nils-herrmann/clean_scirepeval huggingface dataset:
Computational Investigations of the Lithium Superoxide Dimer Rearrangement on Noisy Quantum Devices.

Quantum chemistry studies of biradical systems are challenging due to the required multiconfigurational nature of the wavefunction. In this work, Variational Quantum Eigensolver (VQE) is used to compute the energy profile for the lithium superoxide dimer rearrangement, involving biradical species, on quantum simulators and devices. Considering that current quantum devices can only handle limited number of qubits, we present guidelines for selecting an appropriate active space to perform computations on chemical systems that require many qubits. We show that with VQE performed with a quantum simulator reproduces results obtained with full-configuration interaction (Full CI) for the chosen active space. However, results deviate from exact values by about 39 mHa for calculations on a quantum device. This deviation can be improved to about 4 mHa using the readout mitigation approach and can be further improved to 2 mHa, approaching chemical accuracy, using the state tomography technique to purify the calculated quantum state.
Fast Robot Arm Inverse Kinematics and Path Planning Under Complex Static and Dynamic Obstacle Constraints.

Described here is a simple, reliable, and quite general method for rapid computation of robot arm inverse kinematic solutions and motion path plans in the presence of complex obstructions. The method derived from the MSC (map-seeking circuit) algorithm, optimized to exploit the characteristics of practical arm configurations. The representation naturally incorporates both arm and obstacle geometries. The consequent performance on modern hardware is suitable for applications requiring real-time response, including smooth continuous avoidance of dynamic obstacles which impinge on the planned path during the traversal of the arm. On high-end GPGPU hardware computation of both final pose for an 8 DOF arm and a smooth obstacle-avoiding motion path to that pose takes approximately 200-300msec depending on the number of waypoints implemented. The mathematics of the method is accessible to high school seniors, making it suitable for broad instruction.
Tidal evolution of dark matter annihilation rates in subhalos

Dark matter halos grow by hierarchical clustering as they merge together to produce ever larger structures. During these merger processes, the smaller halo can potentially survive as a subhalo of the larger halo, so a galaxy-scale halo today likely possesses a rich abundance of substructure. This substructure can greatly boost the rate of dark matter annihilation within the host halo, but the precise magnitude of this boost is clouded by uncertainty about the survival prospects of these subhalos. In particular, tidal forces gradually strip material from the subhalos, reducing their annihilation signals and potentially destroying them. In this work, we use high-resolution idealized N-body simulations to develop and tune a model that can predict the impact of this tidal evolution on the annihilation rates within subhalos. This model predicts the time evolution of a subhalo’s annihilation rate as a function of three physically motivated parameters of the host-subhalo system: the energy injected into subhalo particles per orbit about the host, the ratio of stretching to compressive tidal forces, and the radial distribution of tidal heating within the subhalo. Our model will improve the accuracy of predictions of the magnitude and morphology of annihilation signals from dark matter substructure. Additionally, our parametrization can describe the time evolution of other subhalo properties, so it has implications for understanding aspects of subhalo tidal evolution beyond the annihilation rate.
Shattering of Cosmic Sheets due to Thermal Instabilities: A Formation Channel for Metal-free Lyman Limit Systems

We present a new cosmological zoom-in simulation, where the zoom region consists of two halos with virial mass Mv ∼ 5 × 1012M⊙ and an approximately megaparsec long cosmic filament connecting them at z ∼ 2. Using this simulation, we study the evolution of the intergalactic medium in between these two halos at unprecedented resolution. At 5 ≳ z ≳ 3, the two halos are found to lie in a large intergalactic sheet, or “pancake,” consisting of multiple coplanar dense filaments along which nearly all halos with Mv > 109M⊙ are located. This sheet collapses at z ∼ 5 from the merger of two smaller sheets. The strong shock generated by this merger leads to thermal instabilities in the postshock region, and to a shattering of the sheet resulting in ≲ kiloparsec-scale clouds with temperatures of T ≳ 2 × 104 K and densities of n ≳ 10−3 cm−3, which are pressure confined in a hot medium with T ∼ 106 K and n ≳ 10−5 cm−3. When the sheet is viewed face-on, these cold clouds have neutral hydrogen column densities of NH i > 1017.2 cm−2, making them detectable as Lyman limit systems, though they lie well outside the virial radius of any halo and even well outside the dense filaments. Their chemical composition is pristine, having zero metallicity, similar to several recently observed systems. Since these systems form far from any galaxies, these results are robust to galaxy formation physics, resulting purely from the collapse of large-scale structure and radiative cooling, provided sufficient spatial resolution is available.
Uncertainty-aware Model-based Policy Optimization

