Miracle-dz/UniFAR-FTUs huggingface dataset:
Gluon fragmentation into ${^{3}\hspace{-0.6mm}P_{J}^{[1,8]}}$ quark pair and test of NRQCD factorization at two-loop level

The next-to-leading order (NLO) ($\mathcal{O}(\alpha_s^3)$) corrections for gluon fragmentation functions to a heavy quark-antiquark pair in ${^{3}\hspace{-0.6mm}P_{J}^{[1,8]}}$ states are calculated within the NRQCD factorization. We use integration-by-parts reduction and differential equations to semi-analytically calculate fragmentation functions in full-QCD, and find that infrared divergences can be absorbed by the NRQCD long distance matrix elements. Thus, the NRQCD factorization conjecture is verified at two-loop level via a physical process, which is free of artificial ultraviolet divergences. Through matching procedure, infrared-safe short distance coefficients and $\mathcal{O}(\alpha_s^2)$ perturbative NRQCD matrix elements $\langle{\mathcal O}^{{^{3}\hspace{-0.6mm}P_{J}^{[1,8]}}}({^{3}\hspace{-0.6mm}S_{1}^{[8]}})\rangle_{\mathrm{NLO}}$ are obtained simultaneously. The NLO short distance coefficients are found to have significant corrections comparing with the LO ones.
Estimating expectation values using approximate quantum states

We introduce an approximate description of an $N$-qubit state, which contains sufficient information to estimate the expectation value of any observable to a precision that is upper bounded by the ratio of a suitably-defined seminorm of the observable to the square root of the number of the system's identical preparations $M$, with no explicit dependence on $N$. We describe an operational procedure for constructing the approximate description of the state that requires, besides the quantum state preparation, only single-qubit rotations followed by single-qubit measurements. We show that following this procedure, the cardinality of the resulting description of the state grows as $3MN$. We test the proposed method on Rigetti's quantum processor unit with 12, 16 and 25 qubits for random states and random observables, and find an excellent agreement with the theory, despite experimental errors.
GANterpretations

Since the introduction of Generative Adversarial Networks (GANs) [Goodfellow et al., 2014] there has been a regular stream of both technical advances (e.g., Arjovsky et al. [2017]) and creative uses of these generative models (e.g., [Karras et al., 2019, Zhu et al., 2017, Jin et al., 2017]). In this work we propose an approach for using the power of GANs to automatically generate videos to accompany audio recordings by aligning to spectral properties of the recording. This allows musicians to explore new forms of multi-modal creative expression, where musical performance can induce an AI-generated musical video that is guided by said performance, as well as a medium for creating a visual narrative to follow a storyline (similar to what was proposed by Frosst and Kereliuk [2019]).
EstBERT: A Pretrained Language-Specific BERT for Estonian

This paper presents EstBERT, a large pretrained transformer-based language-specific BERT model for Estonian. Recent work has evaluated multilingual BERT models on Estonian tasks and found them to outperform the baselines. Still, based on existing studies on other languages, a language-specific BERT model is expected to improve over the multilingual ones. We first describe the EstBERT pretraining process and then present the models’ results based on the finetuned EstBERT for multiple NLP tasks, including POS and morphological tagging, dependency parsing, named entity recognition and text classification. The evaluation results show that the models based on EstBERT outperform multilingual BERT models on five tasks out of seven, providing further evidence towards a view that training language-specific BERT models are still useful, even when multilingual models are available.
Taylor dispersion of elongated rods

