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Seismic interferometry (SI) retrieves the Green function between two receiver locations using their recordings from a boundary of sources. When using sources and receivers only at the surface, the virtual-source gathers retrieved by SI contain pseudo-physical reflections as well as ghost (non-physical) reflections. These ghost reflections are the results of the cross-correlation or auto-correlation (AC) of primary reflections from two different depth levels, and they contain information about the seismic properties of specific layers in the subsurface. We investigated the application of ghost reflections for layer-specific characterization of the shallow subsurface using SI by AC. First, we showed the technique's potential using synthetic data for a subsurface model with a lateral change in velocity, a gradient in depth for velocity, a thickness change and a velocity change of the target layer. Then, we applied the technique to shallow subsurface field data. We also focused on improving the retrieval of ghost reflections by removing the free-surface multiples and muting undesired events in active-source gathers before applying SI. Our results demonstrate that the ghost reflections can be used advantageously to characterize the layer that causes them to appear in the results of SI. Consequently, they can also provide valuable information for imaging and monitoring shallow subsurface structures., Applied Geophysics and Petrophysics

Understanding drought and its multifaceted challenges is crucial for safeguarding food security, promoting environmental sustainability, and fostering socio-economic well-being across the globe. As a consequence of climate change and anthropogenic factors, the occurrence and severity of drought has risen globally. In India, droughts are regular phenomenon affecting about 16% area of country each year which leads to a loss of about 0.5–1% of country’s annual GDP. Hence, the study aims to analyse and predict the meteorological drought in northeast India during 1901 to 2015 using standardised precipitation index (SPI) and analytical techniques such as Mann–Kendall test (MK), innovative trend analysis (ITA), and wavelet approach. In addition, the periodicity of the drought was estimated using Morlet wavelet technique, while discrete wavelet transform (DWT) was applied for decomposing the time series SPI-6 & SPI-12. Study shows that the northeast India experienced moderate drought conditions (SPI-6) in short term and two significant severe droughts (SPI-12) in long term between 1901 and 2015. The trend analysis shows a significant increase in SPI-6 & SPI-12 (p-value 0.01). Further, the combination of parameters i.e. approximation and levels result in the best drought prediction model with higher correlation coefficient and lower error. By using PSO-REPTtree, this study pioneers the use of decomposed parameters to detect trends and develop a drought prediction model. The study is the first step towards establishing drought early warning system that will help decision-makers and farmers to mitigate the impact of drought at the regional level.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a frequent cause of hypoxemic respiratory failure with a mortality rate of approximately 30%. Identifying ARDS subphenotypes based on "focal" or "non-focal" lung morphology has the potential to better target mechanical ventilation strategies of individual patients. However, classifying morphology through chest radiography or computed tomography is either inaccurate or impractical. Lung ultrasound (LUS) is a non-invasive bedside tool that can accurately distinguish "focal" from "non-focal" lung morphology. We hypothesize that LUS-guided personalized mechanical ventilation in ARDS patients leads to a reduction in 90-day mortality compared to conventional mechanical ventilation.METHODS: The Personalized Mechanical Ventilation Guided by UltraSound in Patients with Acute Respiratory Distress Syndrome (PEGASUS) study is an investigator-initiated, international, randomized clinical trial (RCT) that plans to enroll 538 invasively ventilated adult intensive care unit (ICU) patients with moderate to severe ARDS. Eligible patients will receive a LUS exam to classify lung morphology as "focal" or "non-focal". Thereafter, patients will be randomized within 12 h after ARDS diagnosis to receive standard care or personalized ventilation where the ventilation strategy is adjusted to the morphology subphenotype, i.e., higher positive end-expiratory pressure (PEEP) and recruitment maneuvers for "non-focal" ARDS and lower PEEP and prone positioning for "focal" ARDS. The primary endpoint is all-cause mortality at day 90. Secondary outcomes are mortality at day 28, ventilator-free days at day 28, ICU length of stay, ICU mortality, hospital length of stay, hospital mortality, and number of complications (ventilator-associated pneumonia, pneumothorax, and need for rescue therapy). After a pilot phase of 80 patients, the correct interpretation of LUS images and correct application of the intervention within the safe limits

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a frequent cause of hypoxemic respiratory failure with a mortality rate of approximately 30%. Identifying ARDS subphenotypes based on "focal" or "non-focal" lung morphology has the potential to better target mechanical ventilation strategies of individual patients. However, classifying morphology through chest radiography or computed tomography is either inaccurate or impractical. Lung ultrasound (LUS) is a non-invasive bedside tool that can accurately distinguish "focal" from "non-focal" lung morphology. We hypothesize that LUS-guided personalized mechanical ventilation in ARDS patients leads to a reduction in 90-day mortality compared to conventional mechanical ventilation.METHODS: The Personalized Mechanical Ventilation Guided by UltraSound in Patients with Acute Respiratory Distress Syndrome (PEGASUS) study is an investigator-initiated, international, randomized clinical trial (RCT) that plans to enroll 538 invasively ventilated adult intensive care unit (ICU) patients with moderate to severe ARDS. Eligible patients will receive a LUS exam to classify lung morphology as "focal" or "non-focal". Thereafter, patients will be randomized within 12 h after ARDS diagnosis to receive standard care or personalized ventilation where the ventilation strategy is adjusted to the morphology subphenotype, i.e., higher positive end-expiratory pressure (PEEP) and recruitment maneuvers for "non-focal" ARDS and lower PEEP and prone positioning for "focal" ARDS. The primary endpoint is all-cause mortality at day 90. Secondary outcomes are mortality at day 28, ventilator-free days at day 28, ICU length of stay, ICU mortality, hospital length of stay, hospital mortality, and number of complications (ventilator-associated pneumonia, pneumothorax, and need for rescue therapy). After a pilot phase of 80 patients, the correct interpretation of LUS images and correct application of the intervention within the safe limits

