Miracle-dz/UniFAR-FTUs huggingface dataset:
Freezing Rate Due to Heterogeneous Nucleation

Abstract The heterogeneous nucleation of ice from supercooled water is influenced by the nature of the foreign nuclei that serve as the sites for ice embryo formation, and by the stochastic nature of the process of embryo growth to critical size. The relative roles of these two factors have been the subject of some debate, especially as they influence the way nucleation of ice is modeled in clouds. “Freezing rate” is defined as the time-dependent rate at which a population of macroscopically identical samples (e.g., drops in a volume of air) freeze due to the nuclei contained in them. Freezing rate is the combined result of nucleus content and of time dependence. The time-dependent freezing rate model (TDFR) is consistent with available empirical evidence. For droplets cooled at rates of the order of −1°C per min, the nucleus content, or nucleus spectrum, predicts the freezing rate with reasonable accuracy. For samples exposed to a fixed temperature, the time dependence of the freezing rate becomes import.
(Noisy) Gap Cycle Counting Strikes Back: Random Order Streaming Lower Bounds for Connected Components and Beyond

We continue the study of the communication complexity of gap cycle counting problems. These problems have been introduced by Verbin and Yu [SODA 2011] and have found numerous applications in proving streaming lower bounds. In the noisy gap cycle counting problem (NGC), there is a small integer k ≥ 1 and an n-vertex graph consisted of vertex-disjoint union of either k-cycles or 2k-cycles, plus O(n/k) disjoint paths of length k−1 in both cases (“noise”). The edges of this graph are partitioned between Alice and Bob whose goal is to decide which case the graph belongs to with minimal communication from Alice to Bob. We study the robust communication complexity of NGC—à la Chakrabarti, Cormode, and McGregor [STOC 2008]—namely, when edges are partitioned randomly between the players. This is in contrast to all prior work on gap cycle counting problems in adversarial partitions. While NGC can be solved trivially with zero communication when k < logn, we prove that when k is a constant factor larger than logn, the robust (one-way) communication complexity of NGC is Ω(n) bits. As a corollary of this result, we can prove several new graph streaming lower bounds for random order streams. In particular, we show that any streaming algorithm that for every > 0 estimates the number of connected components of a graph presented in a random order stream to within an · n additive factor requires 2Ω(1/є) space, settling a conjecture of Peng and Sohler [SODA 2018]. We further discuss new implications of our lower bounds to other problems such as estimating size of maximum matchings and independent sets on planar graphs, random walks, as well as to stochastic streams.
A New Visual & Inertial Based Satellite Quality Evaluation Method

Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite quality evaluation method is proposed. This method can judge whether the GPS (Global Positioning System) is interfered by comparing the visual-inertial navigation information with the GPS data information in the geographic coordinate system. This method enables the UAV to quickly switch the navigation mode under the condition of being disturbed, which ensures that the navigation function is accurate and free from interference, and provides a more solid foundation for the navigation of the UAV and other platforms.
Quantum Hydrodynamics of Charge Carriers in Graphene

Graphene is the carbon monolayer. This material has a lot of interesting properties and is in a centre of many present day researches. In literature graphene is considered as inde- pendent system and as a part of semiconductor geterostructures and fleld efiect transistor. Thus, especially the graphene conductivity properties and properties of graphene carriers have been studied. One of the unusual and important properties of graphene carriers is the carrier disper- sion. The energy of free nonrelativistic electron is proportional to the square of the momentum then the dispersion law is quadratic. Novoselov etal. shown that graphene carriers has linear dis- persion law and they are massless. This result is analogous to the free ultrarelativistic electrons and massless particles. In quantum case the massless Dirac's equation gives the linear dispersion law. Thereby, the massless Dirac's equation was suggested for description of grapnene carrier dynamics. Instead of speed of light in Dirac's equation the Fermi velocity graphene electron were used. Properties of single carrier quasi-particle in graphene is a fundamental properties which give us understanding of microscopic carrier dynamics, but for understanding of a graphene role in semiconductor structures we need to study collective dynamics of graphene carriers. For this purpose in literature many-particle massless Dirac's equation including the Coulomb interaction between carriers is used. Feynman's diagram method has been used in many papers for studying collective properties of graphene carriers. In this paper we present a new method of studying of graphene carriers collective properties. This method is called many-particle quantum hydrodynamics (QHD). One is based on many-particle massless Dirac's equation. QHD method allows us to derive equation which described dynamic of particles in physical space (for graphene it is a two dimensional space) instead of 2N dimensional conflguration space where the wave's spinor is deflned. We derive QHD equations for the graphene carriers and use these equations for the calculation of dispersion properties of collective excitations in graphene. We suppose that graphene situated in external uniform magnetic fleld directed at difierent angles to the grapheme plane. As the results we present dispersion dependencies for collective excitations in grapheme and make comparison with the results of other authors which they found by other methods.
A Hybrid Planning System for Smart Charging of Electric Fleets

