Miracle-dz/UniFAR-FTUs huggingface dataset:
Control Prefixes for Text Generation

Prompt learning methods adapt pre-trained language models to downstream applications by using a task-speciﬁc prompt together with the input. Most of the current work on prompt learning in text generation relies on a shared dataset-level prompt for all examples in the dataset. We extend this approach and propose a dynamic method, C ONTROL P REFIXES , which allows for the inclusion of conditional input-dependent information in each prompt. C ONTROL P REFIXES is at the intersection of prompt learning and controlled generation, em-powering the model to have ﬁner-grained control during text generation. The method in-corporates attribute-level learnable representations into different layers of a pre-trained transformer, allowing for the generated text to be guided in a particular direction. We provide a systematic evaluation of the technique and apply it to ﬁve datasets from the GEM benchmark for natural language generation (NLG). We present state-of-the-art results on several data-to-text datasets, including WebNLG.
BNAS v2: Learning Architectures for Binary Networks with Empirical Improvements

Backbone architectures of most binary networks are well-known floating point (FP) architectures such as the ResNet family. Questioning that the architectures designed for FP networks might not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective. Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder. The novel search objective diversifies early search to learn better performing binary architectures. We show that our method searches architectures with stable training curves despite the quantization error inherent in binary networks. Quantitative analyses demonstrate that our searched architectures outperform the architectures used in state-of-the-art binary networks and outperform or perform on par with state-of-the-art binary networks that employ various techniques other than architectural changes. In addition, we further propose improvements to the training scheme of our searched architectures. With the new training scheme for our searched architectures, we achieve the state-of-the-art performance by binary networks by outperforming all previous methods by non-trivial margins.
The formation of massive stellar clusters in converging galactic flows with photoionisation

We have performed simulations of cluster formation along two regions of a spiral arm taken from a global Milky Way simulation, including photoionising feedback. One region is characterised by strongly converging flows, the other represents a more typical spiral arm region. We find that more massive clusters are able to form on shorter timescales for the region with strongly converging flows. Mergers between clusters are frequent in the case of the strongly converging flows and enable the formation of massive clusters. We compare equivalent clusters formed in simulations with and without ionisation. Photoionisation does not prevent massive cluster formation, but can be seen to limit the masses of the clusters. On average the mass is reduced by around 20%, but we see a large spread from ionisation having minimal difference to leading to a 50% reduction in mass. Photoionisation is also able to clear out the gas in the vicinity of the clusters on Myr timescales, which can produce clusters with larger radii that are surrounded by more massive stellar halos. We find that the ionising feedback has more impact in our second region which is less dense and has less strongly converging flows.
Impact of COVID-19 Policies and Misinformation on Social Unrest

The novel coronavirus disease (COVID-19) pandemic has impacted every corner of earth, disrupting governments and leading to socioeconomic instability. This crisis has prompted questions surrounding how different sectors of society interact and influence each other during times of change and stress. Given the unprecedented economic and societal impacts of this pandemic, many new data sources have become available, allowing us to quantitatively explore these associations. Understanding these relationships can help us better prepare for future disasters and mitigate the impacts. Here, we focus on the interplay between social unrest (protests), health outcomes, public health orders, and misinformation in eight countries of Western Europe and four regions of the United States. We created 1-3 week forecasts of both a binary protest metric for identifying times of high protest activity and the overall protest counts over time. We found that for all regions, except Belgium, at least one feature from our various data streams was predictive of protests. However, the accuracy of the protest forecasts varied by country, that is, for roughly half of the countries analyzed, our forecasts outperform a naïve model. These mixed results demonstrate the potential of diverse data streams to predict a topic as volatile as protests as well as the difficulties of predicting a situation that is as rapidly evolving as a pandemic.
Online Facility Location with Predictions

We provide nearly optimal algorithms for online facility location (OFL) with predictions. In OFL, $n$ demand points arrive in order and the algorithm must irrevocably assign each demand point to an open facility upon its arrival. The objective is to minimize the total connection costs from demand points to assigned facilities plus the facility opening cost. We further assume the algorithm is additionally given for each demand point $x_i$ a natural prediction $f_{x_i}^{\mathrm{pred}}$ which is supposed to be the facility $f_{x_i}^{\mathrm{opt}}$ that serves $x_i$ in the offline optimal solution. Our main result is an $O(\min\{\log {\frac{n\eta_\infty}{\mathrm{OPT}}}, \log{n} \})$-competitive algorithm where $\eta_\infty$ is the maximum prediction error (i.e., the distance between $f_{x_i}^{\mathrm{pred}}$ and $f_{x_i}^{\mathrm{opt}}$). Our algorithm overcomes the fundamental $\Omega(\frac{\log n}{\log \log n})$ lower bound of OFL (without predictions) when $\eta_\infty$ is small, and it still maintains $O(\log n)$ ratio even when $\eta_\infty$ is unbounded. Furthermore, our theoretical analysis is supported by empirical evaluations for the tradeoffs between $\eta_\infty$ and the competitive ratio on various real datasets of different types.
A new type of discrete conformal structures on surfaces with boundary