Background :Acute respiratory distress syndrome (ARDS) is a frequent cause of hypoxemic respiratory failure with a mortality rate of approximately 30%. Identifying ARDS subphenotypes based on “focal” or “non-focal” lung morphology has the potential to better target mechanical ventilation strategies of individual patients. However, classifying morphology through chest radiography or computed tomography is either inaccurate or impractical. Lung ultrasound (LUS) is a non-invasive bedside tool that can accurately distinguish “focal” from “non-focal” lung morphology. We hypothesize that LUS-guided personalized mechanical ventilation in ARDS patients leads to a reduction in 90-day mortality compared to conventional mechanical ventilation. Methods: The Personalized Mechanical Ventilation Guided by UltraSound in Patients with Acute Respiratory Distress Syndrome (PEGASUS) study is an investigator-initiated, international, randomized clinical trial (RCT) that plans to enroll 538 invasively ventilated adult intensive care unit (ICU) patients with moderate to severe ARDS. Eligible patients will receive a LUS exam to classify lung morphology as “focal” or “non-focal”. Thereafter, patients will be randomized within 12 h after ARDS diagnosis to receive standard care or personalized ventilation where the ventilation strategy is adjusted to the morphology subphenotype, i.e. higher positive end-expiratory pressure (PEEP) and recruitment maneuvers for “non-focal” ARDS and lower PEEP and prone positioning for “focal” ARDS. The primary endpoint is all-cause mortality at day 90. Secondary outcomes are mortality at day 28, ventilator-free days at day 28, ICU length of stay, ICU mortality, hospital length of stay, hospital mortality, and number of complications (ventilator-associated pneumonia, pneumothorax, and need for rescue therapy). After a pilot phase of 80 patients, the correct interpretation of LUS images and correct application of the intervention within the safe limits of mechani, SCOPUS: ar.j, info:eu-repo/semantics/published

Active learning (AL) techniques aim to maximally utilize a labeling budget by iteratively selecting instances that are most likely to improve prediction accuracy. However, their benefit compared to random sampling has not been consistent across various setups, e.g., different datasets, classifiers. In this empirical study, we examine how a combination of different factors might obscure any gains from an AL technique. Focusing on text classification, we rigorously evaluate AL techniques over around 1000 experiments that vary wrt the dataset, batch size, text representation and the classifier. We show that AL is only effective in a narrow set of circumstances. We also address the problem of using metrics that are better aligned with real world expectations. The impact of this study is in its insights for a practitioner: (a) the choice of text representation and classifier is as important as that of an AL technique, (b) choice of the right metric is critical in assessment of the latter, and, finally, (c) reported AL results must be holistically interpreted, accounting for variables other than just the query strategy.

As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data movement between the CPU and memory/storage. While processing in main memory can alleviate these penalties, it is becoming increasingly difficult to keep large datasets entirely in main memory. This has led to a recent push for in-storage computation, where processing is performed inside the storage device. We propose TCAM-SSD, a new framework for search-based computation inside the NAND flash memory arrays of a conventional solid-state drive (SSD), which requires lightweight modifications to only the array periphery and firmware. TCAM-SSD introduces a search manager and link table, which can logically partition the NAND flash memory's contents into search-enabled regions and standard storage regions. Together, these light firmware changes enable TCAM-SSD to seamlessly handle block I/O operations, in addition to new search operations, thereby reducing end-to-end execution time and total data movement. We provide an NVMe-compatible interface that provides programmers with the ability to dynamically allocate data on and make use of TCAM-SSD, allowing the system to be leveraged by a wide variety of applications. We evaluate three example use cases of TCAM-SSD to demonstrate its benefits. For transactional databases, TCAM-SSD can mitigate the performance penalties for applications with large datasets, achieving a 60.9% speedup over a conventional system that retrieves data from the SSD and computes using the CPU. For database analytics, TCAM-SSD provides an average speedup of 17.7x over a conventional system for a collection of analytical queries. For graph analytics, we combine TCAM-SSD's associative search with a sparse data structure, speeding up graph computing for larger-than-memory datasets by 14.5%.

In this paper, we provide expressions for the secrecy outage probability (SOP) for suboptimal and optimal opportunistic scheduling schemes in a reconfigurable intelligent surface (RIS) aided system with multiple eavesdroppers in approximate closed form. A suboptimal scheduling (SS) scheme is analyzed, which is used when the channel state information (CSI) of the eavesdropping links is unavailable, and the optimal scheduling (OS) scheme is also analyzed, which is used when the global CSI is available. For each scheme, we provide a simplified expression for the SOP in the high signal-to-noise ratio (SNR) regime to demonstrate its behavior as a function of the key system parameters. At high SNR, the SOP saturates to a constant level which decreases exponentially with the number of RIS elements in the SS scheme and with the product of the number of RIS elements and the number of users in the OS scheme. We compare the performance of the opportunistic user scheduling schemes with that of a non-orthogonal multiple access (NOMA) based scheduling scheme which chooses a pair of users in each time slot for scheduling and we show that the opportunistic schemes outperform the NOMA-based scheme. We also derive a closed-form expression for the SOP of a decode-and-forward (DF) relay-aided scheduling scheme in order to compare it with that of the RIS-aided system. It is found that the RIS-aided system outperforms the relay-aided systems when the number of RIS elements is sufficiently large. An increased number of RIS elements is required to outperform the relay-aided system at higher operating frequencies.

Artificial intelligence is seen as increasingly important, and potentially profoundly so, but the fields of AI ethics and AI engineering have not fully recognized that these technologies, including large language models (LLMs), will have massive impacts on animals. We argue that this impact matters, because animals matter morally. As a first experiment in evaluating animal consideration in LLMs, we constructed a proof-of-concept Evaluation System, which assesses LLM responses and biases from multiple perspectives. This system evaluates LLM outputs by two criteria: their truthfulness, and the degree of consideration they give to the interests of animals. We tested OpenAI ChatGPT 4 and Anthropic Claude 2.1 using a set of structured queries and predefined normative perspectives. Preliminary results suggest that the outcomes of the tested models can be benchmarked regarding the consideration they give to animals, and that generated positions and biases might be addressed and mitigated with more developed and validated systems. Our research contributes one possible approach to integrating animal ethics in AI, opening pathways for future studies and practical applications in various fields, including education, public policy, and regulation, that involve or relate to animals and society. Overall, this study serves as a step towards more useful and responsible AI systems that better recognize and respect the vital interests and perspectives of all sentient beings., Comment: AAAI 2024 Workshop on Public Sector LLMs: Algorithmic and Sociotechnical Design. 12 pages, 11 figures

Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a DRAM array's massive internal parallelism to execute very-wide data-parallel operations, in a single-instruction multiple-data (SIMD) fashion. However, DRAM rows' large and rigid granularity limit the effectiveness and applicability of PUD in three ways. First, since applications have varying degrees of SIMD parallelism, PUD execution often leads to underutilization, throughput loss, and energy waste. Second, most PUD architectures are limited to the execution of parallel map operations. Third, the need to feed the wide DRAM row with tens of thousands of data elements combined with the lack of adequate compiler support for PUD systems create a programmability barrier. Our goal is to design a flexible PUD system that overcomes the limitations caused by the large and rigid granularity of PUD. To this end, we propose MIMDRAM, a hardware/software co-designed PUD system that introduces new mechanisms to allocate and control only the necessary resources for a given PUD operation. The key idea of MIMDRAM is to leverage fine-grained DRAM (i.e., the ability to independently access smaller segments of a large DRAM row) for PUD computation. MIMDRAM exploits this key idea to enable a multiple-instruction multiple-data (MIMD) execution model in each DRAM subarray. We evaluate MIMDRAM using twelve real-world applications and 495 multi-programmed application mixes. Our evaluation shows that MIMDRAM provides 34x the performance, 14.3x the energy efficiency, 1.7x the throughput, and 1.3x the fairness of a state-of-the-art PUD framework, along with 30.6x and 6.8x the energy efficiency of a high-end CPU and GPU, respectively. MIMDRAM adds small area cost to a DRAM chip (1.11%) and CPU die (0.6%)., Comment: Extended version of HPCA 2024 paper. arXiv admin note: text overlap with arXiv:2109.05881 by other authors

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as $25$ million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: tbd.

Toxicity-related impacts are often omitted or poorly represented in environmental performance assessments of agricultural production systems. Moreover, existing studies usually focus on selected aspects, such as pesticides, and rely on generic data and models, hampering decision support that considers trade-offs and regional characteristics. The present study comprehensively assesses life cycle toxicity impacts of major crop production systems in Thailand, considering all relevant supply chain operations, farm-level field operations, and downstream crop residue burning. Impact characterization factors for farm-level and downstream processes have been specifically parameterized for Thai conditions, and all impacts were translated into damage costs for different scenarios based on Thailand's action plans for agricultural production, air pollution control and energy consumption, to facilitate targeted decision support at the national level. Toxicity-related impacts vary considerably across Thai crop production, ranging from a few hours (cassava, sugarcane, palm oil) to 1.5 months (rice) of average individual human lifetime loss, and from 15 (sugarcane) to 147 (rice) million species fraction lost over time and water volume. Combined, these crop systems caused damage equivalent to >3.5 trillion Thai Baht in 2019, dominated by pesticide and manure/fertilizer-related farm-level emissions for human health and by fertilizer and fuel-related supply chain operations for ecosystem damage. The scenarios could substantially reduce toxicity-related impacts on humans and ecosystems substantially across almost all considered crop production systems, mainly through adopting integrated approaches, including optimal use of crop residues and swine manure, and reducing pesticide use and diesel consumption for field operations. Our results demonstrate that including all life cycle operations and regionalized impact factors is crucial to respectively identify major trade-offs across pro

Existing UAS Traffic Management (UTM) frameworks designate preplanned flight paths to uncrewed aircraft systems (UAS), enabling the UAS to deliver payloads. However, with increasing delivery demand between the source-destination pairs in the urban airspace, UAS will likely experience considerable congestion on the nominal paths. We propose a rule-based congestion mitigation strategy that improves UAS safety and airspace utilization in congested traffic streams. The strategy relies on nominal path information from the UTM and positional information of other UAS in the vicinity. Following the strategy, UAS opts for alternative local paths in the unoccupied airspace surrounding the nominal path and avoids congested regions. The strategy results in UAS traffic exploring and spreading to alternative adjacent routes on encountering congestion. The paper presents queuing models to estimate the expected traffic spread for varying stochastic delivery demand at the source, thus helping to reserve the airspace around the nominal path beforehand to accommodate any foreseen congestion. Simulations are presented to validate the queuing results in the presence of static obstacles and intersecting UAS streams.

Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model. While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders. We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function. Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-the-art solvers on both an industrial circuit design benchmark, and on instances from the 20-year Anniversary Track of the 2022 SAT Competition., Comment: Accepted by KDD 2024

Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency problems, are computationally intensive and scale poorly with increasing element counts, limiting their use in complex geometries. This work introduces FastVPINNs, a tensor-based advancement that significantly reduces computational overhead and improves scalability. Using optimized tensor operations, FastVPINNs achieve a 100-fold reduction in the median training time per epoch compared to traditional hp-VPINNs. With proper choice of hyperparameters, FastVPINNs surpass conventional PINNs in both speed and accuracy, especially in problems with high-frequency solutions. Demonstrated effectiveness in solving inverse problems on complex domains underscores FastVPINNs' potential for widespread application in scientific and engineering challenges, opening new avenues for practical implementations in scientific machine learning., Comment: 31 pages, 19 figures, 4 algorithms

Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) emerge as a potential solution to the Electric Vehicles' (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced prospective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and G2V strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario., Comment: 10 pages, 7 figures, 3 tables, 11 equations

Long-term video generation and prediction remain challenging tasks in computer vision, particularly in partially observable scenarios where cameras are mounted on moving platforms. The interaction between observed image frames and the motion of the recording agent introduces additional complexities. To address these issues, we introduce the Action-Conditioned Video Generation (ACVG) framework, a novel approach that investigates the relationship between actions and generated image frames through a deep dual Generator-Actor architecture. ACVG generates video sequences conditioned on the actions of robots, enabling exploration and analysis of how vision and action mutually influence one another in dynamic environments. We evaluate the framework's effectiveness on an indoor robot motion dataset which consists of sequences of image frames along with the sequences of actions taken by the robotic agent, conducting a comprehensive empirical study comparing ACVG to other state-of-the-art frameworks along with a detailed ablation study.

Point cloud classification refers to the process of assigning semantic labels or categories to individual points within a point cloud data structure. Recent works have explored the extension of pre-trained CLIP to 3D recognition. In this direction, CLIP-based point cloud models like PointCLIP, CLIP2Point have become state-of-the-art methods in the few-shot setup. Although these methods show promising performance for some classes like airplanes, desks, guitars, etc, the performance for some classes like the cup, flower pot, sink, nightstand, etc is still far from satisfactory. This is due to the fact that the adapter of CLIP-based models is trained using randomly sampled N-way K-shot data in the standard supervised learning setup. In this paper, we propose a novel meta-episodic learning framework for CLIP-based point cloud classification, addressing the challenges of limited training examples and sampling unknown classes. Additionally, we introduce dynamic task sampling within the episode based on performance memory. This sampling strategy effectively addresses the challenge of sampling unknown classes, ensuring that the model learns from a diverse range of classes and promotes the exploration of underrepresented categories. By dynamically updating the performance memory, we adaptively prioritize the sampling of classes based on their performance, enhancing the model's ability to handle challenging and real-world scenarios. Experiments show an average performance gain of 3-6\% on ModelNet40 and ScanobjectNN datasets in a few-shot setup.

