julilili42/papers huggingface dataset:
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
2022-02-21

A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors. We hypothesize that many of these hidden factors in the physical world are constant over time, changing only sparsely. To study this hypothesis, we propose Gated Regularized Dynamics (GateL0RD), a novel recurrent architecture that incorporates the inductive bias to maintain stable, sparsely changing latent states. The bias is implemented by means of a novel internal gating function and a penalty on the norm of latent state changes. We demonstrate that GateL0RD can compete with or outperform state-of-the-art RNNs in a variety of partially observable prediction and control tasks. GateL0RD tends to encode the underlying generative factors of the environment, ignores spurious temporal dependencies, and generalizes better, improving sampling efficiency and overall performance in model-based planning and reinforcement learning tasks. Moreover, we show that the developing latent states can be easily interpreted, which is a step towards better explainability in RNNs.
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
2022-02-21

Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation process. In this work, we take a step towards bridging this gap by studying the statistical performance of kernel-based clustering algorithms under non-parametric mixture models. We provide necessary and sufficient separability conditions under which these algorithms can consistently recover the underlying true clustering. Our analysis provides guarantees for kernel clustering approaches without structural assumptions on the form of the component distributions. Additionally, we establish a key equivalence between kernel-based data-clustering and kernel density-based clustering. This enables us to provide consistency guarantees for kernel-based estimators of non-parametric mixture models. Along with theoretical implications, this connection could have practical implications, including in the systematic choice of the bandwidth of the Gaussian kernel in the context of clustering.
Latent Event-Predictive Encodings through Counterfactual Regularization
2022-02-21

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making.
Physical conditions in the warped accretion disk of a massive star. 349 GHz ALMA observations of G023.01$-$00.41
2022-02-21

Young massive stars warm up the large amount of gas and dust which condenses in their vicinity, exciting a forest of lines from different molecular species. Their line brightness is a diagnostic tool of the gas physical conditions locally, which we use to set constraints on the environment where massive stars form. We made use of the Atacama Large Millimeter/submillimeter Array at frequencies near 349 GHz, with an angular resolution of , to observe the methyl cyanide (CH CN) emission which arises from the accretion disk of a young massive star. We sample the disk midplane with twelve distinct beams, where we get an independent measure of the gas (and dust) physical conditions. The accretion disk extends above the midplane showing a double-armed spiral morphology projected onto the plane of the sky, which we sample with ten additional beams: along these apparent spiral features, gas undergoes velocity gradients of about per 2000 au. The gas temperature (T) rises symmetrically along each side of the disk, from about 98 K at 3000 au to 289 K at 250 au, following a power law with radius, R . The CH CN column density (N) increases from to at the same radii, following a power law with radius, R . In the framework of a circular gaseous disk observed approximately edge-on, we infer an H volume density in excess of at a distance of 250 au from the star. We study the disk stability against fragmentation following the methodology by Kratter et al. (2010), appropriate under rapid accretion, and we show that the disk is marginally prone to fragmentation along its whole extent.
Separable Convolutions for Optimizing 3D Stereo Networks
2022-02-21

Deep learning based 3D stereo networks give superior performance compared to 2D networks and conventional stereo methods. However, this improvement in the performance comes at the cost of increased computational complexity, thus making these networks non-practical for the real-world applications. Specifically, these networks use 3D convolutions as a major work horse to refine and regress disparities. In this work first, we show that these 3D convolutions in stereo networks consume up to 94% of overall network operations and act as a major bottleneck. Next, we propose a set of "plug-&amp;-run" separable convolutions to reduce the number of parameters and operations. When integrated with the existing state of the art stereo networks, these convolutions lead up to 7x reduction in number of operations and up to 3.5x reduction in parameters without compromising their performance. In fact these convolutions lead to improvement in their performance in the majority of cases.
Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training
2022-02-21

