Recent Proposals Research Articles

Fill in the Blank? A Discussion of Prefilled Tax Returns in the U.S.

ABSTRACT The U.S. tax system imposes a substantial cost of preparation on individual taxpayers. Prefilled tax returns, in which the government prepares tax returns and provides them to taxpayers for any necessary revisions or approvals, would fundamentally overhaul this practice. Such a program has long been debated in the U.S., including multiple recent legislative proposals of a “Tax Filing Simplification Act.” In this paper, we consider a prefilled tax return system for the U.S., concluding that it offers the opportunity to improve the tax filing experience of many Americans by reducing their involvement in an activity widely perceived as unpleasant. However, effective implementation will require careful design. We draw on the implementation and design of programs in other developed countries, along with relevant academic research, to provide insight into the potential design of a prefilled tax return program in the U.S.

Hippocampal subfields and their neocortical interactions during autobiographical memory

Abstract Advances in ultra-high field 7 Tesla functional magnetic resonance imaging (7 T fMRI) have provided unprecedented opportunities to gain insights into the neural underpinnings supporting human memory. The hippocampus, a heterogeneous brain structure comprising several subfields, plays a central role during vivid re-experiencing of autobiographical memories (AM). However, due to technical limitations, how hippocampal subfields differentially support AM, whether this contribution is specific to one portion along the hippocampal long-axis, and how subfields are functionally connected with other brain regions typically associated with AM retrieval remains elusive. Here, we leveraged technical advances of parallel imaging and employed a submillimeter Echo Planar Imaging sequence over the whole brain while participants re-experienced vivid, detail-rich AM. We found that all hippocampal subfields along the long-axis were engaged during AM retrieval. Nonetheless, only the pre/parasubiculum within the anterior body of the hippocampus contributed over and above to AM retrieval. Moreover, whole-brain functional connectivity analyses of the same data revealed that this part of the hippocampus was the only one that was strongly connected to other brain regions typically associated with AM, such as the ventromedial prefrontal cortex (vmPFC) and medial/lateral parietal regions. In the context of the broader literature, our results support recent proposals that the anterior body of the pre/parasubiculum may play an important role in scene-based cognition, such as its engagement during the re-experiencing of personal past events.

A probabilistic successor representation for context-dependent learning.

Two of the main impediments to learning complex tasks are that relationships between different stimuli, including rewards, can be uncertain and context-dependent. Reinforcement learning (RL) provides a framework for learning, by predicting total future reward directly (model-free RL), or via predictions of future states (model-based RL). Within this framework, "successor representation" (SR) predicts total future occupancy of all states. A recent theoretical proposal suggests that the hippocampus encodes the SR in order to facilitate prediction of future reward. However, this proposal does not take into account how learning should adapt under uncertainty and switches of context. Here, we introduce a theory of learning SRs using prediction errors which includes optimally balancing uncertainty in new observations versus existing knowledge. We then generalize that approach to a multicontext setting, allowing the model to learn and maintain multiple task-specific SRs and infer which one to use at any moment based on the accuracy of its predictions. Thus, the context used for predictions can be determined by both the contents of the states themselves and the distribution of transitions between them. This probabilistic SR model captures animal behavior in tasks which require contextual memory and generalization, and unifies previous SR theory with hippocampal-dependent contextual decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Ethical considerations in AI-enhanced medical decision support systems: A review

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

Topological Strings on Non-commutative Resolutions

In this paper we propose a definition of torsion refined Gopakumar–Vafa (GV) invariants for Calabi–Yau threefolds with terminal nodal singularities that do not admit Kähler crepant resolutions. Physically, the refinement takes into account the charge of five-dimensional BPS states under a discrete gauge symmetry in M-theory. We propose a mathematical definition of the invariants in terms of the geometry of all non-Kähler crepant resolutions taken together. The invariants are encoded in the A-model topological string partition functions associated to non-commutative (nc) resolutions of the Calabi–Yau. Our main example will be a singular degeneration of the generic Calabi–Yau double cover of P3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {P}}^3$$\\end{document} and leads to an enumerative interpretation of the topological string partition function of a hybrid Landau–Ginzburg model. Our results generalize a recent physical proposal made in the context of torus fibered Calabi–Yau manifolds by one of the authors and clarify the associated enumerative geometry.