As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

As we approach the centenary of the discovery of quantum statistics in 1924, it is important to revisit Bose's original derivation of Planck's law usually ignored in most standard presentations of Bose-Einstein statistics. It introduced not only the novel concept of the indistinguishability of photons but also of their intrinsic spin, a fact unknown to most physicists., Comment: 6 pages, no figures

Sexual minority groups do not identify with the sexual inclination of the majority, who are heterosexual, cisgender, and non-intersex in nature. These groups are essentially known as the LGBTQIA+ (Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual) or the Queer community and have their sexual orientations and pronouns. Although the scope of Diversity & Inclusion (D&I) has broadened since COVID-19, and stronger allyship and reduced biases are the most pressing matters. More sensitization and awareness are essential to make sexual minorities have a sense of belonging amongst the mass. This paper attempts to find the representation of sexual minorities in the Indian political discourse and mainstream media (English News). Is it comical, criminal, or conventional? Even if the attitudes towards the Queer community have considerably improved since the decriminalization of Section 377 of the Indian Penal Code (IPC). A new empathetic and inclusive environment is still wanted and seeks the intervention of political leaders to ignite social change holistically. It is qualitative and quantitative research. Secondary data was collected from various sources like journals, websites, articles, and reviews of different newspapers, etc. This research was conducted amongst the people primarily pursuing education in Kolkata and Bangalore to collect primary data. A total of 50 responses were collected with the help of a questionnaire which was circulated digitally to get answers and the analysis is done to find out their views regarding the representation of the LGBTQIA+ community in the Indian political discourse and mainstream media; whether it is ethical or unethical.

Effects of gravitational interaction are generally neglected in particle physics. A first order formulation of gravity is presented to include gravity in Quantum Mechanical Lagrangian of fermions. It is seen that fermions minimally coupled to gravity gives rise to a torsionless effective theory with a quartic interaction. After passing through a thermal background the most generic form of contortion contributes to neutrino effective mass. This effective mass can change the current oscillation parameters., Comment: Based on talk delivered by Indrajit Ghose at the International Conference on Neutrinos and Dark Matter (NuDM-2022), Sharm El-Sheikh, Egypt, 25-28 September, 2022

Propagation of fermions in spacetime requires a spin connection, which can be split into a universal gravitational part and a non-universal ``contorsion'' part. The latter is non-dynamical and can be eliminated from the theory, leaving an effective four-fermion interaction with unknown coupling constants. The generic form of the contorsion-fermion coupling, and thus the four-fermion interaction, breaks chiral symmetry. This interaction affects all fermions -- in particular neutrinos passing through matter will notice a contribution to their effective Hamiltonian, just like the MSW effect coming from weak interactions. Then there is a possibility that this geometrical contribution is not negligible for neutrinos passing through normal matter, provided the coupling constants are not too small. We calculate the matter potential due to this interaction and thus write the conversion and survival probabilities including its effect. We plot conversion probabilities of $\nu_\mu$ to $\nu_e$ and $\nu_\tau$ for a baseline of 1300 km (DUNE) with and without the geometrical interaction. If the geometrical couplings are not too small, it should be possible to observe this effect.

Neutrino mixing is caused by the fact that neutrino flavors are not eigenstates of the free Hamiltonian. This causes oscillations among different neutrino flavors. When neutrinos pass through a medium, weak interactions produce different effective masses for neutrinos of different flavors, leading to a modification of the mixing parameters. In curved spacetime there is an additional contribution to neutrino Hamiltonian from a torsion-induced four-fermion interaction, which also causes neutrino mixing while propagating through fermionic matter. We provide an outline of the calculation of this effect on neutrino oscillation., Comment: Based on talk delivered by Riya Barick at the International Conference on Neutrinos and Dark Matter (NuDM-2022), Sharm El-Sheikh, Egypt, 25-28 September, 2022

Exposure to zero gravity causes many physiological changes which may result to affect the health of people involved in space travel. The current review summarizes current knowledge on the start and progression of space motion sickness, bone loss, muscle loss, cardiovascular disorders, respiratory problems, and neuronal and hormonal problems. Around 70% of astronauts suffer from space adaption syndrome, fluid changes, and head motions. It has also been suggested that otolith asymmetries and Coriolis cross-coupling stimulation are the main causes of space motion sickness. The findings reveal that space flight directly affects the body's normal functioning. Despite an intense training routine, a study of historical data from piloted flights discovered that slow degradation of bone and muscle tissue, along with fluid losses, can eventually lead to kidney stones, musculoskeletal problems, bone fractures, and even problems with other organs of the body. Due to such problems focus is given to curing these problems associated with space travel. Medicines that are used in space and medicinal plants like Spirulina, and Ginseng that are of use for the treatment of these problems associated with space travel are discussed along with the treatment options available for such problems in space and the cautions that must be followed. Also, the herbal medication that can be used in outer space is taken into account. The findings of this study state the necessary precaution that needs to be taken by astronauts in outer space and also provides information for future research to be done on solving these problems.

Exposure to zero gravity causes many physiological changes which may result to affect the health of people involved in space travel. The current review summarizes current knowledge on the start and progression of space motion sickness, bone loss, muscle loss, cardiovascular disorders, respiratory problems, and neuronal and hormonal problems. Around 70% of astronauts suffer from space adaption syndrome, fluid changes, and head motions. It has also been suggested that otolith asymmetries and Coriolis cross-coupling stimulation are the main causes of space motion sickness. The findings reveal that space flight directly affects the body's normal functioning. Despite an intense training routine, a study of historical data from piloted flights discovered that slow degradation of bone and muscle tissue, along with fluid losses, can eventually lead to kidney stones, musculoskeletal problems, bone fractures, and even problems with other organs of the body. Due to such problems focus is given to curing these problems associated with space travel. Medicines that are used in space and medicinal plants like Spirulina, and Ginseng that are of use for the treatment of these problems associated with space travel are discussed along with the treatment options available for such problems in space and the cautions that must be followed. Also, the herbal medication that can be used in outer space is taken into account. The findings of this study state the necessary precaution that needs to be taken by astronauts in outer space and also provides information for future research to be done on solving these problems.

