Miracle-dz/UniFAR-FTUs huggingface dataset:
A measurement of the ratio of the W and Z cross sections with exactly one associated jet in pp collisions at

The ratio of production cross sections of the W and Z bosons with exactly one associated jet is presented as a function of jet transverse momentum threshold. The measurement has been designed to maximise cancellation of experimental and theoretical uncertainties, and is reported both within a particle-level kinematic range corresponding to the detector acceptance and as a total cross-section ratio. Results are obtained with the ATLAS detector at the LHC in pp collisions at a centre-of-mass energy of 7 TeV using an integrated luminosity of 33 pb − 1 . The results are compared with perturbative leading-order, leading-log, and next-to-leading-order QCD predictions, and are found to agree within experimental and theoretical uncertainties. The ratio is measured for events with a single jet with p T > 30 GeV to be 8 . 73 ± 0 . 30(stat) ± 0 . 40(syst) in the electron channel, and 8 . 49 ± 0 . 23(stat) ± 0 . 33(syst) in the muon channel.
Camera Based Automated Quality Assessment of Fruits for a Smart Home

A camera-based fruit quality system is essential for the smart home. In this work, we propose a fruit detection system based on their qualities. Sorting fruits by humans is a time-consuming process, subject to inconsistencies and influenced by surrounding conditions. In this camera-based fruit quality assessment system, the fruits are detected and predicted under qualities like Fresh and Rotten. In the system, the Camera module captures the image of the fruit and then sends it to the Quality Assessment module. In this module, the information from that image is extracted and tested based on YOLOv5. After detecting the quality, the system indicates to the the user whether the fruit is fresh or rotten. This helps the user to easily get to know about the quality of the fruit and can segregate the fruits according to their freshness. This automatic fruit handling system helps every user in a smart home to save time and buy better quality fruits.
Partial Orderings as Heuristic for Multi-Objective Model-Based Reasoning

Model-based reasoning is becoming increasingly common in software engineering. The process of building and analyzing models helps stakeholders to understand the ramifications of their software decisions. But complex models can confuse and overwhelm stakeholders when these models have too many candidate solutions. We argue here that a technique based on partial orderings lets humans find acceptable solutions via a binary chop needing $O(log(N))$ queries (or less). This paper checks the value of this approach via the iSNEAK partial ordering tool. Pre-experimentally, we were concerned that (a)~our automated methods might produce models that were unacceptable to humans; and that (b)~our human-in-the-loop methods might actual overlooking significant optimizations. Hence, we checked the acceptability of the solutions found by iSNEAK via a human-in-the-loop double-blind evaluation study of 20 Brazilian programmers. We also checked if iSNEAK misses significant optimizations (in a corpus of 16 SE models of size ranging up to 1000 attributes by comparing it against two rival technologies (the genetic algorithms preferred by the interactive search-based SE community; and the sequential model optimizers developed by the SE configuration community~\citep{flash_vivek}). iSNEAK 's solutions were found to be human acceptable (and those solutions took far less time to generate, with far fewer questions to any stakeholder). Significantly, our methods work well even for multi-objective models with competing goals (in this work we explore models with four to five goals). These results motivate more work on partial ordering for many-goal model-based problems.
Online Scratch Activities during the COVID-19 Pandemic: Computational and Creative Thinking

This paper investigated the effect of online Scratch activities on college students’ computational and creative thinking. The study adopted a mixed research design including one group pretest-posttest. The sample consisted of 24 child development undergraduates (23 female and 1 male) in the 2019-2020 academic year. The research was carried out in "Teaching Science and Mathematics in Preschool Education” course and lasted 12 weeks. The participants developed Scratch projects based on eight learning outcomes (four math and four science). Data were collected using the Computational Thinking Scale, the Marmara Creative Thinking Dispositions Scale, and reflective journals. The quantitative data were analyzed using a paired sample t-test. The qualitative data were analyzed using content analysis. The results showed that Scratch activities helped students develop computational and creative thinking.
Constituency Parsing using LLMs

Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this task. We employ three linearization strategies to transform output trees into symbol sequences, such that LLMs can solve constituency parsing by generating linearized trees. We conduct experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT, LLaMA, and Alpaca, comparing their performance against the state-of-the-art constituency parsers. Our experiments encompass zero-shot, few-shot, and full-training learning settings, and we evaluate the models on one in-domain and five out-of-domain test datasets. Our findings reveal insights into LLMs' performance, generalization abilities, and challenges in constituency parsing.
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity

How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience. Various studies have suggested plausible solutions for back-propagating error signals through multi-layer networks. These purely functionally motivated models assume distinct neuronal compartments to represent local error signals that determine the sign of synaptic plasticity. However, this explicit error modulation is inconsistent with phenomenological plasticity models in which the sign depends primarily on postsynaptic activity. Here we show how a plausible microcircuit model and Hebbian learning rule derived within an adaptive control theory framework can resolve this discrepancy. Assuming errors are encoded in top-down dis-inhibitory synaptic afferents, we show that error-modulated learning emerges naturally at the circuit level when recurrent inhibition explicitly influences Hebbian plasticity. The same learning rule accounts for experimentally observed plasticity in the absence of inhibition and performs comparably to back-propagation of error (BP) on several non-linearly separable benchmarks. Our findings bridge the gap between functional and experimentally observed plasticity rules and make concrete predictions on inhibitory modulation of excitatory plasticity.
Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systems

The theory of Kazantzis-Kravaris/Luenberger (KKL) observer design introduces a methodology that uses a nonlinear transformation map and its left inverse to estimate the state of a nonlinear system through the introduction of a linear observer state space. Data-driven approaches using artificial neural networks have demonstrated the ability to accurately approximate these transformation maps. This paper presents a novel approach to observer design for nonlinear dynamical systems through meta-learning, a concept in machine learning that aims to optimize learning models for fast adaptation to a distribution of tasks through an improved focus on the intrinsic properties of the underlying learning problem. We introduce a framework that leverages information from measurements of the system output to design a learning-based KKL observer capable of online adaptation to a variety of system conditions and attributes. To validate the effectiveness of our approach, we present comprehensive experimental results for the estimation of nonlinear system states with varying initial conditions and internal parameters, demonstrating high accuracy, generalization capability, and robustness against noise.
A central limit theorem for Hilbert modular forms

For a prime ideal $\mathfrak{p}$ in a totally real number field $L$ with the adele ring $\mathbb{A}$, we study the distribution of angles $\theta_\pi(\mathfrak{p})$ coming from Satake parameters corresponding to unramified $\pi_\mathfrak{p}$ where $\pi_\mathfrak{p}$ comes from a global $\pi$ ranging over a certain finite set $\Pi_{\underline{k}}(\mathfrak{n})$ of cuspidal automorphic representations of GL$_2(\mathbb{A})$ with trivial central character. For such a representation $\pi$, it is known that the angles $\theta_\pi(\mathfrak{p})$ follow the Sato-Tate distribution. Fixing an interval $I\subseteq [0,\pi]$, we prove a central limit theorem for the number of angles $\theta_\pi(\mathfrak{p})$ that lie in $I$, as $\mathrm{N}(\mathfrak{p})\to\infty$. The result assumes $\mathfrak{n}$ to be a squarefree integral ideal, and that the components in the weight vector $\underline{k}$ grow suitably fast as a function of $x$.
Knolling bot 2.0: Enhancing Object Organization with Self-supervised Graspability Estimation

