nils-herrmann/clean_scirepeval huggingface dataset:
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations

The popularity of Deep Learning for real-world applications is ever-growing. With the introduction of high performance hardware, applications are no longer limited to image recognition. With the introduction of more complex problems comes more and more complex solutions, and the increasing need for explainable AI. Deep Neural Networks for Video tasks are amongst the most complex models, with at least twice the parameters of their Image counterparts. However, explanations for these models are often ill-adapted to the video domain. The current work in explainability for video models is still overshadowed by Image techniques, while Video Deep Learning itself is quickly gaining on methods for still images. This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for video deep learning, and then noting the scarcity of research into explanations for these methods.
New regularity results and long time behavior of pathwise (stochastic) Hamilton-Jacobi equations

We present two new sharp regularity results (regularizing effect and propagation of regularity) for viscosity solutions of uniformly convex space homogeneous Hamilton-Jacobi equations. In turn, these estimates yield new intermittent stochastic regularization results for pathwise (stochastic) viscosity solutions of Hamilton-Jacobi equations with uniformly convex Hamiltonians and rough multiplicative time dependence. Finally, we use the intermittent estimates to study the long time behavior of the pathwise (stochastic) viscosity solutions of convex Hamilton-Jacobi equations.
Raiders of the Lost Art

Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.
A new concept of technology with systemic-purposeful perpsective: theory, examples and empirical application

Although definitions of technology exist to explain the patterns of technological innovations, there is no general definition that explain the role of technology for humans and other animal species in environment. The goal of this study is to suggest a new concept of technology with a systemic-purposeful perspective for technology analysis. Technology here is a complex system of artifact, made and_or used by living systems, that is composed of more than one entity or sub-system and a relationship that holds between each entity and at least one other entity in the system, selected considering practical, technical and_or economic characteristics to satisfy needs, achieve goals and_or solve problems of users for purposes of adaptation and_or survival in environment. Technology T changes current modes of cognition and action to enable makers and_or users to take advantage of important opportunities or to cope with consequential environmental threats. Technology, as a complex system, is formed by different elements given by incremental and radical innovations. Technological change generates the progress from a system T1 to T2, T3, etc. driven by changes of technological trajectories and technological paradigms. Several examples illustrate here these concepts and a simple model with a preliminary empirical analysis shows how to operationalize the suggested definition of technology. Overall, then, the role of adaptation (i.e. reproductive advantage) can be explained as a main driver of technology use for adopters to take advantage of important opportunities or to cope with environmental threats. This study begins the process of clarifying and generalizing, as far as possible, the concept of technology with a new perspective that it can lay a foundation for the development of more sophisticated concepts and theories to explain technological and economic change in environment.
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Induction

The task of bilingual dictionary induction (BDI) is commonly used for intrinsic evaluation of cross-lingual word embeddings. The largest dataset for BDI was generated automatically, so its quality is dubious. We study the composition and quality of the test sets for five diverse languages from this dataset, with concerning findings: (1) a quarter of the data consists of proper nouns, which can be hardly indicative of BDI performance, and (2) there are pervasive gaps in the gold-standard targets. These issues appear to affect the ranking between cross-lingual embedding systems on individual languages, and the overall degree to which the systems differ in performance. With proper nouns removed from the data, the margin between the top two systems included in the study grows from 3.4% to 17.2%. Manual verification of the predictions, on the other hand, reveals that gaps in the gold standard targets artificially inflate the margin between the two systems on English to Bulgarian BDI from 0.1% to 6.7%. We thus suggest that future research either avoids drawing conclusions from quantitative results on this BDI dataset, or accompanies such evaluation with rigorous error analysis.
The role of the chemical potential in coupling superfluid dark matter to baryons