Particles transported in fluid flows, such as cells, polymers, or nanorods, are rarely spherical. In this study, we numerically and theoretically investigate the dispersion of an initially localized patch of passive elongated Brownian particles constrained to one degree of rotational freedom in a two-dimensional Poiseuille flow, demonstrating that elongated particles exhibit an enhanced longitudinal dispersion. In a shear flow, the rods translate due to advection and diffusion and rotate due to rotational diffusion and their classical Jeffery's orbit. The magnitude of the enhanced dispersion depends on the particle's aspect ratio and the relative importance of its shear-induced rotational advection and rotational diffusivity. When rotational diffusion dominates, we recover the classical Taylor dispersion result for the longitudinal spreading rate using an orientationally averaged translational diffusivity for the rods. However, in the high-shear limit, the rods tend to align with the flow and ultimately disperse more due to their anisotropic diffusivities. Results from our Monte Carlo simulations of the particle dispersion are captured remarkably well by a simple theory inspired by Taylor's original work. For long times and large Peclet numbers, an effective one-dimensional transport equation is derived with integral expressions for the particles' longitudinal transport speed and dispersion coefficient. The enhanced dispersion coefficient can be collapsed along a single curve for particles of high aspect ratio, representing a simple correction factor that extends Taylor's original prediction to elongated particles.
Relation-weighted Link Prediction for Disease Gene Identification

Identification of disease genes, which are a set of genes associated with a disease, plays an important role in understanding and curing diseases. In this paper, we present a biomedical knowledge graph designed specifically for this problem, propose a novel machine learning method that identifies disease genes on such graphs by leveraging recent advances in network biology and graph representation learning, study the effects of various relation types on prediction performance, and empirically demonstrate that our algorithms outperform its closest state-of-the-art competitor in disease gene identification by 24.1%. We also show that we achieve higher precision than Open Targets, the leading initiative for target identification, with respect to predicting drug targets in clinical trials for Parkinson's disease.
Optical and spin manipulation of non-Kramers rare-earth ions in a weak magnetic field for quantum memory applications

Rare-earth ion doped crystals have proven to be solid platforms for implementing quantum memories. Their potential use for integrated photonics with large multiplexing capability and unprecedented coherence times is at the core of their attractiveness. The best performances of these ions are, however, usually obtained when subjected to a DC magnetic field, but consequences of such fields on the quantum memory protocols have only received little attention. In this paper, we focus on the effect of a DC bias magnetic field on the population manipulation of non-Kramers ions with nuclear quadrupole states, both in the spin and optical domains, by developing a simple theoretical model. We apply this model to explain experimental observations in a $^{151}\mathrm{Eu}:{\mathrm{Y}}_{2}{\mathrm{SiO}}_{5}$ crystal, and highlight specific consequences on the atomic frequency comb spin-wave protocol. The developed analysis should allows predicting optimal magnetic field configurations for various protocols.
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current heuristic-based algorithms such as the slot-based method rely heavily on hand-engineered parameters and are restricted to specific scenarios. Supervised learning methods such as Imitation Learning lack generalization and safety guarantees. To address these problems and to ensure a robust framework, we propose a Robust-Hierarchical Reinforcement Learning (HRL) framework for learning autonomous driving policies. We adapt a state-of-the-art algorithm, Hierarchical Double Deep Q-learning (h-DDQN), and make the framework robust by (1) constituting the decision of selecting driving maneuver as a high-level option; (2) for the lower-level controller, outputting waypoint trajectories to track with a Proportional-Integral-Derivative (PID) controller instead of direct acceleration/steering actions; and (3) using a Long-Short-Term-Memory (LSTM) layer in the network to alleviate the effects of observation noise and dynamic driving behaviors. Moreover, to improve the sample efficiency, we use Hybrid Reward Mechanism and Reward-Driven Exploration. Results from the high-fidelity CARLA simulator while simulating different interactive lane change scenarios indicate that the proposed framework reduces convergence time, generates smoother trajectories, and can better handle dynamic surroundings and noisy observations as compared to other traditional RL approaches.
Probabilistic Indistinguishability and the Quality of Validity in Byzantine Agreement