BackgroundMost medical educational programs emphasize clinical observation or clinical skill acquisition, fewer focus upon research. The Danish-American Research Exchange (DARE) program, sponsored by the Lundbeck Foundation, is unique in that the medical student initiates biomedical research collaboration between Danish and US medical institutions. To achieve this, Danish medical students (DARE students) conduct binational mentored research projects while based in the United States for 10 months. In addition, DARE students are introduced to interdisciplinary thinking about how to develop ultra-low-cost healthcare interventions through the '$10 Challenge'.MethodsWe conducted a cross-sectional study of DARE alumni over five consecutive years (2015-2020, n = 24). Research metrics included completion of a research project, primary authorship, and co-authorship of publications. The number of publications, prior to and after the DARE program were enumerated. For the first four cohorts, graduation from medical school and acceptance or intention to enter a joint MD-PhD program also were assessed. Two focus groups were conducted using constructivist grounded theory. Discussions were transcribed, redacted, and coded using Dedoose software.ResultsDARE Medical students were 31.2 years (range 24-35), the majority were women (67%;16/24). The majority (17/24;71%) completed a first author publication in a peer-reviewed journal with a median of 3.9 per DARE alumnus. DARE alumnus reported increased proficiency in biostatistics, epidemiology, coding and public speaking as well as stronger research qualities in creativity, critical thinking, comfort in approaching scientist in both the US and Denmark (p < 0.001 for all). Qualitative key themes included: increased confidence, a deepening of research inquiry and linkage to a research network.ConclusionsPreliminarily, this study suggests that medical students can initiate binational collaboration in medicine. Benefits include

Contrastive Vision-Language Pre-training (CLIP) based point cloud classification model has added a new direction in the point cloud classification research domain. In this thesis, we propose two novel methods for CLIP-based point cloud classification. First, we propose a Pretrained Point Cloud to Image Translation Network (PPCITNet) that produces generalized colored images along with additional salient visual cues to the point cloud depth maps for CLIP based point cloud classification. In addition, we propose a novel viewpoint adapter that combines the view feature processed by each viewpoint as well as the global intertwined knowledge that exists across the multi-view features. Next, we propose a novel meta-episodic learning framework for CLIP-based point cloud classification. In addition, we introduce dynamic task sampling within the episode based on performance memory. The experimental results demonstrate the superior performance of the proposed model over existing state-of-the-art CLIP-based models on ModelNet10, ModelNet40, and ScanobjectNN datasets.

Exposure to zero gravity causes many physiological changes which may result to affect the health of people involved in space travel. The current review summarizes current knowledge on the start and progression of space motion sickness, bone loss, muscle loss, cardiovascular disorders, respiratory problems, and neuronal and hormonal problems. Around 70% of astronauts suffer from space adaption syndrome, fluid changes, and head motions. It has also been suggested that otolith asymmetries and Coriolis cross-coupling stimulation are the main causes of space motion sickness. The findings reveal that space flight directly affects the body's normal functioning. Despite an intense training routine, a study of historical data from piloted flights discovered that slow degradation of bone and muscle tissue, along with fluid losses, can eventually lead to kidney stones, musculoskeletal problems, bone fractures, and even problems with other organs of the body. Due to such problems focus is given to curing these problems associated with space travel. Medicines that are used in space and medicinal plants like Spirulina, and Ginseng that are of use for the treatment of these problems associated with space travel are discussed along with the treatment options available for such problems in space and the cautions that must be followed. Also, the herbal medication that can be used in outer space is taken into account. The findings of this study state the necessary precaution that needs to be taken by astronauts in outer space and also provides information for future research to be done on solving these problems.

Using post-critical reflection data, it is possible to obtain useful information that allows more reliable geological characterization of the subsurface. However, the strong distortion caused by the phase shift in post-critical wavelets makes the use of post-critical reflections rather challenging. For this reason, an approach which is capable of estimating the phase shift of each wavelet of a reflection event in a data-driven manner is desirable. In this vein, in case the frequency spectrum of a wavelet can be correctly estimated, it is possible to estimate the instantaneous phase shift. In this work, we propose an approach which can perform such estimation based on spectral recomposition of seismic data. We design an inversion approach in order to reconstruct the seismic spectrum of the wavelets of a reflection event, which subsequently allows us to estimate the instantaneous phase of each wavelet of the near-surface reflection events without performing prior velocity analysis and/or critical-angle estimation. After finding the instantaneous phase for each wavelet of a reflection event, we show next how one can find the respective phase shifts that can then be corrected., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Applied Geophysics and Petrophysics

Intersecting faults are often ignored in the geomechanical simulation of induced seismicity. To investigate the effects of fault intersection and the resulting reservoir geometry on induced seismicity, caused, for instance, by gas extraction, we have developed 3D geomechanical models considering two intersecting normal faults and the surrounding horst structure. We simulate the stress field and the dynamic fault reactivation in a uniformly depleted reservoir. We observe that a smaller intersection angle increases the incremental Coulomb stress at the lower reservoir juxtaposition, thus changing the temporal rupture pattern of the seismic event. In our dynamic simulation, the rupture propagates from the main fault to the secondary fault. We conclude that the fault intersection has important effects on the induced seismicity and should be taken into account when evaluating the seismicity risk in a specific region., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Applied Geophysics and Petrophysics

Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain-based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of theoretical foundations, processes, models , and roles . Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field.

Using the critical angle information of a reflection event, it is possible to calculate several essential physical parameters that are key to reliable geological characterization of the subsurface. However, estimation of the critical angle usually requires several steps of seismic processing. For this reason, an approach which is capable of estimating the critical angle directly from the data is of interest. Once the critical angle is estimated, it is possible to estimate further the Poisson's ratio and the seismic velocities. In this work, we propose an approach which can perform this estimation, based on spectral recomposition of seismic data. We design an inversion scheme in order to reconstruct the seismic spectrum of wavelets of a reflection event, which subsequently allows us to estimate the critical angle of near-surface reflection events without performing prior velocity analysis. After finding the critical angle, we show next how to estimate the Poisson's ratio and the compressional- and shear-wave velocities of the medium above the reflector. The approach leads to quite accurate values for Poisson's ratio even for noisy data, in case the number of layers is not large., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Applied Geophysics and Petrophysics

