two EEG motor imagery datasets. Our innovative model, RLEEGNet, incorporates a 1D-CNN-LSTM architecture as the Online Q-Network within the DQN, facilitating continuous adaptation and optimization of control strategies through feedback. This mechanism allows the system to learn optimal actions through trial and error, progressively improving its performance. RLEEGNet demonstrates high accuracy in classifying MI-EEG signals, achieving as high as 100% accuracy in MI tasks across both the GigaScience (3-class) and BCI-IV-2a (4-class) datasets. These results highlight the potential of combining DQN with a 1D-CNN-LSTM architecture to significantly enhance the adaptability and responsiveness of BCI systems., Comment: 23 pages, 1 figure, 6 tables

Protecting software supply chains from malicious packages is paramount in the evolving landscape of software development. Attacks on the software supply chain involve attackers injecting harmful software into commonly used packages or libraries in a software repository. For instance, JavaScript uses Node Package Manager (NPM), and Python uses Python Package Index (PyPi) as their respective package repositories. In the past, NPM has had vulnerabilities such as the event-stream incident, where a malicious package was introduced into a popular NPM package, potentially impacting a wide range of projects. As the integration of third-party packages becomes increasingly ubiquitous in modern software development, accelerating the creation and deployment of applications, the need for a robust detection mechanism has become critical. On the other hand, due to the sheer volume of new packages being released daily, the task of identifying malicious packages presents a significant challenge. To address this issue, in this paper, we introduce a metadata-based malicious package detection model, MeMPtec. This model extracts a set of features from package metadata information. These extracted features are classified as either easy-to-manipulate (ETM) or difficult-to-manipulate (DTM) features based on monotonicity and restricted control properties. By utilising these metadata features, not only do we improve the effectiveness of detecting malicious packages, but also we demonstrate its resistance to adversarial attacks in comparison with existing state-of-the-art. Our experiments indicate a significant reduction in both false positives (up to 97.56%) and false negatives (up to 91.86%).

The frame scaling problem is: given vectors $U := \{u_{1}, ..., u_{n} \} \subseteq \mathbb{R}^{d}$, marginals $c \in \mathbb{R}^{n}_{++}$, and precision $\varepsilon > 0$, find left and right scalings $L \in \mathbb{R}^{d \times d}, r \in \mathbb{R}^n$ such that $(v_1,\dots,v_n) := (Lu_1 r_1,\dots,Lu_nr_n)$ simultaneously satisfies $\sum_{i=1}^n v_i v_i^{\mathsf{T}} = I_d$ and $\|v_{j}\|_{2}^{2} = c_{j}, \forall j \in [n]$, up to error $\varepsilon$. This problem has appeared in a variety of fields throughout linear algebra and computer science. In this work, we give a strongly polynomial algorithm for frame scaling with $\log(1/\varepsilon)$ convergence. This answers a question of Diakonikolas, Tzamos and Kane (STOC 2023), who gave the first strongly polynomial randomized algorithm with poly$(1/\varepsilon)$ convergence for the special case $c = \frac{d}{n} 1_{n}$. Our algorithm is deterministic, applies for general $c \in \mathbb{R}^{n}_{++}$, and requires $O(n^{3} \log(n/\varepsilon))$ iterations as compared to $O(n^{5} d^{11}/\varepsilon^{5})$ iterations of DTK. By lifting the framework of Linial, Samorodnitsky and Wigderson (Combinatorica 2000) for matrix scaling to frames, we are able to simplify both the algorithm and analysis. Our main technical contribution is to generalize the potential analysis of LSW to the frame setting and compute an update step in strongly polynomial time that achieves geometric progress in each iteration. In fact, we can adapt our results to give an improved analysis of strongly polynomial matrix scaling, reducing the $O(n^{5} \log(n/\varepsilon))$ iteration bound of LSW to $O(n^{3} \log(n/\varepsilon))$. Additionally, we prove a novel bound on the size of approximate frame scaling solutions, involving the condition measure $\bar{\chi}$ studied in the linear programming literature, which may be of independent interest., Comment: Comments welcome

Due to recent development in quantum computing, the invention of a large quantum computer is no longer a distant future. Quantum computing severely threatens modern cryptography, as the hard mathematical problems beneath classic public-key cryptosystems can be solved easily by a sufficiently large quantum computer. As such, researchers have proposed PQC based on problems that even quantum computers cannot efficiently solve. Generally, post-quantum encryption and signatures can be hard to compute. This could potentially be a problem for IoT, which usually consist lightweight devices with limited computational power. In this paper, we survey existing literature on the performance for PQC in resource-constrained devices to understand the severeness of this problem. We also review recent proposals to optimize PQC algorithms for resource-constrained devices. Overall, we find that whilst PQC may be feasible for reasonably lightweight IoT, proposals for their optimization seem to lack standardization. As such, we suggest future research to seek coordination, in order to ensure an efficient and safe migration toward IoT for the post-quantum era., Comment: 13 pages, 3 figures and 7 tables. Formatted version submitted to ACM Computer Surveys

A species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classification, and understanding genetic predispositions like drug resistance and virulence. However, the vast number of potential species poses significant challenges in developing a general-purpose whole genome classification tool. Traditional bioinformatics tools have made notable progress but lack scalability and are computationally expensive. Machine learning-based frameworks show promise but must address the issue of large classification vocabularies with long-tail distributions. In this study, we propose addressing this problem through zero-shot learning using TEPI, Taxonomy-aware Embedding and Pseudo-Imaging. We represent each genome as pseudo-images and map them to a taxonomy-aware embedding space for reasoning and classification. This embedding space captures compositional and phylogenetic relationships of species, enabling predictions in extensive search spaces. We evaluate TEPI using two rigorous zero-shot settings and demonstrate its generalization capabilities qualitatively on curated, large-scale, publicly sourced data., Comment: Accepted to IEEE JBHI