This paper provides a formal framework for reasoning about randomized distributed algorithms. We generalize the notion of indistinguishability, the most useful tool in deterministic lower bounds, to apply to a probabilistic setting. We use the new notion to prove a lower bound on the probability at which it can be guaranteed that honest parties will not decide on a possibly bogus value. Moreover, we show that the bound is tight by providing a protocol that matches the bound. This completely characterizes the quality of decisions that protocols for a randomized multi-valued Consensus problem can guarantee in an asynchronous environment with Byzantine faults. Proving lower bounds for probabilistic protocols can be rather subtle. Probabilistic protocols have been around for at least four decades and are receiving a lot of attention with the emergence of blockchain systems. Nonetheless, we are aware of only a handful of randomized lower bounds. The formal tool that we develop in order to carry out our lower bound proof is of independent interest. It provides a rigorous yet accessible means to prove lower bounds for probabilistic protocols in general.
Dynamic Relay Selection and Power Allocation for Minimizing Outage Probability: A Hierarchical Reinforcement Learning Approach

Cooperative communication is an effective approach to improve spectrum utilization. When considering relay selection and power allocation in cooperative communication, most of the existing studies require the assumption of channel state information (CSI). However, it is difficult to get an accurate CSI in practice. In this paper, we consider an outage-based method subjected to a total transmission power constraint in the two-hop cooperative communication scenario. We use reinforcement learning (RL) methods to learn strategies, and complete the optimal relay selection and power allocation, which do not need any prior knowledge of CSI but simply rely on the interaction with the communication environment. It is noted that conventional RL methods, including common deep reinforcement learning (DRL) methods, perform poorly when the search space is large. Therefore, we first propose a practical DRL framework with an outage-based reward function, which is used as a baseline. Then, we further propose our novel hierarchical reinforcement learning (HRL) algorithm for dynamic relay selection and power allocation. A key difference from other RL-based methods in existing literatures is that, our HRL approach decomposes relay selection and power allocation into two hierarchical optimization objectives, which are trained in different levels. Simulation results reveal that our HRL algorithm trains faster and obtains a lower outage probability when compared with traditional DRL methods, especially in a sparse reward environment.
Testing and dating structural changes in copula-based dependence measures

This paper is concerned with testing and dating structural breaks in the dependence structure of multivariate time series. We consider a cumulative sum (CUSUM) type test for constant copula-based dependence measures, such as Spearman's rank correlation and quantile dependencies. The asymptotic null distribution is not known in closed form and critical values are estimated by an i.i. d. bootstrap procedure. We analyze size and power properties in a simulation study under different dependence measure settings, such as skewed and fat-tailed distributions. To date breakpoints and to decide whether two estimated break locations belong to the same break event, we propose a pivot confidence interval procedure. Finally, we apply the test to the historical data of 10 large financial firms during the last financial crisis from 2002 to mid-2013.
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?

A learning algorithm is private if the produced model does not reveal (too much) about its training set. InstaHide [Huang, Song, Li, Arora, ICML'20] is a recent proposal that claims to preserve privacy by an encoding mechanism that modifies the inputs before being processed by the normal learner. We present a reconstruction attack on InstaHide that is able to use the encoded images to recover visually recognizable versions of the original images. Our attack is effective and efficient, and empirically breaks InstaHide on CIFAR-10, CIFAR-100, and the recently released InstaHide Challenge. We further formalize various privacy notions of learning through instance encoding and investigate the possibility of achieving these notions. We prove barriers against achieving (indistinguishability based notions of) privacy through any learning protocol that uses instance encoding.
Criticality in the Mechanical Regulation of Cell Adhesion