Building on recent advancements in transformer based approaches for domestic robots performing knolling, the art of organizing scattered items into neat arrangements. This paper introduces Knolling bot 2.0. Recognizing the challenges posed by piles of objects or items situated closely together, this upgraded system incorporates a self-supervised graspability estimation model. If objects are deemed ungraspable, an additional behavior will be executed to separate the objects before knolling the table. By integrating this grasp prediction mechanism with existing visual perception and transformer based knolling models, an advanced system capable of decluttering and organizing even more complex and densely populated table settings is demonstrated. Experimental evaluations demonstrate the effectiveness of this module, yielding a graspability prediction accuracy of 95.7%.
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts

Visual question answering (VQA) is the task of answering questions about an image. The task assumes an understanding of both the image and the question to provide a natural language answer. VQA has gained popularity in recent years due to its potential applications in a wide range of fields, including robotics, education, and healthcare. In this paper, we focus on knowledge-augmented VQA, where answering the question requires commonsense knowledge, world knowledge, and reasoning about ideas and concepts not present in the image. We propose a multimodal framework that uses language guidance (LG) in the form of rationales, image captions, scene graphs, etc to answer questions more accurately. We benchmark our method on the multi-choice question-answering task of the A-OKVQA, Science-QA, VSR, and IconQA datasets using CLIP and BLIP models. We show that the use of language guidance is a simple but powerful and effective strategy for visual question answering. Our language guidance improves the performance of CLIP by 7.6% and BLIP-2 by 4.8% in the challenging A-OKVQA dataset. We also observe consistent improvement in performance on the Science-QA, VSR, and IconQA datasets when using the proposed language guidances. The implementation of LG-VQA is publicly available at https:// github.com/declare-lab/LG-VQA.
Overall Currents Unbalance Assessment for Several Three-Phase Loads under Sinusoidal Conditions of Four-Wire Circuit

The paper deals with complex-valued negative and zero sequences current unbalance factors, which in addition to the magnitudes ratios also take into account the phase differences between the negative and positive sequences' currents and between the zero and positive sequences' currents. These coefficients allow uniquely locate any currents asymmetry modes. The equations for calculating the general complex negative sequence current unbalance factor and zero sequence current unbalance factor according to the known similar coefficients for each separate three-phase load of a four-wire circuit under sinusoidal voltages and currents conditions are presented. Also the equations for the desired phase currents to be drawn from the power network by a balancing device (in particular a passive or/and active filter or some other type compensator) to eliminate both the negative and zero currents components are offered. Thus, the approach makes it possible to assess the overall current asymmetry and the necessary measures to eliminate it even before connecting three-phase loads, without directly metering the total circuit currents values. An example of calculating the above complex current unbalance factors, based on metering in a PSIM model magnitudes and initial phase angles of currents, is considered. The compensated mode is also demonstrated via using AC current sources of the PSIM model.
Development of Therapeutic Monoclonal Antibodies for Emerging Arbovirus Infections

Antibody-based passive immunotherapy has been used effectively in the treatment and prophylaxis of infectious diseases. Outbreaks of emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to their rapid spread, urging measures and the treatment of infected individuals to combat them. Preparedness in advances in developing antivirals and relevant epidemiological studies protect us from damage and losses. Immunotherapy based on monoclonal antibodies (mAbs) has been shown to be very specific in combating infectious diseases and various other illnesses. Recent advances in mAb discovery techniques have allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and the next-generation strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for dengue, Zika, chikungunya, West Nile and tick-borne encephalitis virus are presented, as well as the protective effect demonstrated in animal model studies.
The impact of cold water releases on the critical period of post‐spawning survival and its implications for Murray cod (Maccullochella peelii peelii): a case study of the Mitta Mitta River, southeastern Australia