Quantum ontology without textbooks. Nor overlapping

In this paper, I critically assess two recent proposals for an interpretation-independent understanding of non-relativistic quantum mechanics: the overlap strategy (Fraser & Vickers, 2022) and the textbook account (Egg, 2021). My argument has three steps. I first argue that they presume a Quinean-Carnapian meta-ontological framework that yields flat, structureless ontologies. Second, such ontologies are unable to solve the problems that quantum ontologists want to solve. Finally, only structured ontologies are capable of solving the problems that quantum ontologists want to solve. But they require some dose of speculation. In the end, I defend the conservative way to do quantum ontology, which is (and must be) speculative and non-neutral.

Obstacles to Electric Vehicle Adoption in India and Proposed Remedies

The inductive approach focuses on analyzing the factors influencing Electric Vehicle (EV) adoption in India, while the deductive approach centers on understanding how these factors play out in developed nations and their applicability to developing countries. By iteratively applying the inductive-deductive approach, a taxonomy is developed categorizing these factors into micro-, macro-, and meso-level antecedents. This taxonomy serves as a framework for systematically organizing cohesive initiatives to promote EV adoption in developing nations, stressing the importance of tailoring these factors to the unique infrastructural, economic, and market conditions of such countries. One significant challenge hindering the adoption of EVs is the concern over their limited driving range. Recent proposals suggest the implementation of dynamic wireless charging, enabling power exchange between vehicles and the grid while in motion. This paper highlights the importance of optimizing EV routes requiring charging to maximize the utilization of Mobile Energy Disseminators (MEDs) functioning as mobile charging stations. The growing popularity of EVs has brought about a corresponding increase in challenges. Lengthy waiting times at charging stations present a major hurdle to widespread EV adoption. To address this, battery swapping stations (BSSs) offer an efficient solution, reducing wait times and promoting healthy recharging cycles for batteries. Additionally, these swapping stations create opportunities not only for EVs but also for power systems, providing regulation services to the grid, particularly beneficial for smaller networks like microgrid (MG) systems. This study delves into determining the optimal location and size of swap stations to maximize revenue within an MG system.

LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.

Where grief education goes to die? A response to making learning about grief, death, and loss mandatory in Scotland’s Curriculum for Excellence

This rapid response raises challenges to Dawson et al’s (2023) recent proposals for mandatory grief education in schools; in particular, it considers curriculum crowding, the limitations of legal mandates and initial-teacher education. It proposes collaborative working between specialist groups as a way forward.

Medicare Eligibility and Reported Support for Proposals to Expand Medicare

This study estimates the association between Medicare eligibility and support for recent proposals to expand program participation and benefits.

Phonon thermal transport shaped by strong spin-phonon scattering in a Kitaev material Na2Co2TeO6

The report of a half-quantized thermal Hall effect and oscillatory structures in the magnetothermal conductivity in the Kitaev material α-RuCl3 have sparked a strong debate on whether it is generated by Majorana fermion edge currents, spinon Fermi surface, or whether other more conventional mechanisms are at its origin. Here, we report low temperature thermal conductivity (κ) of another candidate Kitaev material, Na2Co2TeO6. The application of a magnetic field (B) along different principal axes of the crystal reveals a strong directional-dependent B impact on κ, while no evidence for mobile quasiparticles except phonons can be concluded at any field. Instead, severely scattered phonon transport prevails across the B−T phase diagram, revealing cascades of phase transitions for all B directions. Our results thus cast doubt on recent proposals for significant itinerant magnetic excitations in Na2Co2TeO6, and emphasize the importance of discriminating true spin liquid transport properties from scattered phonons in candidate materials.

No, Galatians 4:3 τὰ στοιχεῖα τοῦ κόσμου Does Not Refer to a Schmuck

Abstract This note refutes a recent proposal that κόσμος in the phrase τὰ στοιχεῖα τοῦ κόσμου (“the elemental constituents/principles/spirits of the world”) in Gal 4:3 is a reference to the foreskin.

How does Carer’s Allowance in the UK construct family carers? History and recent developments

This article explores the historical development of Carer’s Allowance in the UK, alongside legal challenges and recent proposals for changes to it in Scotland. Eligibility criteria for receipt of this allowance construct carers as people of ‘working age’ who provide care within dyadic relationships and exclude people whose unpaid caring work is more complex and/or interdependent. Disabled people are simultaneously constructed as dependent recipients of this care. Carer’s Allowance provides symbolic but conditional recognition of unpaid caring, while doing little to meet the financial or support needs of carers. This symbolism reinforces the role of gendered families as the main providers of care and of disabled people as dependent.

