nils-herrmann/clean_scirepeval huggingface dataset:
Assessing BERT's Syntactic Abilities

I assess the extent to which the recently introduced BERT model captures English syntactic phenomena, using (1) naturally-occurring subject-verb agreement stimuli; (2) "coloreless green ideas" subject-verb agreement stimuli, in which content words in natural sentences are randomly replaced with words sharing the same part-of-speech and inflection; and (3) manually crafted stimuli for subject-verb agreement and reflexive anaphora phenomena. The BERT model performs remarkably well on all cases.
Latest results on dark matter searches with H.E.S.S

The nature of Dark Matter (DM) is one of the most debated questions of contemporary physics. Ground-based arrays of Cherenkov telescopes such as the High Energy Spectroscopic System (H.E.S.S.) search for DM signatures through the detection of Very-High-Energy (VHE, E > 100 GeV) gamma-rays. DM particles could selfannihilate in dense environments producing VHE γ-rays in the final states that could be eventually detected by H.E.S.S.. The H.E.S.S. observation strategy for DM search focuses towards the Galactic Centre (GC) region and nearby dwarf galaxy satellites of the Milky Way. The GC dataset provides the most stringent constraints on the DM annihilation cross section in the mass range 300 GeV - 70 TeV. Searches have been carried out towards classical and ultra-faint dwarf galaxies to test specific heavy DM models. The latest results towards the GC and dwarf galaxies are shown.
Seizure Detection Using Least Eeg Channels by Deep Convolutional Neural Network

This work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively. EEG Data is acquired from 29 pediatric patients admitted to KK Woman’s and Children’s Hospital who were diagnosed as typical absence seizures. We conduct leave-one-out cross validation for all subjects. Using full channel data, BPsvm yields a sensitivity of 86.6% and 0.84 false alarm (per hour) while SeizNet yields overall sensitivity of 95.8 % with 0.17 false alarm. More interestingly, two channels seizNet outperforms full channel BPsvm with a sensitivity of 93.3% and 0.58 false alarm. We further investigate interpretability of SeizNet by decoding the filters learned along convolutional layers. Seizure-like characteristics can be clearly observed in the filters from third and forth convolutional layers.
Slow Decay of Impact in Equity Markets: Insights From the ANcerno Database

We present an empirical study of price reversion after the executed metaorders. We use a dataset with more than 8 million metaorders executed by institutional investors in the US equity market. We show that relaxation takes place as soon as the metaorder ends: while at the end of the same day, it is on average  see text] of the peak impact, the decay continues for the next few days, following a power-law function at short-time scales, and converges to a non-zero asymptotic value at long-time scales ( see text] days) equal to  see text] of the impact at the end of the first day, that is  see text] of peak impact. Due to a significant, multiday correlation of the sign of executed metaorders, a careful deconvolution of the observed impact must be performed to extract the estimate of the impact decay of isolated metaorders.
A Review of Domain Adaptation without Target Labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: How can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based, and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting, and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.
Representation Learning on Graphs: A Reinforcement Learning Application

In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation policy iteration (RPI). We consider the limitations of proto-value functions (PVFs) at accurately approximating the value function in low dimensions and we highlight the importance of features learning for an improved low-dimensional value function approximation. Then, we adopt different representation learning algorithm on graphs to learn the basis functions that best represent the value function. We empirically show that node2vec, an algorithm for scalable feature learning in networks, and the Variational Graph Auto-Encoder constantly outperform the commonly used smooth proto-value functions in low-dimensional feature space.
UAN: Unified Attention Network for Convolutional Neural Networks

We propose a new architecture that learns to attend to different Convolutional Neural Networks (CNN) layers (i.e., different levels of abstraction) and different spatial locations (i.e., specific layers within a given feature map) in a sequential manner to perform the task at hand. Specifically, at each Recurrent Neural Network (RNN) timestep, a CNN layer is selected and its output is processed by a spatial soft-attention mechanism. We refer to this architecture as the Unified Attention Network (UAN), since it combines the "what" and "where" aspects of attention, i.e., "what" level of abstraction to attend to, and "where" should the network look at. We demonstrate the effectiveness of this approach on two computer vision tasks: (i) image-based camera pose and orientation regression and (ii) indoor scene classification. We evaluate our method on standard benchmarks for camera localization (Cambridge, 7-Scene, and TUM-LSI datasets) and for scene classification (MIT-67 indoor dataset), and show that our method improves upon the results of previous methods. Empirically, we show that combining "what" and "where" aspects of attention improves network performance on both tasks.
Ancient solutions in Lagrangian mean curvature flow