Electric vehicle (EV) fleets are well suited for last-mile deliveries both from sustainability and operational cost perspectives. To ensure economic parity with non-EV options, even captive chargers for EV fleets need to be managed intelligently. Specifically, the EVs needs to be adequately charged for their entire delivery runs while handling reduced time flexibility between runs; limited number of chargers; and deviations from the planned schedule. Existing works either solve smaller instances of this problem optimally, or larger instances with significant sub-optimality. In addition, they typically consider either day-ahead or real-time planning in isolation. We complement existing works with a hybrid approach that first identifies a day-ahead plan for assigning EVs to chargers; and then uses online replanning to handle any deviations in real-time. For the day-ahead planning, we use a learning agent (LA) that learns to assign EVs to chargers over several problem instances. Because the agent solves a given instance during its testing phase, it achieves scale in problem size with limited sub-optimality. For the online replanning, we use a greedy heuristic that dynamically refines the day-ahead plan to handle delays in EV arrivals. We evaluate our approach using representative datasets. As baselines for the LA, we use an exact mixed-integer linear program (MILP) (greedy heuristic) for small (large) problem instances. As baselines for the replanning, we use no-planning and no-replanning. Our experiments show that LA performs better (8.5-14%) than greedy heuristic in large problem instances, while being reasonably close (< 22%) to the optimal in smaller instances. For online replanning, our approach performs about 7-20% better than no-planning and no-replanning for a range of delay profiles.
NP-Hardness of Approximating Meta-Complexity: A Cryptographic Approach

It is a long-standing open problem whether the Minimum Circuit Size Problem (MCSP) and related meta-complexity problems are NP-complete. Even for the rare cases where the NP-hardness of meta-complexity problems are known, we only know very weak hardness of approximation. In this work, we prove NP-hardness of approximating meta-complexity with nearly-optimal approximation gaps. Our key idea is to use cryptographic constructions in our reductions, where the security of the cryptographic construction implies the correctness of the reduction. We present both conditional and unconditional hardness of approximation results as follows. 1. Assuming subexponentially-secure witness encryption exists, we prove essentially optimal NP-hardness of approximating conditional time-bounded Kolmogorov complexity (Kt(x | y)) in the regime where t >> |y|. Previously, the best hardness of approximation known was a |x|1/ (loglog|x|) factor and only in the sublinear regime (t << |y|). 2. Unconditionally, we show that for any constant c > 1, the Minimum Oracle Circuit Size Problem (MOCSP) is NP-hard to approximate, where Yes instances have circuit complexity at most s, and No instances have circuit complexity at least sc. Our reduction builds on a witness encryption construction proposed by Garg, Gentry, Sahai, and Waters (STOC’13). Previously, it was unknown whether it is NP-hard to distinguish between oracle circuit complexity s versus 10slogN. 3. Finally, we define a “multi-valued” version of MCSP, called mvMCSP, and show that w.p. 1 over a random oracle O, it is NP-hard to approximate mvMCSPO under quasi-polynomial-time reductions with an O oracle. Intriguingly, this result follows almost directly from the security of Micali’s CS Proofs (Micali, SICOMP’00). In conclusion, we give three results convincingly demonstrating the power of cryptographic techniques in proving NP-hardness of approximating meta-complexity.
Research on the Generation of Emotional Dialogue Statements in Generative Adversarial Networks

With the continuous progress of neural network technology in different fields, people have put forward higher requirements and prospects for the research of deep neural network technology in the field of artificial intelligence, and language intelligence is the core problem of artificial intelligence. As an important research content in the field of natural language processing, dialogue generation has been widely concerned by the academic community for a long time, and this research is also widely used in people's daily lives, such as medical Q&A, e-commerce shopping, emotional response, etc. There are still many problems in the existing model for dialogue generation research, such as easy to produce universal replies ("ok", "emm", etc.), the emotional information of the reply statement is not strong enough, and the response is not related to the subject. For this problem, we propose to use an emotional dialogue generation model based on a generative adversarial network to generate statements with emotional responses, and our network has a generator and multi-level discriminator. In terms of discriminators, multi-level discourse discriminators are introduced to guide the generation of replies and the strengthening of emotional information, to discriminate emotions at the individual word level and sentence level, to solve the problem that the same text may be different in different semantic environments and the emotional information of some words is not clear in some cases, in order to achieve maximum accuracy the emotional discrimination results are fed back to the generator and guide the generation of generator statements. In addition, compared with the traditional generative adversarial network, the task processing ability for large pieces of text is improved, and the model has been shown to produce emotional reply statements with better baseline levels than in the past.
Fast Position-based Multi-Agent Group Dynamics