We illuminate the many-body effects underlying the structure, formation, and dissolution of cellular adhesion domains in the presence of forces. We consider Glauber dynamics of a two-dimensional model of nearest-neighbor interacting adhesion bonds with intrinsic binding-affinity under the action of a shared pulling or pushing force. Highly accurate analytical results are obtained on the pair-correlation level of the Bethe-Guggenheim approximation for the complete thermodynamics and kinetics of adhesion clusters of any size, including the thermodynamic limit. A new kind of dynamical phase transition is uncovered -- the mean formation and dissolution times per adhesion bond change discontinuously with respect to the bond-coupling parameter. At the respective critical points cluster formation and dissolution are fastest, while the statistically dominant transition path undergoes a qualitative change -- the entropic barrier to complete binding/unbinding is rate-limiting below, and the phase transition between dense and dilute phases above the dynamical critical point. Our results may have far reaching consequences for the mechanical regulation of cell adhesion and its dysregulation in the metastatic transition of cancer cells, as well as T cell mediated immune response. The response of these processes to changes in membrane stiffness or applied forces is shown to be largest at criticality.
ALMA CO Observations of the Gamma-Ray Supernova Remnant RX J1713.7–3946: Discovery of Shocked Molecular Cloudlets and Filaments at 0.01 pc Scales

RX J1713.7−3946 is a unique core-collapse supernova remnant (SNR) that emits bright TeV gamma-rays and synchrotron X-rays caused by cosmic rays, in addition to interactions with interstellar gas clouds. We report here on results of Atacama Large Millimeter/submillimeter Array 12CO(J = 1–0) observations toward the northwestern shell of the SNR. We newly found three molecular complexes consisting of dozens of shocked molecular cloudlets and filaments with typical radii of ∼0.03–0.05 pc and densities of ∼104 cm−3. These cloudlets and filaments are located not only along synchrotron X-ray filaments, but also in the vicinity of X-ray hotspots with month- or year-scale time variations. We argue that X-ray hotspots and filaments were generated by shock–cloudlet interactions through magnetic-field amplification up to mG. The interstellar medium density contrast of ∼105, coexistence of molecular cloudlets and low-density diffuse medium of ∼0.1 cm−3, is consistent with such a magnetic field amplification as well as a wind-bubble scenario. The small-scale cloud structures also affect hadronic gamma-ray spectra considering the magnetic field amplification on surface and inside clouds.
Label-free Raman spectroscopy and machine learning enables sensitive evaluation of differential response to immunotherapy

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, particularly due to a lack of reliable biomarkers for accurate prediction of treatment outcomes and evaluation of response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biochemical changes induced by immunotherapy in the tumor microenvironment. We used CT26 murine colorectal cancer cells to grow tumor xenografts and subjected them to treatment with anti-CTLA-4 and anti-PD-L1 antibodies. Multivariate curve resolution - alternating least squares (MCR-ALS) decomposition of Raman spectral dataset obtained from the treated and control tumors revealed subtle differences in lipid, nucleic acid, and collagen content due to therapy. Our supervised classification analysis using support vector machines and random forests provided excellent prediction accuracies for both immune checkpoint inhibitors and delineated important spectral markers specific to each therapy, consistent with their differential mechanisms of action. Our findings pave the way for in vivo studies of response to immunotherapy in clinical patients using label-free Raman spectroscopy and machine learning.
When is approximate counting for conjunctive queries tractable?

Conjunctive queries are one of the most common class of queries used in database systems, and the best studied in the literature. A seminal result of Grohe, Schwentick, and Segoufin (STOC 2001) demonstrates that for every class G of graphs, the evaluation of all conjunctive queries whose underlying graph is in G is tractable if, and only if, G has bounded treewidth. In this work, we extend this characterization to the counting problem for conjunctive queries. Specifically, for every class C of conjunctive queries with bounded treewidth, we introduce the first fully polynomial-time randomized approximation scheme (FPRAS) for counting answers to a query in C, and the first polynomial-time algorithm for sampling answers uniformly from a query in C. As a corollary, it follows that for every class G of graphs, the counting problem for conjunctive queries whose underlying graph is in G admits an FPRAS if, and only if, G has bounded treewidth (unless BPP is different from P). In fact, our FPRAS is more general, and also applies to conjunctive queries with bounded hypertree width, as well as unions of such queries. The key ingredient in our proof is the resolution of a fundamental counting problem from automata theory. Specifically, we demonstrate the first FPRAS and polynomial time sampler for the set of trees of size n accepted by a tree automaton, which improves the prior quasi-polynomial time randomized approximation scheme (QPRAS) and sampling algorithm of Gore, Jerrum, Kannan, Sweedyk, and Mahaney ’97. We demonstrate how this algorithm can be used to obtain an FPRAS for many open problems, such as counting solutions to constraint satisfaction problems (CSP) with bounded hypertree width, counting the number of error threads in programs with nested call subroutines, and counting valid assignments to structured DNNF circuits.
Gyejigachulbutang (Gui-Zhi-Jia-Shu-Fu-Tang, Keishikajutsubuto, TJ-18) in Degenerative Knee Osteoarthritis Patients: Lessons and Responders from a Multicenter Randomized Placebo-Controlled Double-Blind Clinical Trial