A fundamental challenge in Deep Learning is to find optimal step sizes for stochastic gradient descent automatically. In traditional optimization, line searches are a commonly used method to determine step sizes. One problem in Deep Learning is that finding appropriate step sizes on the full-batch loss is unfeasibly expensive. Therefore, classical line search approaches, designed for losses without inherent noise, are usually not applicable. Recent empirical findings suggest, inter alia, that the full-batch loss behaves locally parabolically in the direction of noisy update step directions. Furthermore, the trend of the optimal update step size changes slowly. By exploiting these and more findings, this work introduces a line-search method that approximates the full-batch loss with a parabola estimated over several mini-batches. Learning rates are derived from such parabolas during training. In the experiments conducted, our approach is on par with SGD with Momentum tuned with a piece-wise constant learning rate schedule and often outperforms other line search approaches for Deep Learning across models, datasets, and batch sizes on validation and test accuracy. In addition, our approach is the first line search approach for Deep Learning that samples a larger batch size over multiple inferences to still work in low-batch scenarios.
Robustness via Uncertainty-aware Cycle Consistency
2022-02-21

Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty, leading to performance degradation when encountering unseen perturbations at test time. To address this, we propose a novel probabilistic method based on Uncertainty-aware Generalized Adaptive Cycle Consistency (UGAC), which models the per-pixel residual by generalized Gaussian distribution, capable of modelling heavy-tailed distributions. We compare our model with a wide variety of state-of-the-art methods on various challenging tasks including unpaired image translation of natural images, using standard datasets, spanning autonomous driving, maps, facades, and also in medical imaging domain consisting of MRI. Experimental results demonstrate that our method exhibits stronger robustness towards unseen perturbations in test data. Code is released here: https://github.com/ExplainableML/UncertaintyAwareCycleConsistency.
Comment on esurf-2021-78
2022-02-21

<strong class="journal-contentHeaderColor">Abstract.</strong> Frost cracking is a dominant mechanical weathering phenomenon facilitating the breakdown of bedrock in periglacial regions. Despite recent advances in understanding frost cracking processes, few studies have addressed how global climate change over the Late Cenozoic may have impacted spatial variations in frost cracking intensity. In this study, we estimate global changes in frost cracking intensity (FCI) by segregation ice growth. Existing process-based models of FCI are applied in combination with soil thickness data from the Harmonized World Soil Database. Temporal and spatial variations in FCI are predicted using surface temperatures changes obtained from ECHAM5 general circulation model simulations conducted for four different paleoclimate time-slices. Time-slices considered include Pre-Industrial (~1850 CE, PI), Mid-Holocene (~6 ka, MH), Last Glacial Maximum (~21 ka, LGM) and Pliocene (~3 Ma, PLIO) times. Results indicate for all paleoclimate time slices that frost cracking was most prevalent (relative to PI times) in the mid to high latitude regions, as well as high-elevation lower latitudes areas such the Himalayas, Tibet, European Alps, the Japanese Alps, the USA Rocky Mountains, and the Andes Mountains. The smallest deviations in frost cracking (relative to PI conditions) were observed in the MH simulation, which yielded slightly higher FCI values in most of the areas. In contrast, larger deviations were observed in the simulations of the colder climate (LGM) and warmer climate (PLIO). Our results indicate that the impact of climate change on frost cracking was most severe during the PI &ndash; LGM period due to higher differences in temperatures and glaciation at higher latitudes. In contrast, the PLIO results indicate low FCI in the Andes and higher values of FCI in Greenland and Canada due to the diminished extent of glaciation in the warmer PLIO climate.
Apparent nonlinear damping triggered by quantum fluctuations
2022-02-21

Nonlinear damping, the change in damping rate with the amplitude of oscillations plays an important role in many electrical, mechanical and even biological oscillators. In novel technologies such as carbon nanotubes, graphene membranes or superconducting resonators, the origin of nonlinear damping is sometimes unclear. This presents a problem, as the damping rate is a key figure of merit in the application of these systems to extremely precise sensors or quantum computers. Through measurements of a superconducting resonator, we show that from the interplay of quantum fluctuations and the nonlinearity of a Josephson junction emerges a power-dependence in the resonator response which closely resembles nonlinear damping. The phenomenon can be understood and visualized through the flow of quasi-probability in phase space where it reveals itself as dephasing. Crucially, the effect is not restricted to superconducting circuits: we expect that quantum fluctuations or other sources of noise give rise to apparent nonlinear damping in systems with a similar conservative nonlinearity, such as nano-mechanical oscillators or even macroscopic systems.
Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis
2022-02-22

The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.
Beyond the spontaneous scalarization: New fully nonlinear mechanism for the formation of scalarized black holes and its dynamical development
2022-02-22