The effects of cold water releases, as a by‐product of storing irrigation water in large dams, has been a source of great concern for its impact on native freshwater fish for some time. The Mitta Mitta River, northeast Victoria, is impacted by altered thermal regimes downstream of the fourth largest dam in Australia, Dartmouth dam, with some daily temperatures 10–12°C below normal. Murray cod (Maccullochella peelii peelii) were endemic to the Mitta Mitta River; however, resident Murray cod have not been found in this river since 1992. The response of eggs and hatched larvae from Murray cod to different temperature gradients of water were measured and the post‐spawning survival recorded. As a case study, post‐spawning survival was then inferred from flow data for each year of operation of Dartmouth Dam, recorded since first operation in 1978, and included in a stochastic population model to explore the impact of the altered (historical) thermal regime on population viability. Experimental results revealed no egg and larval survival below 13°C and predicted historical temperature regimes point to more than 15 years of low temperatures in the Mitta Mitta River. Population modelling indicates that the impact of cold water releases on post‐spawning survival is a significant threatening process to the viability of a Murray cod population. Additionally, we consider changes to the thermal regime to explore how the thermal impact of large dams may be minimized on downstream fish populations through incrementally increasing the temperature of the releases. The modelled Murray cod population responds to minor increases in the thermal regime; however, threats are not completely removed until an increase of at least 5–6°C. Copyright © 2005 John Wiley & Sons, Ltd.
Evolution of Treatment Outcomes and Prognostic Factors in Esophageal Cancer Surgery: A Retrospective Analysis of 1500 Consecutive Esophagostomies

Objective: To clarify the surgical outcomes of esophagectomy in Japan and comprehensively evaluate trends over time. It is important to analyze data from a large number of consecutive patients from a single institution. Methods: We evaluated the treatment outcomes, complications, and prognosis of 1500 consecutive patients who underwent esophagectomy during 5 periods: group A (n = 284), 1964–1984; group B (n = 345), 1985–1993; group C (n = 253), 1994–2002; group D (n = 297), 2003–2012; and group E (n = 321), 2013–March 2020. Results: The incidences of squamous cell carcinoma and adenocarcinoma were 93.8% and 3.3%, respectively. The proportion of adenocarcinoma cases has gradually increased over time. The in-hospital mortality rates for groups A, B, C, D, and E were 12%, 4.6%, 1.2%, 2.9%, and 1.5%, respectively. Group A had a significantly higher mortality rate than the other groups (P < 0.0001). Three-year survival rates were 22.2%, 47.8%, 53.4%, 69.9%, and 72.6% in groups A–E, respectively, 5-year survival rates were 17.2%, 41.3%, 49.2%, 63.9%, and 68.4%, respectively (P < 0.0001, group A vs groups D and E). The prognosis improved over time. Multivariate analysis revealed that depth of invasion, lymph node metastasis, the extent of lymph node resection, curative resection, pulmonary complications, and anastomotic leakage were significant independent prognostic factors. However, for recent surgeries (groups D and E), only the depth of invasion, lymph node metastasis, and curative resection were significant independent prognostic factors. Conclusions: Valuable changes in background and prognostic factors occurred over time. These findings will help optimize esophageal cancer management and improve patient outcomes.
Search for Λc+ → pK+π− and Ds+ → K+K+π− Using Genetic Programming Event Selection

We apply a genetic programming technique to search for the doubly Cabibbo suppressed decays Λc+ → pK+π− and Ds+ → K+K+π−. We normalize these decays to their Cabibbo favored partners and find BR(Λc+ → pK+π−)/BR(Λc+ → pK−π+) < 0.46% and BR(Ds+ → K+K+π−)/BR(Ds+ → K−K+π+) < 0.78% at the 90% confidence level (CL). This is the first successful use of genetic programming in a high energy physics data analysis.
Effects of simulated secondary organic aerosol water on PM1 levels and composition over the US