Ancient solutions of Lagrangian mean curvature flow in C^n naturally arise as Type II blow-ups. In this extended note we give structural and classification results for such ancient solutions in terms of their blow-down and, motivated by the Thomas-Yau Conjecture, focus on the almost calibrated case. In particular, we classify Type II blow-ups of almost calibrated Lagrangian mean curvature flow when the blow-down is a pair of transverse planes or, when n=2, a multiplicity two plane. We also show that the Harvey-Lawson Clifford torus cone in C^3 cannot arise as the blow-down of an almost calibrated Type II blow-up.
Location, Occupation, and Semantics Based Socioeconomic Status Inference on Twitter

The socioeconomic status of people depends on a combination of individual characteristics and environmental variables, thus its inference from online behavioral data is a difficult task. Attributes like user semantics in communication, habitat, occupation, or social network are all known to be determinant predictors of this feature. In this paper we propose three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. Our methods are based on open census data, crawled professional profiles, and remotely sensed, expert annotated information on living environment. Our inference models reach similar performance of earlier results with the advantage of relying on broadly available datasets and of providing a generalizable framework to estimate socioeconomic status of large numbers of Twitter users. These results may contribute to the scientific discussion on social stratification and inequalities, and may fuel several applications.
Lindenbaum Method.

During his brief life, the Polish mathematician and logician Adolf Lindenbaum (1904--1941) contributed to mathematical logic, among other things, by several significant achievements. Some results of Lindenbaum's, which bear his name, were published without proofs by other people from the Lvov--Warsaw School and the proofs later were provided by some others, though the authorship of Lindenbaum has never been challenged. Many may have heard about Lindenbaum's lemma, asserting the existence of Lindenbaum's extension, and Lindenbaum-Tarski algebra; less known is Lindenbaum's logical matrix. This tutorial is devoted to the two last concepts rather than the first one. However, the latter can be understood in a purely algebraic fashion, if one employs the notion of Lindenbaum-Tarski algebra. In general, the notions of Lindenbaum matrix and Lindenbaum-Tarski algebra have paved a way to further algebraization of logic, which had been begun by George Boole in the 19th century, as well as to a new branch of logic, model theory. For this reason, the present tutorial is also a gentle introduction to algebraic logic. A uniting idea of the aforementioned concepts is a special view on the formal judgments of a formal language. It is this view we call the Lindenbaum method. Although Lindenbaum expressed merely a starting viewpoint in the tradition of Polish logic of the time, this viewpoint became a standard ever since and its development goes on until this day, continuing to shape the field of algebraic logic. Our main objective is to demonstrate how this view gave rise to formulating the aforementioned concepts and how it opens door to unexplored paths.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!

The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user’s responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot’s dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.
Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents

In this paper we propose a new training loop for deep reinforcement learning agents with an evolutionary generator. Evolutionary procedural content generation has been used in the creation of maps and levels for games before. Our system incorporates an evolutionary map generator to construct a training curriculum that is evolved to maximize loss within the state-of-the-art Double Dueling Deep Q Network architecture with prioritized replay. We present a case-study in which we prove the efficacy of our new method on a game with a discrete, large action space we made called Attackers and Defenders. Our results demonstrate that training on an evolutionarily-curated curriculum (directed sampling) of maps both expedites training and improves generalization when compared to a network trained on an undirected sampling of maps.
Soft Constraints for Inference with Declarative Knowledge