We introduce a new type of discrete conformal structures on surfaces with boundary, which have nice interpolations in 3-dimensional hyperbolic geometry. Then we prove the global rigidity of the new discrete conformal structures on surfaces with boundary using variational principles, which is dual to Guo-Luo’s rigidity of the discrete conformal structures on surfaces with boundary they introduced in [13]. As a result, some new convexities of the volume functions of some generalized hyperbolic tetrahedra are obtained. Motivated by Chow-Luo’s combinatorial Ricci flow and Luo’s combinatorial Yamabe flow on closed surfaces, we further introduce combinatorial Ricci flow and combinatorial Calabi flows to deform the new discrete conformal structures on surfaces with boundary. The basic properties of the combinatorial curvature flows are established. These combinatorial curvature flows provide effective algorithms for constructing hyperbolic metrics on surfaces with totally geodesic boundary components of prescribed lengths. MSC (2020): 52C26
Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Deep Learning

In this study, a dataset of X-ray images from patients with common viral pneumonia, bacterial pneumonia, confirmed Covid-19 disease was utilized for the automatic detection of the Coronavirus disease. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease,6000 confirmed common bacterial pneumonia and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biological markers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.
A portfolio approach to massively parallel Bayesian optimization

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.
Minor Embedding in Broken Chimera and Pegasus Graphs is NP-complete

The embedding is an essential step when calculating on the D-Wave machine. In this work we show the hardness of the embedding problem for both types of existing hardware, represented by the Chimera and the Pegasus graphs, containing unavailable qubits. We construct certain broken Chimera graphs, where it is hard to find a Hamiltonian cycle. As the Hamiltonian cycle problem is a special case of the embedding problem, this proves the general complexity result for the Chimera graphs. By exploiting the subgraph relation between the Chimera and the Pegasus graphs, the proof is then further extended to the Pegasus graphs.
A unified framework for walking and running of bipedal robots

In this paper, we propose a novel framework capable of generating various walking and running gaits for bipedal robots. The main goal is to relax the fixed center of mass (CoM) height assumption of the linear inverted pendulum model (LIPM) and generate a wider range of walking and running motions, without a considerable increase in complexity. To do so, we use the concept of virtual constraints in the centroidal space which enables generating motions beyond walking while keeping the complexity at a minimum. By a proper choice of these virtual constraints, we show that we can generate different types of walking and running motions. More importantly, enforcing the virtual constraints through feedback renders the dynamics linear and enables us to design a feedback control mechanism which adapts the next step location and timing in face of disturbances, through a simple quadratic program (QP). To show the effectiveness of this framework, we showcase different walking and running simulations of the biped robot Bolt in the presence of both environmental uncertainties and external disturbances.
Noncommutative London Theory of Type-I Superconductors

We incorporate the Seiberg-Witten map of noncommutative theory to generalize the London penetration length in type-I superconductors when an external magnetic field is applied. After defining the noncommutative Maxwell potentials, we derive the London equation for the supercurrent as a function of the noncommutative parameter θ, up to second order in gauge fields. Using the standard approach for the flux quantization in the London theory, the effects of noncommutativity are analyzed and the corresponding deformed Maxwell and London equations are discussed. We show that the flux quantization remains consistent relative to the commutative case. Our effective London penetration length and magnetic permeability reduce to the standard ones in the commutative limit. Our results allow us to argue that the noncommutative effects are twofold: related with the density of Cooper electrons and his mechanism of paring at a scale defined by θ-tensor; and by the noncommutative space-time structure on which the gauge fields are defined.
Sample size calculations for n-of-1 trials

N-of-1 trials, single participant trials in which multiple treatments are sequentially randomized over the study period, can give direct estimates of individual-specific treatment effects. Combining nof-1 trials gives extra information for estimating the population average treatment effect compared with randomized controlled trials and increases precision for individual-specific treatment effect estimates. In this paper, we present a procedure for designing n-of-1 trials. We formally define the design components for determining the sample size of a series of n-of-1 trials, present models for analyzing these trials and use them to derive the sample size formula for estimating the population average treatment effect and the standard error of the individual-specific treatment effect estimates. We recommend first finding the possible designs that will satisfy the power requirement for estimating the population average treatment effect and then, if of interest, finalizing the design to also satisfy the standard error requirements for the individual-specific treatment effect estimates. The procedure is implemented and illustrated in the paper and through a Shiny app.
Annihilation of Magnetic Islands at the Top of Solar Flare Loops

