nils-herrmann/clean_scirepeval huggingface dataset:
Adaptively stacking ensembles for influenza forecasting with incomplete data

Seasonal influenza infects between 10 and 50 million people in the United States every year, overburdening hospitals during weeks of peak incidence. Named by the CDC as an important tool to fight the damaging effects of these epidemics, accurate forecasts of influenza and influenza-like illness (ILI) forewarn public health officials about when, and where, seasonal influenza outbreaks will hit hardest. Multi-model ensemble forecasts---weighted combinations of component models---have shown positive results in forecasting. Ensemble forecasts of influenza outbreaks have been static, training on all past ILI data at the beginning of a season, generating a set of optimal weights for each model in the ensemble, and keeping the weights constant. We propose an adaptive ensemble forecast that (i) changes model weights week-by-week throughout the influenza season, (ii) only needs the current influenza season's data to make predictions, and (iii) by introducing a prior distribution, shrinks weights toward the reference equal weighting approach and adjusts for observed ILI percentages that are subject to future revisions. We investigate the prior's ability to impact adaptive ensemble performance and, after finding an optimal prior via a cross-validation approach, compare our adaptive ensemble's performance to equal-weighted and static ensembles. Applied to forecasts of short-term ILI incidence at the regional and national level in the US, our adaptive model outperforms a naive equal-weighted ensemble, and has similar or better performance to the static ensemble, which requires multiple years of training data. Adaptive ensembles are able to quickly train and forecast during epidemics, and provide a practical tool to public health officials looking for forecasts that can conform to unique features of a specific season.
Likelihood Contribution based Multi-scale Architecture for Generative Flows

Deep generative modeling using flows has gained popularity owing to the tractable exact log-likelihood estimation with efficient training and synthesis process. However, flow models suffer from the challenge of having high dimensional latent space, same in dimension as the input space. An effective solution to the above challenge as proposed by Dinh et al. (2016) is a multi-scale architecture, which is based on iterative early factorization of a part of the total dimensions at regular intervals. Prior works on generative flows involving a multi-scale architecture perform the dimension factorization based on a static masking. We propose a novel multi-scale architecture that performs data dependent factorization to decide which dimensions should pass through more flow layers. To facilitate the same, we introduce a heuristic based on the contribution of each dimension to the total log-likelihood which encodes the importance of the dimensions. Our proposed heuristic is readily obtained as part of the flow training process, enabling versatile implementation of our likelihood contribution based multi-scale architecture for generic flow models. We present such an implementation for the original flow introduced in Dinh et al. (2016), and demonstrate improvements in log-likelihood score and sampling quality on standard image benchmarks. We also conduct ablation studies to compare proposed method with other options for dimension factorization.
Characterising complex healthcare systems using network science: The small world of emergency surgery

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Nevertheless, metrics of performance have to date focused on the performance of individual elements rather than the system as a whole. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counter-intuitive outcomes and a holistic metric of hospital function might help design more efficient services. We aimed to characterise the system of peri-operative care for emergency surgical admissions in our tertiary care hospital using network analysis. We used retrospective electronic health record data to construct a weighted directional network of the system. For this we selected all unplanned admissions during a 3.5 year period involving a surgical intervention during the inpatient stay and obtained a set of 16,500 individual inpatient episodes. We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics. The analysis showed the service to be a complex system with scale-free, small-world network properties. This finding has implications for the structure and resilience of the service as such networks, whilst being robust in general, may be vulnerable to outages at specific key nodes. We also identified such potential hubs and bottlenecks in the system based on a variety of network measures. It is hoped that such a holistic, system-wide description of a hospital service may provide better metrics for hospital strain and serve to help planners engineer systems that are as robust as possible to external shocks.
Corrigibility with Utility Preservation

Corrigibility is a safety property for artificially intelligent agents. A corrigible agent will not resist attempts by authorized parties to alter the goals and constraints that were encoded in the agent when it was first started. This paper shows how to construct a safety layer that adds corrigibility to arbitrarily advanced utility maximizing agents, including possible future agents with Artificial General Intelligence (AGI). The layer counter-acts the emergent incentive of advanced agents to resist such alteration. A detailed model for agents which can reason about preserving their utility function is developed, and used to prove that the corrigibility layer works as intended in a large set of non-hostile universes. The corrigible agents have an emergent incentive to protect key elements of their corrigibility layer. However, hostile universes may contain forces strong enough to break safety features. Some open problems related to graceful degradation when an agent is successfully attacked are identified. The results in this paper were obtained by concurrently developing an AGI agent simulator, an agent model, and proofs. The simulator is available under an open source license. The paper contains simulation results which illustrate the safety related properties of corrigible AGI agents in detail.
Kudla-Rapoport cycles and derivatives of local densities

We prove the local Kudla--Rapoport conjecture, which is a precise identity between the arithmetic intersection numbers of special cycles on unitary Rapoport--Zink spaces and the derivatives of local representation densities of hermitian forms. As a first application, we prove the global Kudla--Rapoport conjecture, which relates the arithmetic intersection numbers of special cycles on unitary Shimura varieties and the central derivatives of the Fourier coefficients of incoherent Eisenstein series. Combining previous results of Liu and Garcia--Sankaran, we also prove cases of the arithmetic Siegel--Weil formula in any dimension.
A Triple AGN in a Mid-infrared Selected Late-stage Galaxy Merger