Los Rohingya son una minoría musulmana en Myanmar que han sido objeto de discriminación y violencia generalizada y se han visto obligados a escapar por la frontera hacia Bangladesh y la India. Están desnutridos y apátridas; por lo tanto, tienen que vivir en campamentos improvisados. Los recientes enfrentamientos étnicos en el estado de Rakhine (Arakan) de Myanmar entre los Rakhines budistas y los Rohingya musulmanes han captado la atención internacional. Parece como si se hubiera corrido una cortina para exponer una cicatriz horrible. Los musulmanes de Rohingya nunca fueron incluidos en la lista de 137 etnias reconocidas por el Gobierno de Myanmar, lo que provocó su falta de reconocimiento como ciudadanos de Myanmar y la incidencia de tortura y discriminación contra ellos por motivos étnicos y religiosos. El pueblo Rohingya ha sido perseguido durante décadas y ya es hora de que su causa reciba la atención que merece. Sin una investigación más profunda sobre los orígenes de la crisis de los Rohingya, el futuro de esta comunidad de minoría étnica parece sombrío. Debe haber consecuencias para aquellos que perpetran, permiten, ayudan e instigan tales crímenes. La comunidad internacional debe tomar medidas para salvaguardar a los Rohingya, abordar los cargos de crímenes de lesa humanidad y garantizar que las atrocidades y la impunidad no queden sin control durante otra generación. A la luz de la agitación política y las rupturas constitucionales en Myanmar, este artículo intenta arrojar luz sobre las causas de estos atroces abusos contra los Rohingyas. El documento también hace un esfuerzo por investigar la situación de los refugiados Rohingya al observar los principios de no devolución y otras normas consuetudinarias del derecho internacional en países vecinos como India. El artículo continúa discutiendo la posición adoptada por el gobierno indio sobre la definición de la nacionalidad de estos refugiados y ofrece recomendaciones para hacerlo., The Rohingya are a Muslim minority in Myanmar who have faced widespread discrimination and violence, forcing them to escape over the border into Bangladesh and India. Malnourished and stateless, they have to live in improvised camps. Recent ethnic confrontations in Myanmar's Rakhine (Arakan) State between Buddhist Rakhines and Muslim Rohingya have drawn international attention. It is as if a curtain has been pulled back to expose a horrible scar. The Rohingya Muslims were never included in the list of 137 ethnicities recognized by the Government of Myanmar, which has led to their lack of recognition as citizens of Myanmar and to their being subjected to torture and discrimination based on their ethnicity and religion. The Rohingya people have been persecuted for decades, and it is time for their cause to receive the attention it deserves. Without a deeper investigation into the origins of the Rohingya crisis, the future of this ethnic minority community seems bleak. There must be consequences for those who perpetrate, enable, aid, and abet such crimes. The international community must take action to protect the Rohingya, address charges of crimes against humanity, and ensure that atrocities and impunity do not go unchecked for another generation. In light of the political upheavals and constitutional breakdowns in Myanmar, this article attempts to shed light on the causes of such heinous abuses against the Rohingyas. The paper also attemps to examine the predicament of the Rohingya refugees by looking at the non-refoulment principles and other customary norms of international law in neighbouring countries such as India. The article then discusses the Indian government’s position on defining the nationality of these refugees and offers recommendations for doing so.

How do particular genres and forms, with their distinct conventions and histories, shape representations of refugee experience? How do representations of refugeedom transform the conventions of genres such as memoir, theatre, comics, song, the essay, poetry or the novel? In this chapter, we outline a humanities approach to refugee studies with reference to significant contributions in the field. Then, we introduce the genres and forms covered in this collection and the relevance of these forms for claiming human rights: the memoir, the graphic novel, poetry, theatre, song, documentary film, media art, the Bildungsroman and the literary novel., Correction published in: Classon Frangos, M., Ghose, S. (2023). Correction to: Why Refugee Genres? Refugee Representation and Cultural Form. In: Classon Frangos, M., Ghose, S. (eds) Refugee Genres. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-031-09257-2_13

This volume brings together research on the forms, genres, media and histories of refugee migration. Chapters come from a range of disciplines and interdisciplinary approaches, including literature, film studies, performance studies and postcolonial studies. The goal is to bring together chapters that use the perspectives of the arts and humanities to study representations of refugee migration. The chapters of the anthology are organized around specific forms and genres: life-writing and memoir, the graphic novel, theater and music, film and documentary, coming-of-age stories, street literature, and the literary novel.

How do particular genres and forms, with their distinct conventions and histories, shape representations of refugee experience? How do representations of refugeedom transform the conventions of genres such as memoir, theatre, comics, song, the essay, poetry or the novel? In this chapter, we outline a humanities approach to refugee studies with reference to significant contributions in the field. Then, we introduce the genres and forms covered in this collection and the relevance of these forms for claiming human rights: the memoir, the graphic novel, poetry, theatre, song, documentary film, media art, the Bildungsroman and the literary novel.

BACKGROUND: Most medical educational programs emphasize clinical observation or clinical skill acquisition, fewer focus upon research. The Danish-American Research Exchange (DARE) program, sponsored by the Lundbeck Foundation, is unique in that the medical student initiates biomedical research collaboration between Danish and US medical institutions. To achieve this, Danish medical students (DARE students) conduct binational mentored research projects while based in the United States for 10 months. In addition, DARE students are introduced to interdisciplinary thinking about how to develop ultra-low-cost healthcare interventions through the '$10 Challenge'.METHODS: We conducted a cross-sectional study of DARE alumni over five consecutive years (2015-2020, n = 24). Research metrics included completion of a research project, primary authorship, and co-authorship of publications. The number of publications, prior to and after the DARE program were enumerated. For the first four cohorts, graduation from medical school and acceptance or intention to enter a joint MD-PhD program also were assessed. Two focus groups were conducted using constructivist grounded theory. Discussions were transcribed, redacted, and coded using Dedoose software.RESULTS: DARE Medical students were 31.2 years (range 24-35), the majority were women (67%;16/24). The majority (17/24;71%) completed a first author publication in a peer-reviewed journal with a median of 3.9 per DARE alumnus. DARE alumnus reported increased proficiency in biostatistics, epidemiology, coding and public speaking as well as stronger research qualities in creativity, critical thinking, comfort in approaching scientist in both the US and Denmark (p < 0.001 for all). Qualitative key themes included: increased confidence, a deepening of research inquiry and linkage to a research network.CONCLUSIONS: Preliminarily, this study suggests that medical students can initiate binational collaboration in medi