Model-based reinforcement learning has the potential to be more sample efficient than model-free approaches. However, existing model-based methods are vulnerable to model bias, which leads to poor generalization and asymptotic performance compared to model-free counterparts. In addition, they are typically based on the model predictive control (MPC) framework, which not only is computationally inefficient at decision time but also does not enable policy transfer due to the lack of an explicit policy representation. In this paper, we propose a novel uncertainty-aware model-based policy optimization framework which solves those issues. In this framework, the agent simultaneously learns an uncertainty-aware dynamics model and optimizes the policy according to these learned models. In the optimization step, the policy gradient is computed by automatic differentiation through the models. With respect to sample efficiency alone, our approach shows promising results on challenging continuous control benchmarks with competitive asymptotic performance and significantly lower sample complexity than state-of-the-art baselines.
Active Learning Solution on Distributed Edge Computing

Industry 4.0 becomes possible through the convergence between Operational and Information Technologies. All the requirements to realize the convergence is integrated on the Fog Platform. Fog Platform is introduced between the cloud server and edge devices when the unprecedented generation of data causes the burden of the cloud server, leading the ineligible latency. In this new paradigm, we divide the computation tasks and push it down to edge devices. Furthermore, local computing (at edge side) may improve privacy and trust. To address these problems, we present a new method, in which we decompose the data aggregation and processing, by dividing them between edge devices and fog nodes intelligently. We apply active learning on edge devices; and federated learning on the fog node which significantly reduces the data samples to train the model as well as the communication cost. To show the effectiveness of the proposed method, we implemented and evaluated its performance for an image classification task. In addition, we consider two settings: massively distributed and non-massively distributed and offer the corresponding solutions.
The Difficulty of Training Sparse Neural Networks

We investigate the difficulties of training sparse neural networks and make new observations about optimization dynamics and the energy landscape within the sparse regime. Recent work of \citep{Gale2019, Liu2018} has shown that sparse ResNet-50 architectures trained on ImageNet-2012 dataset converge to solutions that are significantly worse than those found by pruning. We show that, despite the failure of optimizers, there is a linear path with a monotonically decreasing objective from the initialization to the "good" solution. Additionally, our attempts to find a decreasing objective path from "bad" solutions to the "good" ones in the sparse subspace fail. However, if we allow the path to traverse the dense subspace, then we consistently find a path between two solutions. These findings suggest traversing extra dimensions may be needed to escape stationary points found in the sparse subspace.
Event-Driven Models

In Reinforcement Learning we look for meaning in the flow of input/output information. If we do not find meaning, the information flow is not more than noise to us. Before we are able to find meaning, we should first learn how to discover and identify objects. What is an object? In this article we will demonstrate that an object is an event-driven model. These models are a generalization of action-driven models. In Markov Decision Process we have an action-driven model which changes its state at each step. The advantage of event-driven models is their greater sustainability as they change their states only upon the occurrence of particular events. These events may occur very rarely, therefore the state of the event-driven model is much more predictable.
Dark Matter Signals from Timing Spectra at Neutrino Experiments.

We propose a novel strategy to search for new physics in timing spectra at low-energy neutrino experiments using a pulsed beam, envisioning the situation in which a new particle comes from the decay of its heavier partner with a finite particle width. The timing distribution of events induced by the dark matter (DM) candidate particle scattering at the detector may populate in a relatively narrow range, forming a "resonancelike" shape. Because of this structural feature, the signal may be isolated from the backgrounds, in particular when the backgrounds are uniformly distributed in energy and time. For proof of the principle, we investigate the discovery potential for DM from the decay of a dark photon in the ongoing COHERENT experiment and show the exciting prospects for exploring the associated parameter space with this experiment. We analyze the existing CsI detector data with a timing cut and an energy cut, and we find, for the first time, an excess in the timing distribution that can be explained by such DM. We compare the sensitivity to the kinetic mixing parameter (ε) for current and future COHERENT experiments with the projected limits from LDMX and DUNE.
A Framework for Qualitative Communications Using Big Packet Protocol

In the current Internet architecture, a packet is a minimal or fundamental unit upon which different actions such as classification, forwarding, or discarding are performed by the network nodes. When faced with constrained or poor network conditions, a packet is subjected to undesirable drops and re-transmissions, resulting in unpredictable delays and subsequent traffic overheads in the network. Alternately, we introduce qualitative communication services which allow partial, yet timely, delivery of a packet instead of dropping it entirely. These services allow breaking down packet payloads into smaller units (called chunks), enabling much finer granularity of bandwidth utilization. We propose Packet Wash as anew operation in forwarding nodes to support qualitative services. Upon packet error or network congestion, the forwarding node selectively removes some chunk(s) from the payload based on the relationship among the chunks or the individual signiicance level of each chunk. We also present a qualitative communication framework as well as a Packet Wash directive implemented in a newly evolved data plane technology, called Big Packet Protocol (BPP).
Sensitivity study with a 
D
 and 
B
 mesons modular simulation code of heavy flavor 
RAA
 and azimuthal anisotropies based on beam energy, initial conditions, hadronization, and suppression mechanisms