Superfluid dark matter postulates that the centers of galaxies contain superfluid condensates. An important quantity regarding these superfluids is their chemical potential μ. Here, we discuss two issues related to this chemical potential. First, there is no exactly conserved quantity associated with this chemical potential due to the symmetry-breaking baryon-phonon coupling. Second, μ is sometimes introduced by shifting the phonon field by ;μ ⋅ t which—again due to the symmetry-breaking baryon-phonon coupling—introduces an explicit time dependence in the Lagrangian. We investigate under which conditions introducing a chemical potential is nevertheless justified and show how to correctly introduce it when these conditions are met. We further propose a model that recovers superfluid dark matter's zero-temperature equations of motion including a chemical potential even if the aforementioned conditions for justifying a chemical potential are not met.
Scenior: An Immersive Visual Scripting system of Gamified Training based on VR Software Design Patterns

Virtual reality (VR) has re-emerged as a low-cost, highly accessible consumer product, and training on simulators is rapidly becoming standard in many industrial sectors. Combined with the continued advancements in VR technology, the interest in platforms that generate immersive experiences has increased. However, the available systems are either focusing on gaming context, featuring limited capabilities (embedded editors in game engines) or they support only content creation of virtual environments without any rapid prototyping and modification. In this project we propose Scenior, an innovative coding-free, visual scripting platform to replicate gamified training scenarios through Rapid Prototyping via newly defined VR software design patterns. We implemented and compared three authoring tools: a) Prototyped scripting, b) Visual Scripting and c) VR Editor for rapid reconstruction of VR training scenarios. Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment. We also introduce Action Prototypes, a new software design pattern suitable to replicate behavioural tasks for VR experiences. In a addition, we present the scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum.
InstructableCrowd: Creating IF-THEN Rules for Smartphones via Conversations with the Crowd

Natural language interfaces have become a common part of modern digital life. Chatbots utilize text-based conversations to communicate with users; personal assistants on smartphones such as Google Assistant take direct speech commands from their users; and speech-controlled devices such as Amazon Echo use voice as their only input mode. In this paper, we introduce InstructableCrowd, a crowd-powered system that allows users to program their devices via conversation. The user verbally expresses a problem to the system, in which a group of crowd workers collectively respond and program relevant multi-part IF-THEN rules to help the user. The IF-THEN rules generated by InstructableCrowd connect relevant sensor combinations (e.g., location, weather, device acceleration, etc.) to useful effectors (e.g., text messages, device alarms, etc.). Our study showed that non-programmers can use the conversational interface of InstructableCrowd to create IF-THEN rules that have similar quality compared with the rules created manually. InstructableCrowd generally illustrates how users may converse with their devices, not only to trigger simple voice commands, but also to personalize their increasingly powerful and complicated devices.
Social success of perfumes

We study data on perfumes and their odour descriptors—notes—to understand how note compositions, called accords, influence successful fragrance formulas. We obtain accords which tend to be present in perfumes that receive significantly more customer ratings. Our findings show that the most popular notes and the most over-represented accords are different to those that have the strongest effect to the perfume ratings. We also used network centrality to understand which notes have the highest potential to enhance note compositions. We find that large degree notes, such as musk and vanilla as well as generically-named notes, e.g. floral notes, are amongst the notes that enhance accords the most. This work presents a framework which would be a timely tool for perfumers to explore a multidimensional space of scent compositions.
Life Between the City and the Village: Comparative Analysis of Service Access in Indian Urban Slums

The emergence of India as an urbanized nation is one of the most significant socioeconomic and political processes of the 21st century. An essential feature of India’s urbanization has been the growth and persistence of informal settlements (slums) in its fast-developing cities. Whether living conditions in Indian urban slums constitute a path to human development or a poverty trap is therefore an issue of vital importance. Here, we characterize census data using the framework of urban scaling to systematically characterize the relative properties of Indian urban slums, focusing on attributes of neighbourhoods such as access to basic services like water, sanitation, and electrical power. We find that slums in larger cities offer systematically higher levels of service access than those in smaller cities. Perhaps as expected, we also find consistent underperformance in service access in slums in comparison with non-slum neighbourhoods in the same cities. However, urban slums, on average, offer greater access to services than neighbourhoods in rural areas. This situation, which we quantify systematically, may help explain why Indian larger cities have remained attractive to rural populations in terms of living standards, beyond the need for an economic income premium.
CvxNet: Learnable Convex Decomposition

Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental in computer graphics, where it provides one of the most common ways to approximate geometry, for example, in real-time physics simulation. A convex object also has the property of being simultaneously an explicit and implicit representation: one can interpret it explicitly as a mesh derived by computing the vertices of a convex hull, or implicitly as the collection of half-space constraints or support functions. Their implicit representation makes them particularly well suited for neural network training, as they abstract away from the topology of the geometry they need to represent. However, at testing time, convexes can also generate explicit representations – polygonal meshes – which can then be used in any downstream application. We introduce a network architecture to represent a low dimensional family of convexes. This family is automatically derived via an auto-encoding process. We investigate the applications of this architecture including automatic convex decomposition, image to 3D reconstruction, and part-based shape retrieval.
Hybrid Nano Communications

In this paper, we propose a hybrid nano communication (HNC) system that integrates three possible modes of nano communications: terahertz communication (TC), molecular communication (MC), and neural communication (NC). The paper provides some insights into how the required four key building blocks, namely, terahertz to molecular (T2M) relay interface, molecular to terahertz (M2T) interface, molecular to neural (M2N) relay, and neural to molecular (N2M) interface can be designed to implement the HNC system. In addition, the capacity analysis for the hybrid channel is provided. Sub-channels’ capacities are also calculated. Finally, numerical results are presented to comprehend the data transmission capacity of the proposed HNC system.
Pricing Reliability Options Under Different Electricity Prices’ Regimes

Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first derived by means of closed-form pricing formulae, which are obtained under several assumptions about the dynamics of electricity prices and strike prices. Then, the value of the Reliability Option is simulated under a real-market calibration, using data of the Italian power market. We finally perform sensitivity analyses to highlight the impact of the level and volatility of both power and strike price, of the mean reversion speeds and of the correlation coefficient on the Reliability Options' value.
PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI

Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of concurrent learning-based design of data acquisition and image reconstruction schemes. In this work, we propose a novel approach to the learning of optimal schemes for conjoint acquisition and reconstruction of MRI scans, with the optimization carried out simultaneously with respect to the time-efficiency of data acquisition and the quality of resulting reconstructions. To be of a practical value, the schemes are encoded in the form of general k-space trajectories, whose associated magnetic gradients are constrained to obey a set of predefined hardware requirements (as defined in terms of, e.g., peak currents and maximum slew rates of magnetic gradients). With this proviso in mind, we propose a novel algorithm for the end-to-end training of a combined acquisition-reconstruction pipeline using a deep neural network with differentiable forward- and back-propagation operators. We also demonstrate the effectiveness of the proposed solution in application to both image reconstruction and image segmentation, reporting substantial improvements in terms of acceleration factors as well as the quality of these end tasks.
Stationary Distributions of Continuous-Time Markov Chains: A Review of Theory and Truncation-Based Approximations

Computing the stationary distributions of a continuous-time Markov chain (CTMC) involves solving a set of linear equations. In most cases of interest, the number of equations is infinite or too large, and the equations cannot be solved analytically or numerically. Several approximation schemes overcome this issue by truncating the state space to a manageable size. In this review, we first give a comprehensive theoretical account of the stationary distributions and their relation to the long-term behaviour of CTMCs that is readily accessible to non-experts and free of irreducibility assumptions made in standard texts. We then review truncation-based approximation schemes for CTMCs with infinite state spaces paying particular attention to the schemes' convergence and the errors they introduce, and we illustrate their performance with an example of a stochastic reaction network of relevance in biology and chemistry. We conclude by discussing computational trade-offs associated with error control and several open questions.
Impact of charge trapping on the energy resolution of Ge detectors for rare-event physics searches