We develop a likelihood free inference procedure for conditioning a probabilistic model on a predicate. A predicate is a Boolean valued function which expresses a yes/no question about a domain. Our contribution, which we call predicate exchange, constructs a softened predicate which takes value in the unit interval [0, 1] as opposed to a simply true or false. Intuitively, 1 corresponds to true, and a high value (such as 0.999) corresponds to "nearly true" as determined by a distance metric. We define Boolean algebra for soft predicates, such that they can be negated, conjoined and disjoined arbitrarily. A softened predicate can serve as a tractable proxy to a likelihood function for approximate posterior inference. However, to target exact inference, we temper the relaxation by a temperature parameter, and add a accept/reject phase use to replica exchange Markov Chain Mont Carlo, which exchanges states between a sequence of models conditioned on predicates at varying temperatures. We describe a lightweight implementation of predicate exchange that it provides a language independent layer that can be implemented on top of existingn modeling formalisms.
A System Dynamics Analysis of National R&D Performance Measurement System in Korea

Peer review is one of useful and powerful performance measurement process. In Korea, it needs to increase quality of R&D performance, but bibliometric evaluation and lack of peers have opposite effect. We used system dynamics to describe Korean R&D performance measurement system and ways to increase performance quality. To meet a desired R&D performance quality, increasing fairness and quality of evaluation is needed. Size of peer pool decreased because of the specialization of R&D projects and the Sangpi process both, and it is critical to acquire both fairness and quality. Also, shortening evaluation period affect to R&D performance quality, by causing workloads increase, limiting long-term and innovative R&D projects, and decreasing evaluation quality. Previous evaluation policies do a role like micro-controlling the R&D's activities, but increasing the size of peer pool and changing evaluation period would make a change to quality and fairness of evaluation.
The Faintest Dwarf Galaxies

The lowest luminosity ( see text] L[Formula: see text]) Milky Way satellite galaxies represent the extreme lower limit of the galaxy luminosity function. These ultra-faint dwarfs are the oldest, most dark matter–dominated, most metal-poor, and least chemically evolved stellar systems known. They therefore provide unique windows into the formation of the first galaxies and the behavior of dark matter on small scales. In this review, we summarize the discovery of ultra-faint dwarfs in the Sloan Digital Sky Survey in 2005 and the subsequent observational and theoretical progress in understanding their nature and origin. We describe their stellar kinematics, chemical abundance patterns, structural properties, stellar populations, orbits, and luminosity function, as well as what can be learned from each type of measurement. We conclude the following: ▪ In most cases, the stellar velocity dispersions of ultra-faint dwarfs are robust against systematic uncertainties such as binary stars and foreground contamination. ▪ The chemical abundance patterns of stars in ultra-faint dwarfs require two sources of r-process elements, one of which can likely be attributed to neutron star mergers. ▪ Even under conservative assumptions, only a small fraction of ultra-faint dwarfs may have suffered significant tidal stripping of their stellar components. ▪ Determining the properties of the faintest dwarfs out to the virial radius of the Milky Way will require very large investments of observing time with future telescopes. Finally, we offer a look forward at the observations that will be possible with future facilities as the push toward a complete census of the Local Group dwarf galaxy population continues.
Modified teleparallel theories of gravity in symmetric spacetimes

Teleparallel gravity theories employ a tetrad and a Lorentz spin connection as independent variables in their covariant formulation. In order to solve their field equations, it is helpful to search for solutions which exhibit certain amounts of symmetry, such as spherical or cosmological symmetry. In this article we present how to apply the notion of spacetime symmetries known from Cartan geometry to teleparallel geometries. We explicitly derive the most general tetrads and spin connections which are compatible with axial, spherical, cosmological and maximal symmetry. For homogeneous and isotropic spacetime symmetry we find that the tetrads and spin connection found by the symmetry constraints are "good", in the sense that solve they anti-symmetric, resp. spin connection, field equation universally for any possible teleparallel theory of gravity.
Detecting dark matter with neutron star spectroscopy

The presence of dark matter has been ascertained through a wealth of astrophysical and cosmological phenomena and its nature is a central puzzle in modern science. Elementary particles stand as the most compelling explanation. They have been intensively searched for at underground laboratories looking for an energy recoil signal and at telescopes sifting for excess events in gamma-ray or cosmic-ray observations. In this work, we investigate a detection method based on spectroscopy measurements of neutron stars. We outline the luminosity and age of neutrons stars whose dark matter scattering off neutrons can heat neutron stars up to a measurable level. We show that in this case neutron star spectroscopy could constitute the best probe for dark matter particles over a wide masses and interactions strength.
Multiparticle potentials from lightlike Wilson lines in quark-gluon plasmas: A generalized relation of in-medium splitting rates to jet-quenching parameters 
q^