We present a novel method for simulating groups moving in formation. Recent approaches for simulating group motion operate via forces or velocity-connections. While such approaches are effective for several cases, they do not easily scale to large crowds, irregular formation shapes, and they provide limited fine-grain control over agent and group behaviors. In this paper we propose a novel approach that addresses these difficulties via positional constraints, with a position-based dynamics solver. Our approach allows real-time, interactive simulation of a variety of group numbers, formation shapes, and scenarios of up to thousands of agents.
A CBS Shaper Based on DDQN

For the problem of using time-aware shaping combined with credit shaping-based hybrid traffic scheduling to achieve efficient transmission of hybrid streams, an improvement of CBS shaper based on deep reinforcement learning is proposed. By setting appropriate reward values, this mechanism greatly satisfies the different requirements of time delay for various types of audio and video streams and greatly reduces the delay and loss rate of BE streams without affecting the Time Triggered Flow (TT). The experimental results show that the algorithm improves the delay of AVB streams under the worst conditions. At the same time, the minimum delay and jitter are greatly improved compared with the classical model.
Dynamic ((1+𝜖) ln 𝑛)-Approximation Algorithms for Minimum Set Cover and Dominating Set

The minimum set cover (MSC) problem admits two classic algorithms: a greedy lnn-approximation and a primal-dual f-approximation, where n is the universe size and f is the maximum frequency of an element. Both algorithms are simple and efficient, and remarkably — one cannot improve these approximations under hardness results by more than a factor of (1+є), for any constant є > 0. In their pioneering work, Gupta et al. [STOC’17] showed that the greedy algorithm can be dynamized to achieve O(logn)-approximation with update time O(f logn). Building on this result, Hjuler et al. [STACS’18] dynamized the greedy minimum dominating set (MDS) algorithm, achieving a similar approximation with update time O(Δ logn) (the analog of O(f logn)), albeit for unweighted instances. The approximations of both algorithms, which are the state-of-the-art, exceed the static lnn-approximation by a rather large constant factor. In sharp contrast, the current best dynamic primal-dual MSC algorithms, by Bhattacharya et al. [SODA’21] and Assadi-Solomon [ESA’21], both with update time O(f2) — exceed the static f-approximation by a factor of (at most) 1+є, for any є > 0. This paper aims to bridge the gap between the best approximation factor of the dynamic greedy MSC and MDS algorithms and the static lnn bound. We present dynamic algorithms for weighted greedy MSC and MDS with approximation (1+є)lnn for any є > 0, while achieving the same update time (ignoring dependencies on є) of the best previous algorithms (with approximation significantly larger than lnn). Moreover, we prove that the same algorithms achieve O(min{ logn, logC }) amortized recourse; the recourse measures the number of changes to the maintained structure per update step, and the cost of each set lies in the range [1/C,1].
A Fast Response Neighbor Discovery Algorithm in Low-Duty-Cycle Mobile Sensor Networks

With the rapid development of the Internet of Things, wireless sensor network, one of its important supporting technologies, has attracted more and more attention. We will work in the low duty cycle wireless sensor network, called low duty cycle wireless sensor network. Neighbor discovery is the most initial but essential work in low duty cycle wireless sensor networks. Although some neighbor discovery algorithms can also achieve neighbor discovery, the average discovery delay is long, and it is difficult to achieve the ability to respond quickly. How to make the nodes in the network quickly realize neighbor discovery is a difficult problem in current research. This paper proposes a group-based fast-response neighbor discovery algorithm (GBFR, in short). At the beginning of the time period, the nodes search for their neighbors by sending a short beacon message, so that the nodes group in pairs. By exchanging neighbor work schedules, nodes know ahead of time some other grouped potential neighbors. Combining the relative distance-based algorithm and node movement, it can selectively recommend suitable neighbors so that nodes can wake up actively and determine whether they are neighbors, thereby speeding up neighbor discovery, reducing communication energy consumption, and improving network life. In this paper, a large number of simulation experiments show that the algorithm has achieved good results in reducing the discovery delay and network energy consumption.
AMP-Net Based Beamspace Channel Estimation in mmWave Massive MIMO Systems

Due to the high channel dimensionality caused by the large number of antennas, the beamspace channel estimation is challenging in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) uplink systems. To enhance the channel estimation accuracy, a novel beamspace channel estimation method is presented by employing the benefit from the compressed sensing (CS). Specifically, the problem of beamspace channel estimation is firstly transformed into an underdetermined optimization problem based on the sparse characteristic of the beamspace channel. Then the approximate message passing (AMP) based on neural network is presented, which is called AMP-Net. When the handcrafted optimal transform is orthogonal in the AMP algorithm, a balanced convolutional neural network (CNN) is designed to select the beamspace channel estimation results under the guidance of the AMP algorithm. Finally, the iterative process is expanded into a deep network form. In the network, controllable parameters are introduced to increase the flexibility of the beamspace channel estimation process. Simulation results demonstrate that the proposed algorithm has better the estimation accuracy than conventional CS based algorithms and other network models.
A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems

Reproducibility is a main principle in science and fundamental to ensure scientific progress. However, many recent works point out that there are widespread deficiencies for this aspect in the AI field, making the reproducibility of results impractical or even impossible. We therefore studied the state of reproducibility support on the topic of Reinforcement Learning & Recommender Systems to analyse the situation in this context. We collected a total of 60 papers and analysed them by defining a set of variables to inspect the most important aspects that enable reproducibility, such as dataset, pre-processing code, hardware specifications, software dependencies, algorithm implementation, algorithm hyperparameters, and experiment code. Furthermore, we used the ACM Badges definitions assigning them to the selected papers. We discovered that, like in many other AI domains, the Reinforcement Learning & Recommender Systems field is grappling with a reproducibility crisis, as none of the selected papers were reproducible when strictly applying the ACM Badges definitions according to our analysis.
Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint

In the field of hyperspectral anomaly detection, autoencoder (AE) have become a hot research topic due to their unsupervised characteristics and powerful feature extraction capability. However, autoencoders do not keep the spatial structure information of the original data well during the training process, and is affected by anomalies, resulting in poor detection performance. To address these problems, a hyperspectral anomaly detection method based on autoencoders with superpixel manifold constraints is proposed. Firstly, superpixel segmentation technique is used to obtain the superpixels of the hyperspectral image, and then the manifold learning method is used to learn the embedded manifold that based on the superpixels. Secondly, the learned manifold constraints are embedded in the autoencoder to learn the potential representation, which can maintain the consistency of the local spatial and geometric structure of the hyperspectral images (HSI). Finally, anomalies are detected by computing reconstruction errors of the autoencoder. Extensive experiments are conducted on three datasets, and the experimental results show that the proposed method has better detection performance than other hyperspectral anomaly detectors.
Inclusion of Rainwater Budget and Convective Downdrafts in the Arakawa-Schubert Cumulus Parameterization

Abstract A combined updraft–downdraft model that includes the mass, rainwater, and vertical momentum budget equations for both updrafts and downdrafts is incorporated into the Arakawa–Schubert (A–S) cumulus parameterization. The model assumes that the rainwater generated in an updraft falls partly inside and partly outside the updraft. Two types of stationary solutions are identified for the coupled rainwater budget and vertical momentum equations: solutions for small tilting angles and solutions for large tilting angles. The balance of terms in the rainwater budget equation is quite different between these solutions, and solutions for small tilting angles are unstable, while solutions for large tilting angles are stable. The cause of the instability is interpreted, considering the effect of rain drag on the vertical transport of rainwater. The motivation for an optimum choice of the tilting angle for use in the revised parameterization is discussed. The results of semiprognostic and single-column prognos.
Functional Characterization of Calmodulin-like Proteins, CML13 and CML14, as Novel Light Chains of Arabidopsis Class VIII Myosins

Myosins are important motor proteins that associate with the actin cytoskeleton. Structurally, myosins function as heteromeric complexes where smaller light chains, such as calmodulin (CaM), bind to isoleucine-glutamine (IQ) domains in the neck regions to facilitate mechano-enzymatic activity. We recently identified Arabidopsis CaM-like (CML) proteins, CML13 and CML14 as interactors of proteins containing multiple IQ domains, including a member of the myosin VIII class. Here, using in vivo and in vitro assays we demonstrate that CaM, CML13, and CML14 bind the neck region of all four Arabidopsis myosin VIII isoforms. Among ten CML isoforms tested for in planta binding to myosins VIIIs, CaM, CML13, and CML14 gave the strongest signals using in planta split-luciferase protein-interaction assays. In vitro, recombinant CaM, CML13, and CML14 showed specific, high-affinity, calcium-independent binding to the IQ domains of myosin VIIIs. Subcellular localization analysis indicated that CaM, CML13, and CML14 co-localized to plasma membrane-bound puncta when co-expressed with RFP-myosin fusion proteins containing IQ- and tail-domains of myosin VIIIs. In addition, in vitro actin-motility assays using recombinant myosin holoenzymes demonstrated that CaM, CML13, and CML14 function as light chains for myosin VIIIs. Collectively, our data indicate that Arabidopsis CML13 and CML14 are novel myosin VIII light chains. Highlight Myosins are key proteins in the plant cytoskeleton, but the identity of their light chain components is unknown. Here, we show that calmodulin-like proteins function as novel myosin light chains.