nils-herrmann/clean_scirepeval huggingface dataset:
LIP: Local Importance-Based Pooling

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for discriminative tasks, there is a possibility that these layers lose the discriminative details due to improper pooling strategies, which could hinder the learning process and eventually result in suboptimal models. In this paper, we present a unified framework over the existing downsampling layers (e.g., average pooling, max pooling, and strided convolution) from a local importance view. In this framework, we analyze the issues of these widely-used pooling layers and figure out the criteria for designing an effective downsampling layer. According to this analysis, we propose a conceptually simple, general, and effective pooling layer based on local importance modeling, termed as Local Importance-based Pooling (LIP). LIP can automatically enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. Experiment results show that LIP consistently yields notable gains with different depths and different architectures on ImageNet classification. In the challenging MS COCO dataset, detectors with our LIP-ResNets as backbones obtain a consistent improvement (>=1.4%) over the vanilla ResNets, and especially achieve the current state-of-the-art performance in detecting small objects under the single-scale testing scheme.
On Identifiability in Transformers

In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the identifiability of attention weights and token embeddings, and the aggregation of context into hidden tokens. We show that, for sequences longer than the attention head dimension, attention weights are not identifiable. We propose effective attention as a complementary tool for improving explanatory interpretations based on attention. Furthermore, we show that input tokens retain to a large degree their identity across the model. We also find evidence suggesting that identity information is mainly encoded in the angle of the embeddings and gradually decreases with depth. Finally, we demonstrate strong mixing of input information in the generation of contextual embeddings by means of a novel quantification method based on gradient attribution. Overall, we show that self-attention distributions are not directly interpretable and present tools to better understand and further investigate Transformer models.
Analyzing the structure of basic quantum knowledge for instruction

In order to support students in the development of expertise in quantum mechanics, we asked which concepts and structures can act as organizing principles of the non-relativistic theory. The research question has been addressed in a multi-step process based on the analysis of categorization studies, on a content analysis of a sample of undergraduate textbooks and on the results of existing research on learning difficulties. The answer consists in seven concept maps, intended as models of the organizing principles of quantum knowledge needed to account for the results of measurement and time evolution. By means of these instruments, it is possible to visualize and explore the different facets of the interplay between the vector structure of the quantum states and the operator structure of the observables, and to highlight the educational significance of the relations between observables. The maps can be used by instructors as a support for helping students build a well-organized knowledge structure and by researchers as a basis for the design of investigations into student understanding. While this framework may be adapted to different approaches and interpretive stances, it provides indications in favor of a spin-first approach over a waves-first one. At high school level, a simplified version of the framework has been used as a basis for the design of a teaching-learning sequence.
Sampling distributions of optimal portfolio weights and characteristics in small and large dimensions

Optimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor want to realise the position suggested by the optimal portfolios he/she needs to estimate the unknown parameters and to account the parameter uncertainty into the decision process. Most often, the parameters of interest are the population mean vector and the population covariance matrix of the asset return distribution. In this paper we characterise the exact sampling distribution of the estimated optimal portfolio weights and their characteristics by deriving their sampling distribution which is present in terms of a stochastic representation. This approach possesses several advantages, like (i) it determines the sampling distribution of the estimated optimal portfolio weights by expressions which could be used to draw samples from this distribution efficiently; (ii) the application of the derived stochastic representation provides an easy way to obtain the asymptotic approximation of the sampling distribution. The later property is used to show that the high-dimensional asymptotic distribution of optimal portfolio weights is a multivariate normal and to determine its parameters. Moreover, a consistent estimator of optimal portfolio weights and their characteristics is derived under the high-dimensional settings. Via an extensive simulation study, we investigate the finite-sample performance of the derived asymptotic approximation and study its robustness to the violation of the model assumptions used in the derivation of the theoretical results.
Free surface of a liquid in a rotating frame with time-depend velocity

The shape of liquid surface in a rotating frame depends on the angular velocity. In this experiment, a fluid in a rectangular container with a small width is placed on a rotating table. A smartphone fixed to the rotating frame simultaneously records the fluid surface with the camera and also, thanks to the built-in gyroscope, the angular velocity. When the table starts rotating the surface evolves and develops a parabolic shape. Using video analysis we obtain the surface's shape: concavity of the parabole and height of the vertex. Experimental results are compared with theoretical predictions. This problem contributes to improve the understanding of relevant concepts in fluid dynamics.
Recursion, Probability, Convolution and Classification for Computations