Heavy flavor probes provide important information about the in-medium properties of the quark-gluon plasma produced in heavy-ion collisions. In this work, we investigate the effects of 2D+1 event-by-event fluctuating hydrodynamic backgrounds on the nuclear suppression factor and momentum anisotropies of heavy flavor mesons and non-photonic electrons. Using the state-of-the-art D and B mesons modular simulation code (called "DAB-MOD"), we perform a systematic comparison of different transport equations in the same background, including a few energy loss models --- with and without energy loss fluctuations --- and a relativistic Langevin model with different drag parametrizations. We present the resulting D and B mesons $R_{AA}$, $v_2$, $v_3$, and $v_4$ as well as multi-particle cumulants, in AuAu collisions at $\sqrt{s_\text{NN}}=200\, \text{GeV}$ and PbPb collisions at $\sqrt{s_\text{NN}}=2.76 \, \text{TeV}$ and $\sqrt{s_\text{NN}}=5.02 \, \text{TeV}$, and compare them to the available experimental data. The $v_2\{4\}/v_2\{2\}$ ratio, which is known to be a powerful probe of the initial conditions and flow fluctuations in the soft sector, is also studied in the context of heavy flavor. We also investigate the correlations between the transverse anisotropies of heavy mesons and all charged particles to better understand how heavy quarks couple to the hydrodynamically expanding quark-gluon plasma.
Simultaneous Prediction Intervals for Patient-Specific Survival Curves

Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models -- known as individual survival distributions (ISDs) -- produces patient-specific survival functions that offer greater descriptive power of patient outcomes than was previously possible. Unfortunately, at the time of writing, ISD models almost universally lack uncertainty quantification. In this paper we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results. Furthermore, we introduce both a modification to the existing method and a novel method for estimating simultaneous prediction intervals and show that they offer competitive performance. It is worth emphasizing that these methods are not limited to survival analysis and can be applied in any context in which sampling the distribution of interest is tractable. Code is available at https://github.com/ssokota/spie.
Reconfigurable Interaction for MAS Modelling

We propose a formalism to model and reason about multi-agent systems. We allow agents to interact and communicate in different modes so that they can pursue joint tasks; agents may dynamically synchronize, exchange data, adapt their behaviour, and reconfigure their communication interfaces. The formalism defines a local behaviour based on shared variables and a global one based on message passing. We extend LTL to be able to reason explicitly about the intentions of the different agents and their interaction protocols. We also study the complexity of satisfiability and model-checking of this extension.
Free Ride on LDPC Coded Transmission

In this paper, we formulate the problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra bandwidth. This is possible since a gap to the channel capacity typically exists for a practical code. A new concept, termed as accessible capacity, is introduced to specify the maximum rate at which the superposition transmission of extra bits is reliable and has a negligible effect on the performance of the coded payload link. For a binary-input output-symmetric (BIOS) memoryless channel, the accessible capacity can be characterized as the difference between the channel capacity and the mutual information rate of the coded payload link, which can be numerically evaluated for very short payload codes. For a general payload code, we present a simple lower bound on the accessible capacity, given by the channel capacity minus the coding rate of the payload code. We then focus on the scenarios where low-density parity-check (LDPC) codes are implemented for the payload link. We propose to transmit extra bits by random superposition for encoding, and exhaustive search (with the aid of statistical learning) for decoding. We further propose, by establishing an auxiliary channel (called syndrome channel) induced from “zero-forcing” over the binary field, to transmit extra bits with structured codes such as repetition codes and first-order Reed-Muller (RM) codes. Numerical results show that up to 60 extra bits can be reliably transmitted along with a rate-1/2 LDPC code of length 8064.
Program Synthesis and Semantic Parsing with Learned Code Idioms

Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate PATOIS on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.
Geometry on all prime Three Manifolds