Exploring the expansion of the universe using the Grüneisen parameter

For a perfect fluid, pressure p and energy density ρ are related via the equation of state (EOS) ω=p/ρ, where ω is the EOS parameter, being its interpretation usually constrained to a numerical value for each universe era. Here, based on the Mie–Grüneisen EOS, we show that ω is recognized as the effective Grüneisen parameter Γeff, whose singular contribution, the so-called Grüneisen ratio Γ, quantifies the barocaloric effect. Our analysis suggests that the negative p associated with dark-energy implies a metastable state and that in the dark-energy-dominated era ω is time-dependent, which reinforces recent proposals of a time-dependent cosmological constant. Furthermore, we demonstrate that Γeff is embodied in the energy–momentum stress tensor in the Einstein field equations, enabling us to analyse, in the frame of an imperfect fluid picture, anisotropic effects of the universe expansion. We propose that upon going from decelerated- to accelerated-expansion, a phase transition-like behaviour can be inferred. Yet, our analysis in terms of entropy, Γ, and a by us adapted version of Avramov/Casalini’s model to Cosmology unveil hidden aspects related to the expansion of the universe. Our findings pave the way to interpret cosmological phenomena in connection with concepts of condensed matter Physics via Γeff.

How to Regulate Platforms Through a Non-Exploitative User-Generated-Content Levy

The democratization of technology to re-create content and make that content publicly available has spurred a wave of user-generated content (UGC), which has produced remarkable social and economic benefits. However, under current copyright law, UGC creators face the dilemma of being deterred from creating UGC because of the risk of copyright infringement, copyright owners can rarely obtain remuneration from UGC, and UGC platforms profit from UGC without being held liable for copyright infringement. Recent proposals to extend fair use and compulsory licenses to UGC creators and impose direct liability on UGC platforms cannot solve the UGC dilemma due to the inadequate or unreasonable regulation of UGC platforms. This study aims to solve the UGC dilemma by proposing a non-exploitative UGC levy on UGC platforms. We demonstrate the necessity of the non-exploitative UGC levy by conducting a comparative study of existing proposals and illustrate the feasibility of the non-exploitative UGC levy through an institutional analysis of its framework and enforcement mechanisms. Justification of the proposed levy and responses to possible criticism are also provided. The levy scheme also provides inspiration for how copyright law can address burgeoning artificial intelligence-generated content (AIGC).

Taxonomy and nomenclature of Oophila amblystomatis (Chlorophyceae, Chlamydomonadales).

The unicellular green alga Oophila amblystomatis was named by Lambert in 1905 based upon its association with egg masses of the spotted salamander Ambystoma maculatum. We collected algal cells from Lambert's original egg capsule preparations that were contributed to Phycotheca Boreali-Americana (PBA) in 1905 and subjected them to DNA extraction and PCR with O. amblystomatis-specific 18S rRNA gene primers. DNA amplified from these preparations was cloned and nine clones were sequenced. Along with representative sequences from the Oophila clade and Chlorophyceae, a phylogenetic tree was inferred. Seven sequences clustered within the Oophila clade and two clustered with Chlamydomonas moewusii, which is included in a sister clade to Oophila. By sequencing algal material from the egg capsules of representative type material we can unambiguously characterize O. amblystomatis and define a monophyletic clade centered on this type material. Accordingly, we reject a recent proposal that this species be transferred to Chlorococcum.

Third Generation Therapies in the treatment of Grief

In the face of the recent COVID-19 pandemic, the number of deaths has grown considerably, the new conditions in which the declines are occurring have led to a complication and aggravation of the grief that requires the review of the most recent proposals before the intervention of the same . Third Generation Therapies (TTG) emerge as an alternative in the treatment of psychological problems that are based on the creation of evidence-based interventions, regarding the process of mourning, Acceptance and Commitment Therapy (ACT) and Mindfulness (My) have yielded better results in the face of this problem, by directing their objective to make the experiences of suffering more flexible and tolerate, thereby achieving the acceptance of pain and reduction of avoidance, and assuming the process of experiencing pain as a fundamental part of the life, focusing on the present and highlighting values ​​despite the loss.