Abstract. Water is a key component of atmospheric aerosol, affecting many aerosol processes including gas-to-particle partitioning of semi-volatile compounds. Water related to secondary organic aerosol (SOAW) is often neglected in atmospheric chemical transport models and is not considered in gas-to-particle partitioning calculations for inorganic species. We use a new inorganic aerosol thermodynamics model, ISORROPIA-lite, which considers the effects of SOAW, to perform chemical transport model simulations for 1 year over the continental United States to quantify its effects on aerosol mass concentration and composition. SOAW can increase average fine aerosol water levels by up to a factor of 2 when secondary organic aerosol (SOA) is a major PM1 component. This is often the case in the south-eastern US, where SOA concentrations are higher. Although the annual average impact of this added water on total dry PM1 concentrations due to increased partitioning of nitrate and ammonium is small (up to 0.1 µg m−3), total dry PM1 increases of up to 2 µg m−3 (with nitrate levels increases of up to 200 %) can occur when RH levels and PM1 concentrations are high.
ASSESSMENT OF PERSONAL TRAVEL ADAPTIVE CAPACITY USING A PARTICIPATORY SURVEY APPROACH

Fuel supply issues have the potential to cause significant travel behaviour change as pressure on oil production spare capacity increases into the future. Transportation planners need information on the transport options available to people and their ability to change to reduce fuel demand. This paper investigates a new transport parameter, travel adaptive capacity, the capability to reduce fuel demand without reducing participation in activities. An online survey, the Travel Adaptive Capacity Assessment (TACA), was used to capture travel activities in a normal week. The interactive survey asks for selection of up to three possible alternatives for each trip including modes, destinations and doing the activity without travel. We propose that these alternatives an be used to calculate travel adaptive capacity (TAC). The survey was conducted in the city of Christchurch and the small rural town of Oamaru, New Zealand. The survey results show a surprisingly high adaptive capacity for a cohort with normally very high level of personal automobile use. We report statistical relationships between adaptive capacity and transport, demographic and geographic factors.
Bilingual Degree Teachers’ Beliefs: A Case Study in a Tertiary Setting

El aprendizaje integrado de contenidos y lengua extranjera (AICLE) es cada vez más importante en España ya que las universidades empiezan a aplicar los grados de itinerario bilingüe en respuesta a los desafíos de un mundo cada vez más globalizado. Actualmente, la investigación sobre las creencias de los profesores en el campo de la educación bilingüe a nivel universitario es escasa y una comprensión más completa de estas creencias puede contribuir al éxito de los programas universitarios de AICLE. Este estudio de casos examina cómo se desarrollaron las creencias de cinco profesores universitarios involucrados en un proyecto bilingüe en el transcurso de un programa de formación de profesorado centrado en AICLE.
Root effects on soil carbon and nitrogen cycling in a Pinus radiata D. Don plantation on a coastal sand

Although the contribution of roots to soil carbon (C) fluxes and biochemical processes is recognised, it is difficult to quantify. One approach to assess their importance is the use of trenched plots, in which C inputs to the soil and respiration by living roots has ceased. We give here an account of C and nitrogen (N) pools and mineralisation in samples taken 27 months after trenching in a 26-year-old Pinus radiata D. Don plantation on a coastal sand (an Aquic Udipsamment); above-ground litter inputs continued throughout the 27-month period. Moisture contents were higher in FH material and mineral soil from the trenched than from the control plots. Trenching had no effect on total organic C and N concentrations, but led to decreases in extractable C, microbial C and N, and CO2-C production values at some depths in the soil profile. Mineral-N concentrations and gross nitrification rates were, in contrast, initially higher in the trenched-plot samples, but were similar in both treatments after incubation of the samples at 25°C for 57 days. Mineral-N concentrations were also higher in the trenched than control mineral soil after in situ incubation. On an area basis (to 20 cm depth of mineral soil), inputs from roots were estimated to account for about 40% of the extractable C pool, 28% of microbial C, 26% of microbial N, and 23% of heterotrophic CO2-C production (0–7 days at 25°C) in the control soil. Overall, our results suggest a tight connection between N cycling rates and the labile C pools derived from below-ground inputs, with nitrification in particular increasing as C availability declined as a result of trenching.