nils-herrmann/clean_scirepeval huggingface dataset:
Learning Stages: Phenomenon, Root Cause, Mechanism Hypothesis, and Implications

Under StepDecay learning rate strategy (decaying the learning rate after pre-defined epochs), it is a common phenomenon that the trajectories of learning statistics (training loss, test loss, test accuracy, etc.) are divided into several stages by sharp transitions. This paper studies the phenomenon in detail. Carefully designed experiments suggest the root cause to be the stochasticity of SGD. The convincing fact is the phenomenon disappears when Batch Gradient Descend is adopted. We then propose a hypothesis about the mechanism behind the phenomenon: the noise from SGD can be magnified to several levels by different learning rates, and only certain patterns are learnable within a certain level of noise. Patterns that can be learned under large noise are called easy patterns and patterns only learnable under small noise are called complex patterns. We derive several implications inspired by the hypothesis: (1) Since some patterns are not learnable until the next stage, we can design an algorithm to automatically detect the end of the current stage and switch to the next stage to expedite the training. The algorithm we design (called AutoDecay) shortens the time for training ResNet50 on ImageNet by $ 10 $\% without hurting the performance. (2) Since patterns are learned with increasing complexity, it is possible they have decreasing transferability. We study the transferability of models learned in different stages. Although later stage models have superior performance on ImageNet, we do find that they are less transferable. The verification of these two implications supports the hypothesis about the mechanism.
Einstein Metrics, Self-Dual Weyl Curvature, and Conformally Kaehler Geometry

Peng Wu recently announced a beautiful characterization of conformally Kaehler, Einstein metrics of positive scalar curvature on compact oriented 4-manifolds via the condition det (W^+) > 0. In this note, we buttress his claim by providing an entirely different proof of his result. We then present further consequences of our method, which builds on techniques previously developed in (LeBrun 2015).
DoorGym: A Scalable Door Opening Environment And Baseline Agent

Reinforcement Learning (RL) has brought forth ideas of autonomous robots that can navigate real-world environments with ease, aiding humans in a variety of tasks. RL agents have just begun to make their way out of simulation into the real world. Once in the real world, benchmark tasks often fail to transfer into useful skills. We introduce DoorGym, a simulation environment intended to be the first step to move RL from toy environments towards useful atomic skills that can be composed and extended towards a broader goal.  We also provide a baseline PPO (Proximal Policy Optimization) and SAC (Soft Actor-Critic)implementation, which achieves a success rate of up to 70% for common tasks in this environment. Environment kit available here:this https URL
Teaching Forced Damped Oscillator Using RLC Circuit Through Inverse Modeling

Teaching by direct models in science has been weakening the learning process of the students, because the real problems in engineering are not solved by direct models instead commonly they are solved by inverse models. On the other hand, one of the most relevant topics in the course of waves and particle physics oriented for the forming engineers; it's the subject of simple harmonic motion forced damping, which many physical phenomena can be explained as the quality factor Q and the resonance frequency of an oscillatory forced system. In order to capture the attention of students and give an application to this issue. We have developed an experimental setup to take measurements of electric current, voltages from capacitor and inductor for different frequencies and resistances, once the experimental data were collected to study the behavior of the electrical current inside the circuit and find out the RLC parameters with an inverse model. Finally, we want to show the process in detail how parameters of the system (Resistance, Inductance and Capacitance values) are very relevant in this kind of systems, from the results obtained by experimental measurements of voltage, current and angle of phase shift, where this was achieved by implementing an indirect method described in this document, so that can be applied to studies of more complex systems such as a motor where such parameters may be unknown.
The Science Training Program for Young Italian Physicists and Engineers at Fermilab