Polymorphic Reachability Types: Tracking Freshness, Aliasing, and Separation in Higher-Order Generic Programs

Fueled by the success of Rust, many programming languages are adding substructural features to their type systems. The promise of tracking properties such as lifetimes and sharing is tremendous, not just for low-level memory management, but also for controlling higher-level resources and capabilities. But so are the difficulties in adapting successful techniques from Rust to higher-level languages, where they need to interact with other advanced features, especially various flavors of functional and type-level abstraction. What would it take to bring full-fidelity reasoning about lifetimes and sharing to mainstream languages? Reachability types are a recent proposal that has shown promise in scaling to higher-order but monomorphic settings, tracking aliasing and separation on top of a substrate inspired by separation logic. However, naive extensions on top of the prior reachability type system λ * with type polymorphism and/or precise reachability polymorphism are unsound, making λ * unsuitable for adoption in real languages. Combining reachability and type polymorphism that is precise, sound, and parametric remains an open challenge. This paper presents a rethinking of the design of reachability tracking and proposes new polymorphic reachability type systems. We introduce a new freshness qualifier to indicate variables whose reachability sets may grow during evaluation steps. The new system tracks variables reachable in a single step and computes transitive closures only when necessary, thus preserving chains of reachability over known variables that can be refined using substitution. These ideas yield the simply-typed λ ✦ -calculus with precise lightweight, i.e., quantifier-free, reachability polymorphism, and the F <: ✦ -calculus with bounded parametric polymorphism over types and reachability qualifiers, paving the way for making true tracking of lifetimes and sharing practical for mainstream languages. We prove type soundness and the preservation of separation property in Coq. We discuss various applications (e.g., safe capability programming), possible effect system extensions, and compare our system with Scala’s capture types.

African savanna raptors show evidence of widespread population collapse and a growing dependence on protected areas.

The conversion of natural habitats to farmland is a major cause of biodiversity loss and poses the greatest extinction risk to birds worldwide. Tropical raptors are of particular concern, being relatively slow-breeding apex predators and scavengers, whose disappearance can trigger extensive cascading effects. Many of Africa's raptors are at considerable risk from habitat conversion, prey-base depletion and persecution, driven principally by human population expansion. Here we describe multiregional trends among 42 African raptor species, 88% of which have declined over a ca. 20-40-yr period, with 69% exceeding the International Union for Conservation of Nature criteria classifying species at risk of extinction. Large raptors had experienced significantly steeper declines than smaller species, and this disparity was more pronounced on unprotected land. Declines were greater in West Africa than elsewhere, and more than twice as severe outside of protected areas (PAs) than within. Worryingly, species suffering the steepest declines had become significantly more dependent on PAs, demonstrating the importance of expanding conservation areas to cover 30% of land by 2030-a key target agreed at the UN Convention on Biological Diversity COP15. Our findings also highlight the significance of a recent African-led proposal to strengthen PA management-initiatives considered fundamental to safeguarding global biodiversity, ecosystem functioning and climate resilience.

CARE: Cloudified Android With Optimized Rendering Platform

Due to the excellent rendering capabilities, GPUs are mainstream accelerators in the Cloud-rendering industry. However, current Cloud-rendering systems suffer from a CPU-GPU workload imbalance that not only degrades application performance but also causes a significant waste of GPU resources. Recent proposals (such as API-forwarding and c-GPU) for improving CPU-GPU balance are promising but fail to solve system-resource redundancy issues (i.e., each instance tends to occupy all resources, exceeding its requirements). Such behavior will increase CPU load and lower effective GPU utilization. To demonstrate the severity of the issue, we evaluated real-world applications and results show that in most cases, nearly 50% of resources are useless. To solve this problem, we present CARE, the first framework intended to reduce the system-level redundancy by cloudifying the system from monolithic to Cloud-native. To allow users to configure required services, CARE puts forward a functional unit called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Configurable Android (CA)</i> . To allow multiple instances to share certain types of resources, CARE innovates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sharing Resource (SR)</i> . To reduce the unused services, CARE introduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pruning Resources (PR)</i> . To further alleviate the CPU pressure and achieve CPU-GPU balance, we propose rShare, a system aiming at enhancing CPU effective utilization and increasing Android instance density of the Cloud-rendering platform. Based on Kubernetes, rShare divides all the CPUs into non-overlapping shared CPU pools, allocates instances to pools within milliseconds, and dynamically migrates them by tracking their QoS status. So far, CARE primarily focuses on Android systems and can handle 60 heavyweight instances (e.g., KOG (King of Glory)) on Intel®SG1. rShare can apply instance allocation within milliseconds and increase the platform density by 39.4%.