Charge trapping degrades the energy resolution of germanium (Ge) detectors, which require to have increased experimental sensitivity in searching for dark matter and neutrinoless double-beta decay. We investigate the charge trapping processes utilizing nine planar detectors fabricated from USD-grown crystals with well-known net impurity levels. The charge collection efficiency as a function of charge trapping length is derived from the Shockley-Ramo theorem. Furthermore, we develop a model that correlates the energy resolution with the charge collection efficiency. This model is then applied to the experimental data. As a result, charge collection efficiency and charge trapping length are determined accordingly. Utilizing the Lax model (further developed by CDMS collaborators), the absolute impurity levels are determined for nine detectors. The knowledge of these parameters when combined with other traits such as the Fano factor serve as a reliable indicator of the intrinsic nature of charge trapping within the crystals. We demonstrate that electron trapping is more severe than hole trapping in a p-type detector and the charge collection efficiency depends on the absolute impurity level of the Ge crystal when an adequate bias voltage is applied to the detector. Negligible charge trapping is found when the absolute impurity level is less than 1.0$\times$10$^{11}/$cm$^{3}$ for collecting electrons and 2.0$\times$10$^{11}/$cm$^{3}$ for collecting holes.
Adaptive voter model on simplicial complexes.

Collective decision making processes lie at the heart of many social, political, and economic challenges. The classical voter model is a well-established conceptual model to study such processes. In this work, we define a form of adaptive (or coevolutionary) voter model posed on a simplicial complex, i.e., on a certain class of hypernetworks or hypergraphs. We use the persuasion rule along edges of the classical voter model and the recently studied rewiring rule of edges towards like-minded nodes, and introduce a peer-pressure rule applied to three nodes connected via a 2-simplex. This simplicial adaptive voter model is studied via numerical simulation. We show that adding the effect of peer pressure to an adaptive voter model leaves its fragmentation transition, i.e., the transition upon varying the rewiring rate from a single majority state into a fragmented state of two different opinion subgraphs, intact. Yet, above and below the fragmentation transition, we observe that the peer pressure has substantial quantitative effects. It accelerates the transition to a single-opinion state below the transition and also speeds up the system dynamics towards fragmentation above the transition. Furthermore, we quantify that there is a multiscale hierarchy in the model leading to the depletion of 2-simplices, before the depletion of active edges. This leads to the conjecture that many other dynamic network models on simplicial complexes may show a similar behavior with respect to the sequential evolution of simplices of different dimensions.
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation

Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor scalability of sampling-based planning optimizers, one significant limitation of these approaches is the ability to plan over long horizons to reach distant goals. To that end, we propose a framework for subgoal generation and planning, hierarchical visual foresight (HVF), which generates subgoal images conditioned on a goal image, and uses them for planning. The subgoal images are directly optimized to decompose the task into easy to plan segments, and as a result, we observe that the method naturally identifies semantically meaningful states as subgoals. Across three out of four simulated vision-based manipulation tasks, we find that our method achieves nearly a 200% performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes. Project page: this https URL
Compact 3 DOF Driving Simulator using Immersive Virtual Reality

A driving simulator was created using commercially available 3 degree of freedom motion platform (DOFreality H3) and a virtual reality head-mounted display (Oculus CV1). Using virtual reality headset as the visual simulation system with low-cost moving base platform allowed us to create a high-fidelity driving simulator with minimal cost and space. A custom motion cueing algorithm was used to minimize visuo-vestibular conflict, and simulator sickness questionnaire (SSQ) was used to measure progression of simulator sickness over time while driving on a highway environment.
CTRL: A Conditional Transformer Language Model for Controllable Generation

Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. We have released multiple full-sized, pretrained versions of CTRL at this https URL.