Since 1984 Fermilab has been hosting a two-month summer training program for selected undergraduate and graduate Italian students in physics and engineering. Building on the traditional close collaboration between the Italian National Institute of Nuclear Physics (INFN) and Fermilab, the program is supported by INFN, by the DOE and by the Scuola Superiore di Sant`Anna of Pisa (SSSA), and is run by the Cultural Association of Italians at Fermilab (CAIF). This year the University of Pisa has qualified it as a “University of Pisa Summer School”, and will grant successful students with European Supplementary Credits. Physics students join the Fermilab HEP research groups, while engineers join the Particle Physics, Accelerator, Technical, and Computing Divisions. Some students have also been sent to other U.S. laboratories and universities for special trainings. The programs cover topics of great interest for science and for social applications in general, like advanced computing, distributed data analysis, nanoelectronics, particle detectors for earth and space experiments, high precision mechanics, applied superconductivity. In the years, over 350 students have been trained and are now employed in the most diverse fields in Italy, Europe, and the U.S. In addition, the existing Laurea Program in Fermilab Technical Division was extended tomore » the whole laboratory, with presently two students in Master’s thesis programs on neutrino physics and detectors in the Neutrino Division. And finally, a joint venture with the Italian Scientists and Scholars North-America Foundation (ISSNAF) provided this year 4 professional engineers free of charge for Fermilab. More details on all of the above can be found below.« less
The Italian Summer Students Program at Fermi National Accelerator Laboratory and other US Laboratories

Since 1984 the INFN groups have been running a 2-month summer training program for Italian students at Fermilab. In 1984 the program involved only a few physics students from the Pisa group, but it was later extended to other groups and to engineering students. Since 2004 the program has been supported in part by DOE in the frame of an exchange agreement with INFN. In 2007 the Sant’Anna School of Advanced Studies (Pisa) established an agreement with Fermilab to share the cost of 4 engineering students each year. In the 34 years of its history, the program has involved approximately 500 Italian students from more than 20 Italian universities and from some European universities. In the years 2010-2017, with the support of INFN, INAF, ASI, ISSNAF and the Cultural Association of Italians at Fermilab (CAIF), 20 students were hosted in several other US laboratories and universities. The Fermilab training programs spanned from data analysis to design and construction of particle detectors and accelerator components, R/D on superconductive elements, theory of accelerators, analysis of astrophysical data. In 2015 the University of Pisa created an ad-hoc Summer School. Fermilab interns are enrolled at the University of Pisa for the duration of the internship. They are required to write summary reports published in the Fermilab and University of Pisa web pages. Upon positive evaluation of a University of Pisa committee, students are acknowledged 6 ECTS credits. In 2017 a new agreement with the summer school "MovingKnowledge17" promoted by Oxford University and dedicated to neutrino physics allowed to 3 Fermilab summer students to spend also 4 weeks at Oxford working with the local groups. The entire program will grow under CAIF management. INFN has granted his sponsorship to CAIF, and an agreement has been negotiated between CAIF and ASI for fellowships in US space science laboratories supported by ASI. The program is also part of the outreach of the European Projects MUSE (H2020-MSCA-RISE-2015, GA 690835) and NEWS (H2020-MSCA-RISE-2016, GA 734303). We report on recruiting method, training programs, and on final students’ evaluation.
WiSer: A Highly Available HTAP DBMS for IoT Applications

In a classic transactional distributed database management system (DBMS), write transactions invariably synchronize with a coordinator before final commitment. While enforcing serializability, this model has long been criticized for not satisfying the applications’ availability requirements. When entering the era of Internet of Things (IoT), this problem has become more severe, as an increasing number of applications call for the capability of hybrid transactional and analytical processing (HTAP), where aggregation constraints need to be enforced as part of transactions. Current systems work around this by creating escrows, allowing occasional overshoots of constraints, which are handled via compensating application logic.The WiSer DBMS targets consistency with availability, by splitting the database commit into two steps. First, a PROMISE step that corresponds to what humans are used to as commitment, and runs without talking to a coordinator. Second, a SERIALIZE step, that fixes transactions’ positions in the serializable order, via a consensus procedure. We achieve this split via a novel data representation that embeds read-sets into transaction deltas, and serialization sequence numbers into table rows. WiSer does no sharding (all nodes can run transactions that modify the entire database), and yet enforces aggregation constraints. Both read-write conflicts and aggregation constraint violations are resolved lazily in the serialized data. WiSer also covers node joins and departures as database tables, thus simplifying correctness and failure handling. We present the design of WiSer as well as experiments suggesting this approach has promise.
Circular Proofs as Session-Typed Processes: A Local Validity Condition