Los Rohingya son una minoría musulmana en Myanmar que han sido objeto de discriminación y violencia generalizada y se han visto obligados a escapar por la frontera hacia Bangladesh y la India. Están desnutridos y apátridas; por lo tanto, tienen que vivir en campamentos improvisados. Los recientes enfrentamientos étnicos en el estado de Rakhine (Arakan) de Myanmar entre los Rakhines budistas y los Rohingya musulmanes han captado la atención internacional. Parece como si se hubiera corrido una cortina para exponer una cicatriz horrible. Los musulmanes de Rohingya nunca fueron incluidos en la lista de 137 etnias reconocidas por el Gobierno de Myanmar, lo que provocó su falta de reconocimiento como ciudadanos de Myanmar y la incidencia de tortura y discriminación contra ellos por motivos étnicos y religiosos. El pueblo Rohingya ha sido perseguido durante décadas y ya es hora de que su causa reciba la atención que merece. Sin una investigación más profunda sobre los orígenes de la crisis de los Rohingya, el futuro de esta comunidad de minoría étnica parece sombrío. Debe haber consecuencias para aquellos que perpetran, permiten, ayudan e instigan tales crímenes. La comunidad internacional debe tomar medidas para salvaguardar a los Rohingya, abordar los cargos de crímenes de lesa humanidad y garantizar que las atrocidades y la impunidad no queden sin control durante otra generación. A la luz de la agitación política y las rupturas constitucionales en Myanmar, este artículo intenta arrojar luz sobre las causas de estos atroces abusos contra los Rohingyas. El documento también hace un esfuerzo por investigar la situación de los refugiados Rohingya al observar los principios de no devolución y otras normas consuetudinarias del derecho internacional en países vecinos como India. El artículo continúa discutiendo la posición adoptada por el gobierno indio sobre la definición de la nacionalidad de estos refugiados y ofrece recomendaciones para hacerlo., The Rohingya are a Muslim minority in Myanmar who have faced widespread discrimination and violence, forcing them to escape over the border into Bangladesh and India. Malnourished and stateless, they have to live in improvised camps. Recent ethnic confrontations in Myanmar's Rakhine (Arakan) State between Buddhist Rakhines and Muslim Rohingya have drawn international attention. It is as if a curtain has been pulled back to expose a horrible scar. The Rohingya Muslims were never included in the list of 137 ethnicities recognized by the Government of Myanmar, which has led to their lack of recognition as citizens of Myanmar and to their being subjected to torture and discrimination based on their ethnicity and religion. The Rohingya people have been persecuted for decades, and it is time for their cause to receive the attention it deserves. Without a deeper investigation into the origins of the Rohingya crisis, the future of this ethnic minority community seems bleak. There must be consequences for those who perpetrate, enable, aid, and abet such crimes. The international community must take action to protect the Rohingya, address charges of crimes against humanity, and ensure that atrocities and impunity do not go unchecked for another generation. In light of the political upheavals and constitutional breakdowns in Myanmar, this article attempts to shed light on the causes of such heinous abuses against the Rohingyas. The paper also attemps to examine the predicament of the Rohingya refugees by looking at the non-refoulment principles and other customary norms of international law in neighbouring countries such as India. The article then discusses the Indian government’s position on defining the nationality of these refugees and offers recommendations for doing so.

Exposure to zero gravity causes many physiological changes which may result to affect the health of people involved in space travel. The current review summarizes current knowledge on the start and progression of space motion sickness, bone loss, muscle loss, cardiovascular disorders, respiratory problems, and neuronal and hormonal problems. Around 70% of astronauts suffer from space adaption syndrome, fluid changes, and head motions. It has also been suggested that otolith asymmetries and Coriolis cross-coupling stimulation are the main causes of space motion sickness. The findings reveal that space flight directly affects the body's normal functioning. Despite an intense training routine, a study of historical data from piloted flights discovered that slow degradation of bone and muscle tissue, along with fluid losses, can eventually lead to kidney stones, musculoskeletal problems, bone fractures, and even problems with other organs of the body. Due to such problems focus is given to curing these problems associated with space travel. Medicines that are used in space and medicinal plants like Spirulina, and Ginseng that are of use for the treatment of these problems associated with space travel are discussed along with the treatment options available for such problems in space and the cautions that must be followed. Also, the herbal medication that can be used in outer space is taken into account. The findings of this study state the necessary precaution that needs to be taken by astronauts in outer space and also provides information for future research to be done on solving these problems.

To investigate the physical processes behind induced seismicities due to, for example, production of hydrocarbons from a reservoir, most of the earlier studies performed geomechanical simulations on a simple reservoir geometry. The effect of fluid depletion is, in general, simulated for such a simple geometry. Neglecting the contribution of realistic 3-D reservoir geometries can lead to a wrong estimation of the incremental stress field. A reliable estimate of the induced stress field is key to producing meaningful simulation results. We perform geomechanical simulations on a simple fault model as well as a more realistic model based on the known geological structures at the earthquake source-region in Zeerijp region, the Netherlands. Our results demonstrate that the angle of the fault intersection affects the incremental stress field, including the effective normal stress, the shear stress, and hence, the Coulomb stress and the SCU value. Our results also show a shift in the rupture pattern and the location of the maximum slip on the fault plane. We conclude that, to properly evaluate the effects of production activities and to simulate precisely the in-situ stress field and the induced seismicity, the incorporation of a realistic reservoir structure in modelling is essential., Applied Geophysics and Petrophysics

The Azambuja fault is a NNE trending structure located 50 km north of Lisbon, the capital and most populous city of Portugal. The fault has been considered as a possible source for the historical, large earthquakes. Understanding this fault is a priority in seismic hazard evaluation of this region. The fault has a clear morphological signature. Miocene and Pliocene sediments are tilted eastward and cut by steeply dipping mesoscale fault segments, presenting reverse and normal offsets with a net downthrow to the east. Neotectonic studies indicate a Quaternary slip on the fault of 0.05–0.06 mm/year. However, no direct evidence of the Azambuja fault affecting the Pleistocene or Holocene sediments was found so far. Here, we present the findings from high-resolution seismic reflection studies using both P- and S-waves over the Holocene deposits. The detection of small-throw faulting in ductile sediments is a challenging task. We show that multiple signatures, like perturbations in the reflection hyperbolae visible in shot and CMP gathers, interruptions of reflectors in stacked sections, lateral seismic velocity variations obtained by horizon velocity analysis, all at coincident locations, strongly suggest that the activity of the Azambuja fault has affected the Holocene sediments in the study area. The lateral velocity variations are corroborated by wavepath eikonal traveltime tomography and velocity analysis supported by seismic modeling. By means of 2D viscoelastic modeling, we explain the absence of fault-related diffractions and negligible back-scattered energy from the fault. Using data from nearby boreholes, we find that the 15 ka old alluvium cover has indeed been disturbed by the presence of shallow fault strands. Considering the estimated vertical throws and the empirical relationships between fault length, co-seismic rupture and magnitude, a slip rate of 0.07 mm/y, slightly larger than previously thought, is expected for this fault., Applied Geophysics and Petrophysics

