nils-herrmann/clean_scirepeval huggingface dataset:
Talk the Walk: Navigating New York City through Grounded Dialogue

We introduce "Talk The Walk", the first large-scale dialogue dataset grounded in action and perception. The task involves two agents (a "guide" and a "tourist") that communicate via natural language in order to achieve a common goal: having the tourist navigate to a given target location. The task and dataset, which are described in detail, are challenging and their full solution is an open problem that we pose to the community. We (i) focus on the task of tourist localization and develop the novel Masked Attention for Spatial Convolutions (MASC) mechanism that allows for grounding tourist utterances into the guide's map, (ii) show it yields significant improvements for both emergent and natural language communication, and (iii) using this method, we establish non-trivial baselines on the full task.
Predicting property damage from tornadoes with deep learning

Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations in tornado-prone areas continue to increase in density and extent. Here, we use artificial neural networks to predict tornado-induced property damage using publicly available data. We find that the large number of tornadoes which cause zero property damage (30.6% of the data) poses a challenge for predictive models. We developed a model that predicts whether a tornado will cause property damage to a high degree of accuracy (out of sample accuracy = 0.829 and AUROC = 0.873). Conditional on a tornado causing damage, another model predicts the amount of damage. When combined, these two models yield an expected value for the amount of property damage caused by a tornado event. From the best-performing models (out of sample mean squared error = 0.089 and R2 = 0.473), we provide an interactive, gridded map of monthly expected values for the year 2018. One major weakness is that the model predictive power is optimized with log-transformed, mean-normalized property damages, however this leads to large natural-scale residuals for the most destructive tornadoes. The predictive capacity of this model along with an interactive interface may provide an opportunity for science-informed tornado disaster planning.
Universal Transformers

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.
Spatial Embedding Imposes Constraints on the Network Architectures of Neural Systems

A fundamental understanding of the network architecture of the brain is necessary for the further development of theories explicating circuit function. Perhaps as a derivative of its initial application to abstract informational systems, network science provides many methods and summary statistics that address the network's topological characteristics with little or no thought to its physical instantiation. Recent progress has capitalized on quantitative tools from network science to parsimoniously describe and predict neural activity and connectivity across multiple spatial and temporal scales. Yet, for embedded systems, physical laws can directly constrain processes of network growth, development, and function, and an appreciation of those physical laws is therefore critical to an understanding of the system. Recent evidence demonstrates that the constraints imposed by the physical shape of the brain, and by the mechanical forces at play in its development, have marked effects on the observed network topology and function. Here, we review the rules imposed by space on the development of neural networks and show that these rules give rise to a specific set of complex topologies. We present evidence that these fundamental wiring rules affect the repertoire of neural dynamics that can emerge from the system, and thereby inform our understanding of network dysfunction in disease. We also discuss several computational tools, mathematical models, and algorithms that have proven useful in delineating the effects of spatial embedding on a given networked system and are important considerations for addressing future problems in network neuroscience. Finally, we outline several open questions regarding the network architectures that support circuit function, the answers to which will require a thorough and honest appraisal of the role of physical space in brain network anatomy and physiology.
The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We reproduce the current state of the art and go beyond fairly exploring the GAN landscape. We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
Intermediate-mass black hole growth and feedback in dwarf galaxies at high redshifts

Intermediate-mass black holes (IMBHs; masses between $100\rm{\, and \,}10^{6} \, \mathrm{M}_{\odot }$) historically comprise of an elusive population compared to stellar-mass and supermassive black holes (BHs). Recently, IMBHs have started to be observed at the centres of low-mass galaxies. We perform cosmological hydrodynamical simulations of $(2 \, h^{-1} ~ {\rm Mpc})^3$ comoving boxes and investigate the growth and feedback of central IMBHs in dwarf galaxies (DGs). The earliest BHs appear at z ∼ 18–25 and grow thereafter by accreting gas and by merger with other BHs. We find that, starting from $10^{2} \, \mathrm{M}_{\odot }$, it is possible to build up IMBHs of a few$\times 10^{5}\!-\!10^{6} \, \mathrm{M}_{\odot }$ by z = 5, when the BHs are seeded in haloes less massive than $4 \times 10^{7} \, \mathrm{M}_{\odot }$. The BH accretion rates increase with time and reach $\dot{M}_{\rm BH} = (0.2\!-\!0.8) \dot{M}_{\rm Edd}$ for the massive IMBHs by z = 4. The star formation rate density (SFRD) evolution of the DGs (stellar mass $10^{5}\!-\!10^{8} \, \mathrm{M}_{\odot }$) has a peak plateau between z = 4 and 6. Star formation is quenched between z = 9 and 4. The SFRD is reduced by factors up to 3 when the BHs have grown to a few times $10^5 \, \mathrm{M}_{\odot }$. Even in the presence of stronger supernova (SN)-driven mass ejection, the BHs continue to grow up to z ∼ 6, sustained by gas inflows driven by galaxy mergers and interactions in a cosmological environment. Our conclusions, based on numerical simulation results, support the scenario that early feedback from IMBHs in gas-rich DGs at z = 5–8 can potentially solve several anomalies in the DG mass range within the concordance Λ cold dark matter (ΛCDM) cosmological scenario (Silk 2017). Our results suggest that IMBHs at DG centres grow faster than their host galaxies in the early Universe, and the resulting BH feedback turns the DGs and the BHs dormant.
Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate

Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter in 1988, Pinker and Prince presented a comprehensive rebuttal of many of Rumelhart and McClelland’s claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince’s criticisms without requiring any simplification of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a reexamination of their utility in linguistic and cognitive modeling.
Improved methods for making inferences about multiple skipped correlations

A skipped correlation has the advantage of dealing with outliers in a manner that takes into account the overall structure of the data cloud. For p-variate data, , there is an extant method for testing the hypothesis of a zero correlation for each pair of variables that is designed to control the probability of one or more Type I errors. And there are methods for the related situation where the focus is on the association between a dependent variable and p explanatory variables. However, there are limitations and several concerns with extant techniques. The paper describes alternative approaches that deal with these issues.
Predictably missing satellites: subhalo abundances in Milky Way-like haloes

On small scales there have been a number of claims of discrepancies between the standard cold dark matter (CDM) model and observations. The ‘missing satellites problem’ infamously describes the overabundance of subhaloes from CDM simulations compared to the number of satellites observed in the Milky Way. A variety of solutions to this discrepancy have been proposed; however, the impact of the specific properties of the Milky Way halo relative to the typical halo of its mass has yet to be explored. Motivated by recent studies that identified ways in which the Milky Way is atypical, we investigate how the properties of dark matter haloes with mass comparable to our Galaxy’s – including concentration, spin, shape, and scale factor of the last major merger – correlate with the subhalo abundance. Using zoom-in simulations of Milky Way-like haloes, we build two models of subhalo abundance as functions of host halo properties. From these models we conclude that the Milky Way most likely has fewer subhaloes than the average halo of the same mass. We expect up to 30 per cent fewer subhaloes with low maximum rotation velocities ($V_{\rm max}^{\rm sat} \sim 10$ km s−1) at the 68 per cent confidence level and up to 52 per cent fewer than average subhaloes with high rotation velocities ($V_{\rm max}^{\rm sat} \gtrsim 30$ km s−1, comparable to the Magellanic Clouds) than would be expected for a typical halo of the Milky Way’s mass. Concentration is the most informative single parameter for predicting subhalo abundance. Our results imply that models tuned to explain the missing satellites problem assuming typical subhalo abundances for our Galaxy may be overcorrecting.
Consumption smoothing in the working-class households of interwar Japan

I analyze Osaka factory worker households in the early 1920s, whether idiosyncratic income shocks were shared efficiently, and which consumption categories were robust to shocks. While the null hypothesis of full risk-sharing of total expenditures was rejected, factory workers maintained their households, in that they paid for essential expenditures (rent, utilities, and commutation) during economic hardship. Additionally, children's education expenditures were possibly robust to idiosyncratic income shocks. The results suggest that temporary income is statistically significantly increased if disposable income drops due to idiosyncratic shocks. Historical documents suggest microfinancial lending and saving institutions helped mitigate risk-based vulnerabilities.
On discovering functions in actin filament automata

We simulate an actin filament as an automaton network. Every atom takes two or three states and updates its state, in discrete time, depending on a ratio of its neighbours in some selected state. All atoms/automata simultaneously update their states by the same rule. Two state transition rules are considered. In semi-totalistic Game of Life like actin filament automaton atoms take binary states ‘0’ and ‘1’ and update their states depending on a ratio of neighbours in the state ‘1’. In excitable actin filament automaton atoms take three states: resting, excited and refractory. A resting atom excites if a ratio of its excited neighbours belong to some specified interval; transitions from excited state to refractory state and from refractory state to resting state are unconditional. In computational experiments, we implement mappings of an 8-bit input string to an 8-bit output string via dynamics of perturbation/excitation on actin filament automata. We assign eight domains in an actin filament as I/O ports. To write True to a port, we perturb/excite a certain percentage of the nodes in the domain corresponding to the port. We read outputs at the ports after some time interval. A port is considered to be in a state True if a number of excited nodes in the port's domain exceed a certain threshold. A range of eight-argument Boolean functions is uncovered in a series of computational trials when all possible configurations of eight-elements binary strings were mapped onto excitation outputs of the I/O domains.
Exposure to urban parks improves affect and reduces negativity on Twitter

Urbanization and the decline of access to nature have coincided with a rise of mental health problems. A growing body of research has demonstrated an association between nature contact and improved mental affect (i.e., mood). However, previous approaches have been unable to quantify the benefits of urban greenspace exposure and compare how different types of outdoor public spaces impact mood. Here, we use Twitter to investigate how mental affect varies before, during, and after visits to a large urban park system. We analyze the sentiment of tweets to estimate the magnitude and duration of the affect benefit of visiting parks. We find that affect is substantially higher during park visits and remains elevated for several hours following the visit. Visits to Regional Parks, which are greener and have greater vegetative cover, result in a greater increase in affect compared to Civic Plazas and Squares. Finally, we analyze the words in tweets around park visits to explore several theorized mechanisms linking nature exposure with mental and cognitive benefits. Negation words such as"no","not", and"don't"decrease in frequency during visits to urban parks. These results point to the most beneficial types of nature contact for mental health benefits and can be used by urban planners and public health officials to improve the well-being of growing urban populations.
Optimal continuous state POMDP planning with semantic observations