In the present paper, we show the existence of a fully nonlinear mechanism for the formation of scalarized black holes, which is different from the spontaneous scalarization, and demonstrate its dynamical development. We consider a class of scalar-Gauss-Bonnet gravity theories within which no tachyonic instability can occur. Although the Schwarzschild black holes are linearly stable against scalar perturbations, we show dynamically that for certain choices of the coupling function, they are unstable against nonlinear scalar perturbations. This nonlinear instability leads to the formation of new black holes with scalar hair. The fully nonlinear and self-consistent study of the equilibrium black holes reveals that the spectrum of solutions is more complicated and more than one scalarized branch can exist. We have also considered classes of scalar-Gauss-Bonnet theories where both the standard and the nonlinear scalarization can be present, and they are smoothly connected that completes in an interesting way the picture of black hole scalarization. The fully nonlinear (de)scalarization of a Schwarzschild black hole will always happen with a jump because the stable ``scalarized branch'' is not continuously connected to the Schwarzschild one that can leave clear observational signatures.
The impact of anthracyclines in intermediate and high-risk HER2-negative early breast cancer—a pooled analysis of the randomised clinical trials PlanB and SUCCESS C
2022-02-22

Abstract Background Anthracycline/cyclophosphamide-taxane-containing chemotherapy (AC-T) is the standard of care in the adjuvant treatment of HER2-negative early breast cancer (EBC), but recent studies suggest omission of anthracyclines for reduced toxicity without compromising efficacy. Methods Based on individual patient data ( n = 5924) pooled from the randomised Phase III trials PlanB and SUCCESS C, we compared disease-free survival (DFS) and overall survival (OS) between intermediate to high-risk HER2-negative EBC-patients treated with either six cycles of docetaxel/cyclophosphamide (TC6) or an AC-T regime using univariable and adjusted multivariable Cox regression models. Results AC-T conferred no significant DFS or OS advantage in univariable (DFS: hazard ratio (HR) for TC vs. AT 1.05, 95% confidence interval (CI): 0.89–1.24, P = 0.57; OS: HR 1.00, 95% CI: 0.80–1.26, P = 1.00) and adjusted multivariable analysis (DFS: HR 1.01, 95% CI: 0.86–1.19, P = 0.91; OS: HR 0.97, 95% CI: 0.77–1.22, P = 0.79). Patients receiving TC6 had significantly fewer grade 3–4 adverse events. Exploratory subgroup analysis showed that AC-T was associated with significantly better DFS and OS in pN2/3 patients, specifically in those with lobular histology. Conclusion For most patients with HER2-negative EBC, AC-T is not associated with a survival benefit compared to TC6. However, patients with lobular pN2/pN3 tumours seem to benefit from anthracycline-containing chemotherapy.
Slowly-rotating curved acoustic black holes: Quasinormal modes, Hawking-Unruh radiation, and quasibound states
2022-02-22

Astrophysical black holes are generally surrounded by accretion disks, galactic matter and the omnipresent cosmic microwave background radiation, thus allowing for the concurrent propagation of both gravitational and sound waves. Recently, acoustic black holes were embedded in Schwarzschild spacetime allowing for the coexistence of event and acoustic horizons. Here, we obtain a class of perturbative solutions to the field equations of the relativistic Gross-Pitaevskii and Yang-Mills theories, which describe sound waves propagating on a curved slowly-rotating acoustic black hole, akin to Lense-Thirring spacetime. We investigate the quasinormal mode frequencies, Hawking-Unruh radiation, and quasibound states. Our novel metric mimics the gravitational field of astrophysical compact objects in the limiting case of slow rotation, and therefore could, in principle, shed more light into the underlying classical and quantum physics of black holes through analog acoustic probes.
Potentials of Digitally Guided Excursions at Universities Illustrated Using the Example of an Urban Geography Excursion in Stuttgart
2022-02-22

Abstract The main focus of this article is on digital excursion formats at universities and their advantages with and without pandemic-related restrictions on teaching. The development of the application, its didactics and feedback from students are demonstrated using the example of the urban geography excursion developed as part of the 'InExkurs' project at the University of Tübingen. Compared to traditional overview excursions, digitally guided formats allow students more flexibility, autonomy and independence in the implementation. Aspects of gamification and location-based games can be used to increase motivation and, in combination with individual navigation, create associations with scavenger hunts. The use of extended reality on mobile devices enables a diverse application of digital information on site: the use of historical maps and photos as well as the integration of (fictional) characters is exemplified using the developed digital urban geography excursion. The first feedback on the excursion showed that the majority of participating students appreciated the digital format and would like to participate in excursions in this or similar formats more often.
Informational steady states and conditional entropy production in continuously monitored systems: The case of Gaussian systems
2022-02-22