Proof theory provides a foundation for studying and reasoning about
programming languages, most directly based on the well-known Curry-Howard
isomorphism between intuitionistic logic and the typed lambda-calculus. More
recently, a correspondence between intuitionistic linear logic and the
session-typed pi-calculus has been discovered. In this paper, we establish an
extension of the latter correspondence for a fragment of substructural logic
with least and greatest fixed points. We describe the computational
interpretation of the resulting infinitary proof system as session-typed
processes, and provide an effectively decidable local criterion to recognize
mutually recursive processes corresponding to valid circular proofs as
introduced by Fortier and Santocanale. We show that our algorithm imposes a
stricter requirement than Fortier and Santocanale's guard condition, but is
local and compositional and therefore more suitable as the basis for a
programming language.
Counting Tree-Child Networks and Their Subclasses

Galled trees are studied as a recombination model in population genetics. This class of phylogenetic networks is generalized into tree-child, galled and reticulation-visible network classes by relaxing a structural condition imposed on galled trees. We count tree-child networks through enumerating their component graphs. Explicit counting formulas are also given for galled trees through their relationship to ordered trees, phylogenetic networks with few reticulations and phylogenetic networks in which the child of each reticulation is a leaf.
Policy Evaluation with Latent Confounders via Optimal Balance

Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of no unobserved confounders, which is bound to fail in practice. We study the question of policy evaluation when we instead have proxies for the latent confounders and develop an importance weighting method that avoids fitting a latent outcome regression model. We show that unlike the unconfounded case no single set of weights can give unbiased evaluation for all outcome models, yet we propose a new algorithm that can still provably guarantee consistency by instead minimizing an adversarial balance objective. We further develop tractable algorithms for optimizing this objective and demonstrate empirically the power of our method when confounders are latent.
Stochastic ordering of Gini indexes for multivariate elliptical random variables.

In this paper, we establish the stochastic ordering of the Gini indexes for multivariate elliptical risks which generalized the corresponding results for multivariate normal risks. It is shown that several conditions on dispersion matrices and the components of dispersion matrices of multivariate normal risks for the monotonicity of the Gini index in the usual stochastic order proposed by Samanthi, Wei and Brazauskas (2016) and Kim and Kim (2019) are also suitable for multivariate elliptical risks. We also study the tail probability of Gini index for multivariate elliptical risks and revised a large deviation result for the Gini indexes of multivariate normal risks in Kim and Kim (2019).
Dialog State Tracking: A Neural Reading Comprehension Approach

Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.
Probing the independence within the dark sector in the fluid approximation

The standard model of cosmology is based on two unknown dark components that are uncoupled from each other. In this paper we investigate whether there is evidence for an interaction between these components of cold dark matter (CDM) and dark energy (DE). In particular, we focus on a minimal extension and reconstruct the interaction history at low-redshifts non-parametrically using a variation of the commonly used principal component analysis. Although we focus on the interaction in the dark sector, any significant deviation from the standard model that changes the expansion history of the Universe, should leave imprints detectable by our analysis. Thus, detecting signatures of interaction could also be indicative of other non-standard phenomena even if they are not the results of the interaction. It is thus interesting to note that the results presented in this paper do not provide support for the interaction in the dark sector, although the uncertainty is still quite large. In so far as interaction is present but undetectable using current data, we show from a Fisher forecast that forthcoming LSST and DESI surveys will be able to constrain a DM-DE coupling at 20% precision—enough to falsify the non-interacting scenario, assuming the presence of a modest amount of interaction.
View N-Gram Network for 3D Object Retrieval