A powerful historical insight about the theory of in-medium showering in QCD backgrounds was that splitting rates can be related to a parameter $\hat q$ that characterizes the rate of transverse-momentum kicks to a high-energy particle from the medium. Another powerful insight was that $\hat q$ can be defined (with caveats) even when the medium is strongly coupled, using long, narrow Wilson loops whose two long edges are light-like Wilson lines. The medium effects for the original calculations of in-medium splitting rates can be formulated in terms of 3-body imaginary-valued `potentials' that are defined with three long, light-like Wilson lines. Corrections due to overlap of two consecutive splittings can be calculated using similarly defined 4-body potentials. I give a simple argument for how such $N$-body potentials can be determined in the appropriate limit just from knowledge of the values of $\hat q$ for different color representations. For $N > 3$, the $N$-body potentials have non-trivial color structure, which will complicate calculations of overlap corrections outside of the large-$N_c$ or soft bremsstrahlung limits.
Neutron Star Heating Constraints on Wave-Function Collapse Models.

Spontaneous wave-function collapse models, like continuous spontaneous localization, are designed to suppress macroscopic superpositions while preserving microscopic quantum phenomena. An observable consequence of collapse models is spontaneous heating of massive objects. We calculate the collapse-induced heating rate of astrophysical objects, and the corresponding equilibrium temperature. We apply these results to neutron stars, the densest phase of baryonic matter in the Universe. Stronger collapse model parameters imply greater heating, allowing us to derive competitive bounds on model parameters using neutron star observational data, and to propose speculative bounds based on the capabilities of current and future astronomical surveys.
A predicted correlation between age gradient and star formation history in FIRE dwarf galaxies

We explore the radial variation of star formation histories (SFHs) in dwarf galaxies simulated with Feedback In Realistic Environments (FIRE) physics. The sample contains 26 field dwarf galaxies with Mstar = 105–109 M⊙. We find age gradients are common in our dwarfs, with older stars dominant at large radii. The strength of the gradient correlates with overall galaxy age such that earlier star formation produces a more pronounced gradient. The relation between formation time and strength of the gradient is driven by both mergers and star formation feedback. Mergers can both steepen and flatten the age gradient depending on the timing of the merger and SFHs of the merging galaxy. In galaxies without significant mergers, feedback pushes stars to the outskirts. The strength of the age gradient is determined by the subsequent evolution of the galaxy. Galaxies with weak age gradients constantly grow to z  = 0, meaning that young star formation occurs at a similar radius to which older stars are heated to. In contrast, galaxies with strong age gradients tend to maintain a constant half-mass radius over time. If real galaxies have age gradients as we predict, stellar population studies that rely on sampling a limited fraction of a galaxy can give a biased view of its global SFH. Central fields can be biased young by Gyrs while outer fields are biased old. Fields positioned near the 2D half-light radius will provide the least biased measure of a dwarf galaxy’s global SFH.
Comparative Analysis of Content-based Personalized Microblog Recommendations [Experiments and Analysis]

Microblogging platforms constitute a popular means of real-time communication and information sharing. They involve such a large volume of user-generated content that their users suffer from an information deluge. To address it, numerous recommendation methods have been proposed to organize the posts a user receives according to her interests. The content-based methods typically build a text-based model for every individual user to capture her tastes and then rank the posts in her timeline according to their similarity with that model. Even though content-based methods have attracted lots of interest in the data management community, there is no comprehensive evaluation of the main factors that affect their performance. These are: (i) the representation model that converts an unstructured text into a structured representation that elucidates its characteristics, (ii) the source of the microblog posts that compose the user models, and (iii) the type of user's posting activity. To cover this gap, we systematically examine the performance of 9 state-of-the-art representation models in combination with 13 representation sources and 3 user types over a large, real dataset from Twitter comprising 60 users. We also consider a wide range of 223 plausible configurations for the representation models in order to assess their robustness with respect to their internal parameters. To facilitate the interpretation of our experimental results, we introduce a novel taxonomy of representation models. Our analysis provides novel insights into the performance and functionality of the main factors determining the performance of content-based recommendation in microblogs.