The main motivation of this work was practical, to offer computationally and theoretical scalable ways to structuring large classes of computation. It started from attempts to optimize R code for machine learning/artificial intelligence algorithms for huge data sets, that due to their size, should be handled into an incremental (online) fashion. 
Our target are large classes of relational (attribute based), mathematical (index based) or graph computations. We wanted to use powerful computation representations that emerged in AI (artificial intelligence)/ML (machine learning) as BN (Bayesian networks) and CNN (convolution neural networks). For the classes of computation addressed by us, and for our HPC (high performance computing) needs, the current solutions for translating computations into such representation need to be extended. 
Our results show that the classes of computation targeted by us, could be tree-structured, and a probability distribution (defining a DBN, i.e. Dynamic Bayesian Network) associated with it. More ever, this DBN may be viewed as a recursive CNN (Convolution Neural Network). Within this tree-like structure, classification in classes with size bounded (by a parameterizable may be performed. 
These results are at the core of very powerful, yet highly practically algorithms for restructuring and parallelizing the computations. The mathematical background required for an in depth presentation and exposing the full generality of our approach) is the subject of a subsequent paper. In this paper, we work in an limited (but important) framework that could be understood with rudiments of linear algebra and graph theory. The focus is in applicability, most of this paper discuss the usefulness of our approach for solving hard compilation problems related to automatic parallelism.
Enabling simulation of high‐dimensional micro‐macro biophysical models through hybrid CPU and multi‐GPU parallelism

Micro‐macro models provide a powerful tool to study the relationship between microscale mechanisms and emergent macroscopic behavior. However, the detailed microscopic modeling requires tracking and evolving a potentially high‐dimensional configuration space at high computational cost. In this work, we present a novel parallel algorithm for simulating a high‐dimensional micro‐macro model of a gliding motility assay. We utilize a holistic approach aligning the data residency and simulation scales with the hybrid CPU and multi‐GPU hardware. Our novel approach achieves a speedup factor of 9.25× over previous GPU‐accelerated micro‐macro methods on the same hardware. Furthermore, by decoupling dependencies in the microstructure update, we are able to efficiently distribute the microstructure over multiple GPUs with minimal overhead. We test on up to four GPUs and observe excellent scaling, suggesting that significant further speedups are achievable with additional GPUs. Our approach enables micro‐macro simulations of higher complexity and resolution than would otherwise be feasible.
Interacting dark energy after the latest Planck, DES, and $H_0$ measurements: an excellent solution to the $H_0$ and cosmic shear tensions.

We examine the most well-studied model featuring non-gravitational interactions between dark matter and dark energy in light of the latest cosmological observations. Our data includes Cosmic Microwave Background (CMB) measurements from the Planck 2018 legacy data release, galaxy clustering and cosmic shear measurements from the Dark Energy Survey Year 1 results, and the 2019 local distance ladder measurement of the Hubble constant $H_0$ from the Hubble Space Telescope. We find that the presence of interactions among the two dark sectors can bring the significance level of the long-standing $H_0$ tension below the $1\sigma$ level. The very same model also significantly reduces the $\Omega_{\rm m}-\sigma_8$ tension between CMB and cosmic shear measurements. Interactions between the dark components of our Universe remain therefore as an extremely promising solution to these persisting cosmological tensions. The results presented in this paper are among the first constraints on exotic physics from the Planck 2018 legacy dataset. In a companion paper, we will further investigate these tensions when allowing for more freedom in the dark energy sector.
The far side of the cube

Game-semantic models usually start from the core model of the prototypical language PCF, which is characterised by a range of combinatorial constraints on the shape of plays. Relaxing each such constraint usually corresponds to the introduction of a new language operation, a feature of game semantics commonly known as the `Abramsky Cube'. In this presentation we relax all such combinatorial constraints, resulting in the most general game model, in which all the other game models live. This is perhaps the simplest set up in which to understand game semantics, so it should serve as a portal to the other, more complex, game models in the literature. It might also be interesting in its own right, as an extremal instance of the game-semantic paradigm.
Deep Dexterous Grasping of Novel Objects from a Single View