How to aggregate multi-view representations of a 3D object into an informative and discriminative one remains a key challenge for multi-view 3D object retrieval. Existing methods either use view-wise pooling strategies which neglect the spatial information across different views or employ recurrent neural networks which may face the efficiency problem. To address these issues, we propose an effective and efficient framework called View N-gram Network (VNN). Inspired by n-gram models in natural language processing, VNN divides the view sequence into a set of visual n-grams, which involve overlapping consecutive view sub-sequences. By doing so, spatial information across multiple views is captured, which helps to learn a discriminative global embedding for each 3D object. Experiments on 3D shape retrieval benchmarks, including ModelNet10, ModelNet40 and ShapeNetCore55 datasets, demonstrate the superiority of our proposed method.
Using the Makerspace to Create Educational Open-source Software for Electrical Circuits: A Learning Experience

Virtual learning environments are a useful modality for engaging students in the classroom by affording them a sense of presence and immersion. The motivation of this project was to create an open-source augmented reality electrical circuit application for use in lower division engineering courses to teach students about electricity fundamentals. Softwares that are readily available for use on virtual and augmented reality devices do not typically apply to all disciplines and do not necessarily have a pedagogical or accessibility focus. Considering this lack of appropriate educational applications for the current virtual and augmented reality devices, a team of interdisciplinary students was assembled to create such software. With extensive usability studies, the application was designed for quick adoption and improve accessibility by providing multimodal access such as voice assistant, gray scaling for depth perception and daltonize the app. The software is available as part of VITaL Laboratory, Sonoma State University.
Self-Balanced Dropout

Dropout is known as an effective way to reduce overfitting via preventing co-adaptations of units. In this paper, we theoretically prove that the co-adaptation problem still exists after using dropout due to the correlations among the inputs. Based on the proof, we further propose Self-Balanced Dropout, a novel dropout method which uses a trainable variable to balance the influence of the input correlation on parameter update. We evaluate Self-Balanced Dropout on a range of tasks with both simple and complex models. The experimental results show that the mechanism can effectively solve the co-adaption problem to some extent and significantly improve the performance on all tasks.
Word Embedding for Response-To-Text Assessment of Evidence

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.
Multi-view Deep Subspace Clustering Networks

Multi-view subspace clustering aims to discover the inherent structure by fusing multi-view complementary information. Most existing methods first extract multiple types of hand-crafted features and then learn a joint affinity matrix for clustering. The disadvantage lies in two aspects: 1) Multi-view relations are not embedded into feature learning. 2) The end-to-end learning manner of deep learning is not well used in multi-view clustering. To address the above issues, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end-to-end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices while Unet learns a common self-representation matrix for all views. To exploit the complementarity of multi-view representations, Hilbert Schmidt Independence Criterion (HSIC) is introduced as a diversity regularization, which can capture the non-linear and high-order inter-view relations. As different views share the same label space, the self-representation matrices of each view are aligned to the common one by a universality regularization. Experiments on both multi-feature and multi-modality learning validate the superiority of the proposed multi-view subspace clustering model.
On the Stochastic Processes on 7-Dimensional Spheres

We studied isometric stochastic flows of a Stratonovich stochastic differential equation on spheres, i.e. on the standard sphere and Gromoll-Meyer exotic sphere. The standard sphere $S^7_s$ can be constructed as the quotient manifold $\mathrm{Sp}(2, \mathbb{H})/S^3$ with the so-called ${\bullet}$-action of $S^3$, whereas the Gromoll-Meyer exotic sphere $\Sigma^7_{GM}$ as the quotient manifold $\mathrm{Sp}(2, \mathbb{H})/S^3$ with respect to the so-called ${\star}$-action of $S^3$. The Stratonovich stochastic differential equation which describes a continuous-time stochastic process on the standard sphere is constructed and studied. The corresponding continuous-time stochastic process and its properties on the Gromoll-Meyer exotic sphere can be obtained by constructing a homeomorphism $h: S^7_s\rightarrow \Sigma^7_{GM}$. The corresponding Fokker-Planck equation and entropy rate in the Stratonovich approach is also investigated.
eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubricbased essay scoring to trigger formative feedback messages regarding students’ use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.
Co-Attention Based Neural Network for Source-Dependent Essay Scoring

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.