The point of this work is to construct geometric structures on the oriented closed prime three manifolds that don't at present already have them. One knows these compound prime three manifolds, have canonically up to deformation from the identity, incompressible torus walls whose complementary components are diffeomorphic to "elemental" prime three manifolds carrying single Thurston geometries. This metric geometry is generalized here to Lie geometry meaning an open cover by special coordinate charts in a model space whose transition mappings are related by one of several finite dimensional Lie groups acting on the model space. The Lie group is allowed to vary in a constrained manner from region to region in the manifold. Our geometric version of torus wall crossing after fixing finitely many parameters is rigid using covering spaces, pushouts, and sliding flat toroidal cylinders isometrically together. A new concept, Compound Lie Geometry, describes abstractly what these constructions produce. These Compound Lie Geometries in our case consist of sheaves of germs of charts into the upper half space which are related by four Lie groups acting there. It is key that each germ of the structure can be analytically continued along any path, like solving a classical ODE in the complex plane. This defines for each Lie Geometrized manifold a developing map of its universal cover into upper half space.
Unconventional phase III of high-pressure solid hydrogen

We reassess the phase diagram of high-pressure solid hydrogen using mean-field and many-body wave function-based approaches to determine the nature of phase III of solid hydrogen. To discover the best candidates for phase III, density functional theory calculations within the metageneralized gradient approximation by means of the strongly constrained and appropriately normed (SCAN) semilocal density functional are employed. We study 11 molecular structures with different symmetries, which are the most competitive phases, within the pressure range of 100 to 500 GPa. The SCAN phase diagram predicts that the $C2/c\ensuremath{-}24$ and $P{6}_{1}22\ensuremath{-}36$ structures are the best candidates for phase III with an energy difference of less than 1 meV/atom. To verify the stability of the competitive insulator structures of $C2/c\ensuremath{-}24$ and $P{6}_{1}22\ensuremath{-}36$, we apply the diffusion Monte Carlo (DMC) method to optimize the percentage $\ensuremath{\alpha}$ of exact exchange in the trial many-body wave function. We found that the optimized $\ensuremath{\alpha}$ equals $40%$ and denote the corresponding exchange and correlation functional as PBE1. The energy gain with respect to the well-known hybrid functional PBE0, where $\ensuremath{\alpha}=25%$, varies with density and structure. The PBE1-DMC enthalpy-pressure phase diagram predicts that the $P{6}_{1}22\ensuremath{-}36$ structure is stable up to 210 GPa, where it transforms to the $C2/c\ensuremath{-}24$. Hence, we predict that phase III of high-pressure solid hydrogen is polymorphic.
The syntax of the accounting language: a first step

We review and interpret two propositions published by Ellerman [2014. On double-entry bookkeeping: the mathematical treatment. Accounting Education, 23(5), 483–501] in this journal. The paper builds on this contribution with the view of reconciling the two, apparently dichotomous, perspectives of accounting measurement: the stock and the flow approaches to income. It claims that T-accounts have an underlying algebraic structure suitable for approaching measurement from either or both perspectives. Accountants’ preferences for stocks or flows can be framed in ways which are mutually consistent. This is a first step in addressing this consistency issue. We avoid the difficult mathematics of abstract algebra by applying the concept of syntax to accounting numbers such that the accounting procedure qualifies as a formal language to convey meaning. We are addressing those educators responsible for designing accounting degree programmes, for whom grasping the issue of syntax is important when offering cross-disciplinary options to accounting students with interest in, or need for, engaging with information science.
N-Body Simulations of Gas-Free Disc Galaxies with SMBH Seed in Binary Systems

We have shown the outcome of N-body simulations of the interactions of two disc galaxies without gas with the same mass. Both disc galaxies have halos of dark matter, central bulges and initial supermassive black hole (SMBH) seeds at their centers. The purpose of this work is to study the mass and dynamical evolution of the initial SMBH seed during a Hubble cosmological time. It is a complementation of our previous paper with different initial orbit conditions and by introducing the SMBH seed in the initial galaxy. The disc of the secondary galaxy has coplanar or polar orientation in relation to the disc of the primary galaxy and their initial orbit are eccentric and prograde. The primary and secondary galaxies have mass and size of Milky Way with an initial SMBH seed. We have found that the merger of the primary and secondary discs can result in a final normal disc or a final warped disc. After the fusion of discs, the final one is thicker and larger than the initial disc. The tidal effects are very important, modifying the evolution of the SMBH in the primary and secondary galaxy differently. The mass of the SMBH of the primary galaxy have increased by a factor ranging from 52 to 64 times the initial seed mass, depending on the experiment. However, the mass of the SMBH of the secondary galaxy have increased by a factor ranging from 6 to 33 times the initial SMBH seed mass, depending also on the experiment. Most of the accreted particles have come from the bulge and from the halo, depleting their particles. This could explain why the observations show that the SMBH with masses of approximately $10^6 M_\odot$ are found in many bulgeless galaxies. Only a small number of the accreted particles has come from the disc. In some cases of final merging stage of the two galaxies, the final SMBH of the secondary galaxy was {ejected out of the galaxy}.