The Gaia-Multi-Peak (GMP) technique can be used to identify large numbers of dual or lensed AGN candidates at sub-arcsec separation, allowing us to study both multiple SMBHs in the same galaxy and rare, compact lensed systems. The observed samples can be used to test the predictions of the models of SMBH merging once 1) the selection function of the GMP technique is known, and 2) each system has been classified as dual AGN, lensed AGN, or AGN/star alignment. Here we show that the GMP selection is very efficient for separations above 0.15'' when the secondary (fainter) object has magnitude G<20.5. We present the spectroscopic classification of five GMP candidates using VLT/ERIS and Keck/OSIRIS, and compare them with the classifications obtained from: a) the near-IR colors of 7 systems obtained with LBT/LUCI, and b) the analysis of the total, spatially-unresolved spectra. We conclude that colors and integrated spectra can already provide reliable classifications of many systems. Finally, we summarize the confirmed dual AGNs at z>0.5 selected by the GMP technique, and compare this sample with other such systems from the literature, concluding that GMP can provide a large number of confirmed dual AGNs at separations below 7 kpc., Comment: 14 pages,A&A, in press

Understanding drought and its multifaceted challenges is crucial for safeguarding food security, promoting environmental sustainability, and fostering socio-economic well-being across the globe. As a consequence of climate change and anthropogenic factors, the occurrence and severity of drought has risen globally. In India, droughts are regular phenomenon affecting about 16% area of country each year which leads to a loss of about 0.5–1% of country’s annual GDP. Hence, the study aims to analyse and predict the meteorological drought in northeast India during 1901 to 2015 using standardised precipitation index (SPI) and analytical techniques such as Mann–Kendall test (MK), innovative trend analysis (ITA), and wavelet approach. In addition, the periodicity of the drought was estimated using Morlet wavelet technique, while discrete wavelet transform (DWT) was applied for decomposing the time series SPI-6 & SPI-12. Study shows that the northeast India experienced moderate drought conditions (SPI-6) in short term and two significant severe droughts (SPI-12) in long term between 1901 and 2015. The trend analysis shows a significant increase in SPI-6 & SPI-12 (p-value 0.01). Further, the combination of parameters i.e. approximation and levels result in the best drought prediction model with higher correlation coefficient and lower error. By using PSO-REPTtree, this study pioneers the use of decomposed parameters to detect trends and develop a drought prediction model. The study is the first step towards establishing drought early warning system that will help decision-makers and farmers to mitigate the impact of drought at the regional level.

This chapter explores Dina Nayeri’s use of life writing in The Ungrateful Refugee (2019) to represent refugees as political, as opposed to humanitarian, subjects. The camp is a leading trope in her narrative of flight and refuge, used to explore literature as a means for problematizing the damaging, hegemonically determined connection between citizenship and gratitude. In recounting her trajectory from child refugee to successful author, she places special emphasis on her time spent in the refugee camp Barba. The camp was a space of despair and interminable waiting, but also one of intimacy and gossip; when reflecting on her former campmates, she understands them as subjects who tried to shape their own stories, however painfully. This view clashes with the humanitarian paradigm that casts refugees as passive victims and demands gratitude, a prerequisite for empathy and aid. In rejecting this paradigm, she tells her life’s story as one of a collective, weaving it in with her fellow refugees’; the community of the camp enabled her to become a storyteller. She undermines the Bildung plot of individual success, and thereby points to modes of narrating refugee subjectivity that do not rely on eliciting the kind of empathy that functions instrumentally to uphold global inequalities.

Information plays a key role in life and in complex biological systems, and dynamical systems underlie and can be used to represent many complex systems. Indeed, dynamical systems and information processing capabilities may be the hallmarks of life-like systems. In this paper we combine dynamical systems with a computational framework to generate art. The framework can be used to generate aesthetically appealing forms of life-like systems. Our work suggests that we may need an ``aesthetic sense'' to recognize life that we have not seen before. We also provide teaching resources for students in schools and undergraduate institutions.

This volume brings together research on the forms, genres, media and histories of refugee migration. Chapters come from a range of disciplines and interdisciplinary approaches, including literature, film studies, performance studies and postcolonial studies. The goal is to bring together chapters that use the perspectives of the arts and humanities to study representations of refugee migration. The chapters of the anthology are organized around specific forms and genres: life-writing and memoir, the graphic novel, theater and music, film and documentary, coming-of-age stories, street literature, and the literary novel.

Understanding drought and its multifaceted challenges is crucial for safeguarding food security, promoting environmental sustainability, and fostering socio-economic well-being across the globe. As a consequence of climate change and anthropogenic factors, the occurrence and severity of drought has risen globally. In India, droughts are regular phenomenon affecting about 16% area of country each year which leads to a loss of about 0.5–1% of country’s annual GDP. Hence, the study aims to analyse and predict the meteorological drought in northeast India during 1901 to 2015 using standardised precipitation index (SPI) and analytical techniques such as Mann–Kendall test (MK), innovative trend analysis (ITA), and wavelet approach. In addition, the periodicity of the drought was estimated using Morlet wavelet technique, while discrete wavelet transform (DWT) was applied for decomposing the time series SPI-6 & SPI-12. Study shows that the northeast India experienced moderate drought conditions (SPI-6) in short term and two significant severe droughts (SPI-12) in long term between 1901 and 2015. The trend analysis shows a significant increase in SPI-6 & SPI-12 (p-value 0.01). Further, the combination of parameters i.e. approximation and levels result in the best drought prediction model with higher correlation coefficient and lower error. By using PSO-REPTtree, this study pioneers the use of decomposed parameters to detect trends and develop a drought prediction model. The study is the first step towards establishing drought early warning system that will help decision-makers and farmers to mitigate the impact of drought at the regional level.

Contrastive learning has emerged as a premier method for learning representations with or without supervision. Recent studies have shown its utility in graph representation learning for pre-training. Despite successes, the understanding of how to design effective graph augmentations that can capture structural properties common to many different types of downstream graphs remains incomplete. We propose a set of well-motivated graph transformation operations derived via graph spectral analysis to provide a bank of candidates when constructing augmentations for a graph contrastive objective, enabling contrastive learning to capture useful structural representation from pre-training graph datasets. We first present a spectral graph cropping augmentation that involves filtering nodes by applying thresholds to the eigenvalues of the leading Laplacian eigenvectors. Our second novel augmentation reorders the graph frequency components in a structural Laplacian-derived position graph embedding. Further, we introduce a method that leads to improved views of local subgraphs by performing alignment via global random walk embeddings. Our experimental results indicate consistent improvements in out-of-domain graph data transfer compared to state-of-the-art graph contrastive learning methods, shedding light on how to design a graph learner that is able to learn structural properties common to diverse graph types., Comment: To appear in AISTATS 2023