The dynamics of magnetic reconnection in the solar current sheet (CS) is studied by high-resolution 2.5-dimensional MHD simulation. With the commencing of magnetic reconnection, a number of magnetic islands are formed intermittently and move quickly upward and downward along the CS. Upon collision with the semi-closed flux of the flare loops, the downflow islands cause a second reconnection with a rate comparable with that in the main CS. Though the time-integrated magnetic energy release is still dominated by the reconnection in the main CS, the second reconnection can release substantial magnetic energy, annihilating the main islands and generating secondary islands with various scales at the flare loop top. The distribution function of the flux of the secondary islands is found to follow a power law varying from fψ∼ψ−1 (small scale) to ψ −2 (large scale), which seems to be independent to background plasma β and thermal conduction (TC). However, the spatial scale and the strength of the termination shocks driven by the main reconnection outflows or islands decrease if β increases or if TC is included. We suggest that the annihilation of magnetic islands at the flare loop top, which is not included in the standard flare model, plays a nonnegligible role in releasing magnetic energy to heat flare plasma and accelerate particles.
How Well Do You Know Your Audience? Reader-aware Question Generation

When writing, a person may need to anticipate questions from their readers, but different types of readers may ask very different types of questions. If someone is writing for advice about a problem, what question will a domain expert ask, and is this different from how a novice might react? In this paper, we address the task of reader-aware question generation. We collect a new data set of questions and posts from social media, augmented with background information about the post readers. Based on predictive analysis and descriptive differences, we ﬁnd that different readers, such as experts and novices, consistently ask different types of questions. We next develop several text generation models that incorporate different types of reader background, including discrete and continuous reader representations based on the readers’ prior behavior. We demonstrate that reader-aware models can perform on par or slightly better than the text-only model in some cases, particularly in cases where a post attracts very different questions from readers of different groups. Our work has the potential to help writers anticipate the information needs of different readers.
Toward Uncensorable, Anonymous and Private Access Over Satoshi Blockchains

Abstract Providing unrestricted access to sensitive content such as news and software is difficult in the presence of adaptive and resourceful surveillance and censoring adversaries. In this paper we leverage the distributed and resilient nature of commercial Satoshi blockchains to develop the first provably secure, censorship resistant, cost-efficient storage system with anonymous and private access, built on top of commercial cryptocurrency transactions. We introduce max-rate transactions, a practical construct to persist data of arbitrary size entirely in a Satoshi blockchain. We leverage max-rate transactions to develop UWeb, a blockchain-based storage system that charges publishers to self-sustain its decentralized infrastructure. UWeb organizes blockchain-stored content for easy retrieval, and enables clients to store and access content with provable anonymity, privacy and censorship resistance properties. We present results from UWeb experiments with writing 268.21 MB of data into the live Litecoin blockchain, including 4.5 months of live-feed BBC articles, and 41 censorship resistant tools. The max-rate writing throughput (183 KB/s) and blockchain utilization (88%) exceed those of state-of-the-art solutions by 2-3 orders of magnitude and broke Litecoin’s record of the daily average block size. Our simulations with up to 3,000 concurrent UWeb writers confirm that UWeb does not impact the confirmation delays of financial transactions.
Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails

As student failure rates continue to increase in higher education, predicting student performance in the following semester has become a significant demand. Personalized student performance prediction helps educators gain a comprehensive view of student status and effectively intervene in advance. However, existing works scarcely consider the explainability of student performance prediction, which educators are most concerned about. In this paper, we propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA) by utilizing relationships in student profiles and prior knowledge of related courses. The designed Bidirectional Long Short-Term Memory (BiLSTM) architecture extracts the semantic information in the paths with specific patterns. As for leveraging similar paths' internal relations, a local and global-level attention mechanism is proposed to distinguish the influence of different students or courses for making predictions. Hence, valid reasoning on paths can be applied to predict the performance of students. The ESPA consistently outperforms the other state-of-the-art models for student performance prediction, and the results are intuitively explainable. This work can help educators better understand the different impacts of behavior on students' studies.
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.
MDP Abstraction with Successor Features

: Abstraction plays an important role for generalisation of knowledge and skills, and is key to sample efﬁcient learning and planning. For many complex problems an abstract plan can be formed ﬁrst, which is then instantiated by ﬁlling in the necessary low-level details. Often, such abstract plans generalize well to related new problems. We study abstraction in the context of reinforcement learning, in which agents may perform state or temporal abstractions. Temporal abstractions aka options represent temporally-extended actions in the form of option policies. However, typically acquired option policies cannot be directly transferred to new environments due to changes in the state space or transition dynamics. Furthermore, many existing state abstraction schemes ignore the correlation between state and temporal abstraction. In this work, we propose successor abstraction, a novel abstraction scheme building on successor features. This includes an algorithm for encoding and instantiation of abstract options across different environments, and a state abstraction mechanism based on the abstract options. Our successor abstraction allows us to learn abstract environment models with semantics that are transferable across different environments through encoding and instantiation of abstract options. Empirically, we achieve better transfer and improved performance on a set of benchmark tasks as compared to relevant state of the art baselines.