In this thesis, a novel generative-evaluative method was proposed to solve the problem of dexterous grasping of the novel object with a single view. The generative model is learned from human demonstration. The grasps generated by the generative model are used to train the evaluative model. Two novel evaluative network architectures are proposed. The evaluative model is a deep evaluative network that is trained in the simulation. The generative-evaluative method is tested in a real grasp data set with 49 previously unseen challenging objects. The generative-evaluative method achieves a success rate of 78% that outperforms the purely generative method, that has a success rate of 57%. The thesis provides insights into the strengths and weaknesses of the generative-evaluative method by comparing different deep network architectures.
Quantum break in models of axion dark matter

A system of light axions comprising a classical axion field, one candidate for dark matter, has an instability that can rapidly mix in photon pairs in a coherent fashion if initiated by a quantum break (which eliminates the need for seeds.) Adding more photon states, such as a multiplicity of angles for the case of the axion field at rest, reduces the argument of a logarithmic factor in the mixing time by orders of magnitude. Admitting multiple photon states, all within a window of instability, leads to a synchronization effect that appears to nullify conventional red-shift limitations. Even the fully developed states of the electromagnetic field produced are highly non-classical; they can be looked on as quantum superpositions of different nearly-classical macroscopic systems.
How Galactic Environment Affects the Dynamical State of Molecular Clouds and Their Star Formation Efficiency

We investigate how the dynamical state of molecular clouds relates to host galaxy environment and how this impacts the star formation efficiency (SFE) in the Milky Way and seven nearby galaxies. We compile measurements of molecular cloud and host galaxy properties, and determine mass-weighted mean cloud properties for entire galaxies and distinct subregions within. We find molecular clouds to be in ambient pressure-balanced virial equilibrium, where clouds in gas-rich, molecular-dominated, high-pressure regions are close to self-virialization, whereas clouds in gas-poor, atomic-dominated, low-pressure environments achieve a balance between their internal kinetic pressure and external pressure from the ambient medium. The SFE per free-fall time of molecular clouds is low, ∼0.1%–1%, and shows systematic variations of 2 dex as a function of the virial parameter and host galactic environment. The trend observed for clouds in low-pressure environments—as the solar neighborhood—is well matched by state-of-the-art turbulence-regulated models of star formation. However, these models substantially overpredict the low observed SFEs of clouds in high-pressure environments, which suggest the importance of additional physical parameters not yet considered by these models.
Proposal for a New Quantum Theory of Gravity

We recall a classical theory of torsion gravity with an asymmetric metric, sourced by a Nambu–Goto + Kalb–Ramond string [R. T. Hammond,  (2002)]. We explain why this is a significant gravitational theory and in what sense classical general relativity is an approximation to it. We propose that a noncommutative generalisation of this theory (in the sense of Connes’ noncommutative geometry and Adler’s trace dynamics) is a “quantum theory of gravity.” The theory is in fact a classical matrix dynamics with only two fundamental constants – the square of the Planck length and the speed of light, along with the two string tensions as parameters. The guiding symmetry principle is that the theory should be covariant under general coordinate transformations of noncommuting coordinates. The action for this noncommutative torsion gravity can be elegantly expressed as an invariant area integral and represents an atom of space–time–matter. The statistical thermodynamics of a large number of such atoms yields the laws of quantum gravity and quantum field theory, at thermodynamic equilibrium. Spontaneous localisation caused by large fluctuations away from equilibrium is responsible for the emergence of classical space–time and the field equations of classical general relativity. The resolution of the quantum measurement problem by spontaneous collapse is an inevitable consequence of this process. Quantum theory and general relativity are both seen as emergent phenomena, resulting from coarse graining of the underlying noncommutative geometry. We explain the profound role played by entanglement in this theory: entanglement describes interaction between the atoms of space–time–matter, and indeed entanglement appears to be more fundamental than quantum theory or space–time. We also comment on possible implications for black hole entropy and evaporation and for cosmology. We list the intermediate mathematical analysis that remains to be done to complete this programme.
Hunting for Heavy Dark Matter in the Galactic Center with Ground-based Cherenkov Telescopes

A TeV scale electroweak particle is a well motivated candidate for the dark matter (DM) of our Universe. Yet such a particle may only be detectable using indirect detection instruments sensitive to TeV-scale gamma rays that can result from dark matter annihilations. We present a mock analysis of the sensitivity for the present ground-based Cherenkov telescope array H.E.S.S. (High Energy Spectroscopic System) to detect TeV scale DM in the Galactic Center region. The work combines next-to-leading-logarithmic order calculations for the annihilation photon spectrum, as well as a comprehensive treatment of detector effects and expected backgrounds. Forecast limits on the sensitivity of H.E.S.S. have been derived across the important TeV mass range, assuming different DM density profiles and focusing on the canonical WIMP dark matter candidate Wino. These limits test our present and future ability to probe the predicted thermal cross section for some of the most promising DM candidates that could be discovered in the coming decade.
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection

A classical approach to abnormal activity detection is to learn a representation for normal activities from the training data and then use this learned representation to detect abnormal activities while testing. Typically, the methods based on this approach operate at a fixed timescale — either a single time-instant (e.g. frame-based) or a constant time duration (e.g. video-clip based). But human abnormal activities can take place at different timescales. For example, jumping is a short-term anomaly and loitering is a long-term anomaly in a surveillance scenario. A single and pre-defined timescale is not enough to capture the wide range of anomalies occurring with different time duration. In this paper, we propose a multi-timescale model to capture the temporal dynamics at different timescales. In particular, the proposed model makes future and past predictions at different timescales for a given input pose trajectory. The model is multi-layered where intermediate layers are responsible to generate predictions corresponding to different timescales. These predictions are combined to detect abnormal activities. In addition, we also introduce a single-camera abnormal activity dataset for research use that contains 483,566 annotated frames. Our experiments show that the proposed model can capture the anomalies of different time duration and outperforms existing methods.
LSTM vs. GRU vs. Bidirectional RNN for script generation

Scripts are an important part of any TV series. They narrate movements, actions and expressions of characters. In this paper, a case study is presented on how different sequence to sequence deep learning models perform in the task of generating new conversations between characters as well as new scenarios on the basis of a script (previous conversations). A comprehensive comparison between these models, namely, LSTM, GRU and Bidirectional RNN is presented. All the models are designed to learn the sequence of recurring characters from the input sequence. Each input sequence will contain, say "n" characters, and the corresponding targets will contain the same number of characters, except, they will be shifted one character to the right. In this manner, input and output sequences are generated and used to train the models. A closer analysis of explored models performance and efficiency is delineated with the help of graph plots and generated texts by taking some input string. These graphs describe both, intraneural performance and interneural model performance for each model.
Random Walk Model From the Point of View of Algorithmic Trading

Despite the fact that an intraday market price distribution is not normal, the random walk model of price behaviour is as important for the understanding of basic principles of the market as the pendulum model is a starting point of many fundamental theories in physics. This model is a good zero order approximation for liquid fast moving markets where the queue position is less important than the price action. In this paper we present an exact solution for the cost of the static passive slice execution. It is shown, that if a price has a random walk behaviour, there is no optimal limit level for an order execution: all levels have the same execution cost as an immediate aggressive execution at the beginning of the slice. Additionally the estimations for the risk of a limit order as well as the probability of a limit order execution as functions of the slice time and standard deviation of the price are derived.
Feature Partitioning for Efficient Multi-Task Architectures

Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures while taking resource constraints into consideration. We propose a search space that compactly represents different parameter sharing strategies. This provides more effective coverage and sampling of the space of multi-task architectures. We also present a method for quick evaluation of different architectures by using feature distillation. Together these contributions allow us to quickly optimize for efficient multi-task models. We benchmark on Visual Decathlon, demonstrating that we can automatically search for and identify multi-task architectures that effectively make trade-offs between task resource requirements while achieving a high level of final performance.
AI D\'ecor

Confused about renovating your space? Choosing the perfect color for your walls is always a challenging task. One does rounds of color consultation and several patch tests. This paper proposes an AI tool to pitch paint based on attributes of your room and other furniture, and visualize it on your walls. It makes the color selection process a whole lot easier. It basically takes in images of a room, detects furniture objects using YOLO object detection. Once these objects have been detected, the tool picks out color of the object. Later this object specific information gets appended to the room attributes (room_type, room_size, preferred_tone, etc) and a deep neural net is trained to make predictions for color/texture/wallpaper for the walls. Finally, these predictions are visualized on the walls from the images provided. The idea is to take the knowledge of a color consultant and pitch colors that suit the walls and provide a good contrast with the furniture and harmonize with different colors in the room. Transfer learning for YOLO object detection from the COCO dataset was used as a starting point and the weights were later fine-tuned by training on additional images. The model was trained on 1000 records listing the room and furniture attributes, to predict colors. Given the room image, this method finds the best color scheme for the walls. These predictions are then visualized on the walls in the image using image segmentation. The results are visually appealing and automatically enhance the color look-and-feel.