Background Gyejigachulbutang (GUI-ZHI-JIA-SHU-FU-TANG, GCB) is an herbal formula widely prescribed in traditional East Asian medicine practice for arthritis and muscle pain. We evaluated the efficacy and safety of GCB for degenerative knee osteoarthritis (KOA). Methods Eighty patients with KOA were randomly assigned to the GCB group or the placebo group in a 1 : 1 ratio in two Korean medicine hospitals. Patients took GCB or placebo three times a day for 4 weeks. Primary outcome was the change in the visual analogue scale (VAS) score for knee pain from baseline to 4th week. Secondary outcomes were the change in the VAS score from baseline to 2nd week and 8th week, Korean Western Ontario and McMaster Universities Osteoarthritis Index (K-WOMAC), European Quality of Life Five Dimensions questionnaire (EQ-5D), and safety. Results There was no significant difference between the compared indicators of the GCB and placebo groups. However, in subgroup analysis, GCB was effective for subjects with a BMI lower than 25 kg/m2. The dose of pain medication was significantly lower in the GCB group than in the placebo group after four weeks (p=0.016). There were no serious adverse events in the GCB group. Conclusions GCB was not effective in primary outcome analysis. In exploratory subgroup analysis, GCB might be effective for individuals with BMI lower than 25 kg/m2 for the treatment of degenerative KOA. GCB may also help reduce the consumption of pain medication. Furthermore, research is required for our hypothesis. This trial is registered with KCT0003024.
Hypermanganesemia Induced Chorea and Cognitive Decline in a Tea Seller

Background: Manganese associated neurotoxicity and neurodegeneration is quite rare yet established neurological disorder. This neurotoxic element has predilection for depositing in basal ganglia structures, manifesting mainly as parkinsonian and dystonic movement disorders with behavioral abnormalities. Case report: We report a 40-year-old man who presented with a subacute onset bilateral, asymmetric hyperkinetic movement disorder (predominantly left sided chorea) with multi-domain cognitive impairment, dysarthria, and generalized rigidity. Clinical history and examination yielded multiple differential diagnoses including deposition and metabolic disorders, autoimmune and paraneoplastic encephalitis involving basal ganglia, and neurodegenerative disorders with chorea and cognitive impairment. However, magnetic resonance imaging was suggestive of paramagnetic substance deposition, which came out to be manganese after laboratory investigations. History, clinical examinations, and investigation results pointed towards a diagnosis of acquired hypermanganesemia due to over-ingestion of manganese containing substance (i.e., black tea). He was treated symptomatically and with chelation therapy (calcium disodium edetate). At the sixth month of follow-up, complete resolution of chorea, dysarthria and partial amelioration of rigidity were observed. His cognitive decline and behavioral abnormalities improved. Discussion: This is probably the first reported case of acquired hypermanganesemia that presented as a combination of asymmetric chorea and cognitive dysfunction with atypical imaging characteristics. The clinical picture mimicked that of Huntington’s disease. We highlight the potential deleterious effects of an apparently “benign” non-alcoholic beverage (i.e., black tea) on cerebral metabolism.