The act of measuring a system has profound consequences of dynamical and\\nthermodynamic nature. In particular, the degree of irreversibility ensuing from\\na non-equilibrium process is strongly affected by measurements aimed at\\nacquiring information on the state of a system of interest: the conditional and\\nunconditional entropy production, which quantify the degree of irreversibility\\nof the open system's dynamics, are related to each other by clearly interpreted\\ninformational quantities. Building on a recently proposed collisional-model\\nframework [G. T. Landi {\\\\it et al.}, arXiv:2103.06247], we investigate the case\\nof continuous-variable information carriers prepared in Gaussian states and\\nundergoing Gaussian processes. We build up a toolbox that fully characterizes\\nthe thermodynamics of continuously measured non-equilibrium Gaussian systems\\nand processes, illustrating how the instruments hereby introduced provide key\\ninsight into recent experiments on mesoscopic quantum systems [Phys. Rev. Lett,\\n{\\\\bf 125}, 080601 (2020)].\\n
UREMP, RO-REMP, and OO-REMP: Hybrid perturbation theories for open-shell electronic structure calculations
2022-02-22

An accurate description of the electron correlation energy in closed- and open-shell molecules is shown to be obtained by a second-order perturbation theory (PT) termed REMP. REMP is a hybrid of the Retaining the Excitation degree (RE) and the Møller–Plesset (MP) PTs. It performs particularly encouragingly in an orbital-optimized variant (OO-REMP) where the reference wavefunction is given by an unrestricted Slater determinant whose spin orbitals are varied such that the total energy becomes a minimum. While the approach generally behaves less satisfactorily with unrestricted Hartree–Fock references, reasonable performance is observed for restricted Hartree–Fock and restricted open-shell Hartree–Fock references. Inclusion of single excitations to OO-REMP is investigated and found—as in similar investigations—to be dissatisfying as it deteriorates performance. For the non-multireference subset of the accurate W4-11 benchmark set of Karton et al. [Chem. Phys. Lett. 510, 165–178 (2011)], OO-REMP predicts most atomization and reaction energies with chemical accuracy (1 kcal mol−1) if complete-basis-set extrapolation with augmented and core-polarized basis sets is used. For the W4-11 related test-sets, the error estimates obtained with the OO-REMP method approach those of coupled-cluster with singles, doubles and perturbative triples [CCSD(T)] within 20%–35%. The best performance of OO-REMP is found for a mixing ratio of 20%:80% MP:RE, which is essentially independent of whether radical stabilization energies, barrier heights, or reaction energies are investigated. Orbital optimization is shown to improve the REMP approach for both closed and open shell cases and outperforms coupled-cluster theory with singles and doubles (CCSD), spin-component scaled Møller-Plesset theory at second order (SCS-MP2), and density functionals, including double hybrids in all the cases considered.
The missing link in gravitational-wave astronomy: A summary of discoveries waiting in the decihertz range
2022-02-22

Since 2015 the gravitational-wave observations of LIGO and Virgo have transformed our understanding of compact-object binaries. In the years to come, ground-based gravitational-wave observatories such as LIGO, Virgo, and their successors will increase in sensitivity, discovering thousands of stellar-mass binaries. In the 2030s, the space-based LISA will provide gravitational-wave observations of massive black holes binaries. Between the - band of ground-based observatories and the - band of LISA lies the uncharted decihertz gravitational-wave band. We propose a Decihertz Observatory to study this frequency range, and to complement observations made by other detectors. Decihertz observatories are well suited to observation of intermediate-mass ( - ) black holes; they will be able to detect stellar-mass binaries days to years before they merge, providing early warning of nearby binary neutron star mergers and measurements of the eccentricity of binary black holes, and they will enable new tests of general relativity and the Standard Model of particle physics. Here we summarise how a Decihertz Observatory could provide unique insights into how black holes form and evolve across cosmic time, improve prospects for both multimessenger astronomy and multiband gravitational-wave astronomy, and enable new probes of gravity, particle physics and cosmology.