This work develops novel strategies for optimal planning with semantic observations using continuous state Partially Observable Markov Decision Processes (CPOMDPs). We propose two major innovations to Gaussian mixture (GM) CPOMDP policy approximation methods. While these state of the art methods have many theoretically nice properties, they are hampered by the inability to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes (VB) GM approximations of PBVI Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with binary semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state of the art GM approximations, but require significantly less modeling overhead and runtime cost.
Generating an ATL Model Checker using an Attribute Grammar

In this paper we use attribute grammars as a formal approach for model checkers development. Our aim is to design an ATL (Alternating-Time Temporal Logic) model checker from a context-free grammar which generates the language of the ATL formulas. An attribute grammar may be informally defined as a context-free grammar which is extended with a set of attributes and a collection of semantic rules. We use an ATL attribute grammar for specifying an operational semantics of the language of the ATL formulas by defining a translation into the language which describes the set of states from the ATL model where the corresponding ATL formulas are satisfied. We provide a formal definition for an attribute grammar used as input for Another Tool for Language Recognition (ANTLR) to generate an ATL model checker. Also, the technique of implementing the semantic actions in ANTLR is presented, which is the concept of connection between attribute evaluation in the grammar that generates the language of ATL formulas and algebraic compiler implementation that represents the ATL model checker. The original implementation of the model checking algorithm is based on Relational Databases and Web Services. Several database systems and Web Services technologies were used for evaluating the system performance in verification of large ATL models.
Evidence for Superconductivity at Ambient Temperature and Pressure in Nanostructures

Despite being a low temperature phenomenon till date, superconductivity has found numerous applications in diverse fields of medicine, science and engineering. The great scientific interest in the phenomenon as well as its practical utility has motivated extensive efforts to discover and understand new superconductors. We report the observation of superconductivity at ambient temperature and pressure conditions in films and pellets of a nanostructured material that is composed of silver particles embedded into a gold matrix. Specifically, we observe that upon cooling below 236 K at ambient pressures, the resistance of sample films drops below 10-4 Ohm, being limited by instrument sensitivity. Further, below the transition temperature, samples become strongly diamagnetic, with volume susceptibilities as low as -0.056. We further describe methods to tune the transition to temperatures higher than room temperature.
TASI Lectures on Early Universe Cosmology: Inflation, Baryogenesis and Dark Matter

These lectures, presented at TASI 2018, provide a concise introduction to inflation, baryogenesis, and aspects of dark matter not covered by the other lectures. The emphasis for inflation is an intuitive understanding and techniques for constraining inflationary models. For baryogenesis we focus on two examples, leptogenesis and electroweak baryogenesis, with attention to singlet-assisted two-step phase transitions. Concerning dark matter, we review different classes of models distinguished by their mechanisms for obtaining the observed relic density, including thermal freeze-out, asymmetric dark matter, freeze-in, SIMP dark matter, the misalignment mechanism for ultralight scalars and axions, and production of primordial black holes during inflation.
Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN). Consisting of a perception module that extracts representations of latent object properties and a prediction module that uses those extracted properties to simulate system dynamics, the PPN can be trained in an end-to-end fashion purely from samples of object dynamics. The representations of latent object properties learned by PPNs not only are sufficient to accurately simulate the dynamics of systems comprised of previously unseen objects, but also can be translated directly into human-interpretable properties (e.g., mass, coefficient of restitution) in an entirely unsupervised manner. Crucially, PPNs also generalize to novel scenarios: their gradient-based training can be applied to many dynamical systems and their graph-based structure functions over systems comprised of different numbers of objects. Our results demonstrate the efficacy of graph-based neural architectures in object-centric inference and prediction tasks, and our model has the potential to discover relevant object properties in systems that are not yet well understood.
Effective action of dilaton gravity as the classical double copy of Yang-Mills theory

We compute the classical effective action of color charges moving along worldlines by integrating out the Yang-Mills gauge field to next-to-leading order in the coupling. An adapted version of the Bern-Carrasco-Johansson (BCJ) double-copy construction known from quantum scattering amplitudes is then applied to the Feynman integrands, yielding the prediction for the classical effective action of point masses in dilaton gravity. We check the validity of the result by independently constructing the effective action in dilaton gravity employing field redefinitions and gauge choices that greatly simplify the perturbative construction. Complete agreement is found at next-to-leading order. Finally, upon performing the post-Newtonian expansion of our result, we find agreement with the corresponding action of scalar-tensor theories known from the literature. Our results represent a proof of concept for the classical double-copy construction of the gravitational effective action and provides another application of a BCJ-like double copy beyond scattering amplitudes.