The coevolution of galaxies and the supermassive black holes (SMBHs) at their centers via hierarchical galaxy mergers is a key prediction of ΛCDM cosmology. As gas and dust are funneled to the SMBHs during the merger, the SMBHs light up as active galactic nuclei (AGNs). In some cases, a merger of two galaxies can encounter a third galaxy, leading to a triple merger, which would manifest as a triple AGN if all three SMBHs are simultaneously accreting. Using high spatial resolution X-ray, near-IR, and optical spectroscopic diagnostics, we report here a compelling case of an AGN triplet with mutual separations <10 kpc in the advanced merger SDSS J084905.51+111447.2 at z = 0.077. The system exhibits three nuclear X-ray sources, optical spectroscopic line ratios consistent with AGN in each nucleus, a high excitation near-IR coronal line in one nucleus, and broad Paα detections in two nuclei. Hard X-ray spectral fitting reveals a high column density along the line of sight, consistent with the picture of late-stage mergers hosting heavily absorbed AGNs. Our multiwavelength diagnostics support a triple AGN scenario, and we rule out alternative explanations such as star formation activity, shock-driven emission, and emission from fewer than three AGN. The dynamics of gravitationally bound triple SMBH systems can dramatically reduce binary SMBH inspiral timescales, providing a possible means to surmount the “Final Parsec Problem.” AGN triplets in advanced mergers are the only observational forerunner to bound triple SMBH systems and thus offer a glimpse of the accretion activity and environments of the AGNs prior to the gravitationally bound triple phase.
SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation

For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform. While Neural Architecture Search (NAS) has been effectively used to develop low-latency networks for image classification, there has been relatively little effort to use NAS to optimize DNN architectures for other vision tasks. In this work, we present what we believe to be the first proxyless hardware-aware search targeted for dense semantic segmentation. With this approach, we advance the state-of-the-art accuracy for latency-optimized networks on the Cityscapes semantic segmentation dataset. Our latency-optimized small SqueezeNAS network achieves 68.02% validation class mIOU with less than 35 ms inference times on the NVIDIA Xavier. Our latency-optimized large SqueezeNAS network achieves 73.62% class mIOU with less than 100 ms inference times. We demonstrate that significant performance gains are possible by utilizing NAS to find networks optimized for both the specific task and inference hardware. We also present detailed analysis comparing our networks to recent state-of-the-art architectures. The SqueezeNAS models are available for download here: https://github.com/ashaw596/squeezenas
Semiclassical eigenvalue asymptotics for the Bochner Laplacian of a positive line bundle on a symplectic manifold

We consider the Bochner Laplacian on high tensor powers of a positive line bundle on a closed symplectic manifold (or, equivalently, the semiclassical magnetic Schrodinger operator with the non-degenerate magnetic field). We assume that the operator has discrete wells. The main result of the paper states asymptotic expansions for its low-lying eigenvalues.
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems

A trojan backdoor is a hidden pattern typically implanted in a deep neural network. It could be activated and thus forces that infected model behaving abnormally only when an input data sample with a particular trigger present is fed to that model. As such, given a deep neural network model and clean input samples, it is very challenging to inspect and determine the existence of a trojan backdoor. Recently, researchers design and develop several pioneering solutions to address this acute problem. They demonstrate the proposed techniques have a great potential in trojan detection. However, we show that none of these existing techniques completely address the problem. On the one hand, they mostly work under an unrealistic assumption (e.g. assuming availability of the contaminated training database). On the other hand, the proposed techniques cannot accurately detect the existence of trojan backdoors, nor restore high-fidelity trojan backdoor images, especially when the triggers pertaining to the trojan vary in size, shape and position. In this work, we propose TABOR, a new trojan detection technique. Conceptually, it formalizes a trojan detection task as a non-convex optimization problem, and the detection of a trojan backdoor as the task of resolving the optimization through an objective function. Different from the existing technique also modeling trojan detection as an optimization problem, TABOR designs a new objective function--under the guidance of explainable AI techniques as well as heuristics--that could guide optimization to identify a trojan backdoor in a more effective fashion. In addition, TABOR defines a new metric to measure the quality of a trojan backdoor identified. Using an anomaly detection method, we show the new metric could better facilitate TABOR to identify intentionally injected triggers in an infected model and filter out false alarms......
Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks

The Philippines is a common ground to natural calamities like typhoons, floods, volcanic eruptions and earthquakes. With Twitter as one of the most used social media platform in the Philippines, a total of 39,867 preprocessed tweets were obtained given a time frame starting from November 1, 2013 to  2014. Sentiment analysis determines the underlying emotion given a series of words. The main purpose of this study is to identify the sentiments expressed in the tweets sent by the Filipino people before, during, and after Typhoon Yolanda using two variations of Recurrent Neural Networks; standard and bidirectional. The best generated models after training with various hyperparameters achieved a high accuracy of 81.79% for fine-grained classification using standard RNN and 87.69% for binary classification using bidirectional RNN. Findings revealed that 51.1% of the tweets sent were positive expressing support, love, and words of courage to the victims; 19.8% were negative stating sadness and despair for the loss of lives and hate for corrupt officials; while the other 29% were neutral tweets from local news stations, announcements of relief operations, donation drives, and observations by citizens.
Seeding the Singularity for A.I

The singularity refers to an idea that once a machine having an artificial intelligence surpassing the human intelligence capacity is created, it will trigger explosive technological and intelligence growth. I propose to test the hypothesis that machine intelligence capacity can grow autonomously starting with an intelligence comparable to that of bacteria - microbial intelligence. The goal will be to demonstrate that rapid growth in intelligence capacity can be realized at all in artificial computing systems. I propose the following three properties that may allow an artificial intelligence to exhibit a steady growth in its intelligence capacity: (i) learning with the ability to modify itself when exposed to more data, (ii) acquiring new functionalities (skills), and (iii) expanding or replicating itself. The algorithms must demonstrate a rapid growth in skills of dataprocessing and analysis and gain qualitatively different functionalities, at least until the current computing technology supports their scalable development. The existing algorithms that already encompass some of these or similar properties, as well as missing abilities that must yet be implemented, will be reviewed in this work. Future computational tests could support or oppose the hypothesis that artificial intelligence can potentially grow to the level of superintelligence which overcomes the limitations in hardware by producing necessary processing resources or by changing the physical realization of computation from using chip circuits to using quantum computing principles.
Exploring Neural Net Augmentation to BERT for Question Answering on SQUAD 2.0

Enhancing machine capabilities to answer questions has been a topic of considerable focus in recent years of NLP research. Language models like Embeddings from Language Models (ELMo)[1] and Bidirectional Encoder Representations from Transformers (BERT)  have been very successful in developing general purpose language models that can be optimized for a large number of downstream language tasks. In this work, we focused on augmenting the pre-trained BERT language model with different output neural net architectures and compared their performance on question answering task posed by the Stanford Question Answering Dataset 2.0 (SQUAD 2.0) . Additionally, we also fine-tuned the pre-trained BERT model parameters to demonstrate its effectiveness in adapting to specialized language tasks. Our best output network, is the contextualized CNN that performs on both the unanswerable and answerable question answering tasks with F1 scores of 75.32 and 64.85 respectively.
Probabilistic Permutation Invariant Training for Speech Separation

Single-microphone, speaker-independent speech separation is normally performed through two steps: (i) separating the specific speech sources, and (ii) determining the best output-label assignment to find the separation error. The second step is the main obstacle in training neural networks for speech separation. Recently proposed Permutation Invariant Training (PIT) addresses this problem by determining the output-label assignment which minimizes the separation error. In this study, we show that a major drawback of this technique is the overconfident choice of the output-label assignment, especially in the initial steps of training when the network generates unreliable outputs. To solve this problem, we propose Probabilistic PIT (Prob-PIT) which considers the output-label permutation as a discrete latent random variable with a uniform prior distribution. Prob-PIT defines a log-likelihood function based on the prior distributions and the separation errors of all permutations; it trains the speech separation networks by maximizing the log-likelihood function. Prob-PIT can be easily implemented by replacing the minimum function of PIT with a soft-minimum function. We evaluate our approach for speech separation on both TIMIT and CHiME datasets. The results show that the proposed method significantly outperforms PIT in terms of Signal to Distortion Ratio and Signal to Interference Ratio.
Gravitational Lensing Signatures of Axion Dark Matter Minihalos in Highly Magnified Stars

Axions are a viable candidate for cold dark matter, which should generically form minihalos of subplanetary masses from white-noise isocurvature density fluctuations if the Peccei–Quinn phase transition occurs after inflation. Despite being denser than the larger halos formed out of adiabatic fluctuations from inflation, axion minihalos have surface densities much smaller than the critical value required for gravitational lensing to produce multiple images or high magnification, and hence are practically undetectable as lenses in isolation. However, their lensing effect can be enhanced when superposed near critical curves of other lenses. We propose a method to detect them through photometric monitoring of recently discovered caustic transiting stars behind cluster lenses, under extreme magnification factors μ ≳ 103–104 as the lensed stars cross microlensing caustics induced by intracluster stars. For masses of the first gravitationally collapsed minihalos in the range ∼10−15–10−8 h−1 M⊙, we show that axion minihalos in galaxy clusters should collectively produce subtle surface density fluctuations of amplitude ∼10−4–10−3 on projected length scales of ∼10–104 au, which imprint irregularities on the microlensing lightcurves of caustic transiting stars. We estimate that, inside a cluster halo and over the age of the universe, most of these minihalos are likely to avoid dynamic disruption by encounters with stars or other minihalos.
Toward a robust inference method for the galaxy bispectrum: likelihood function and model selection

The forthcoming generation of galaxy redshift surveys will sample the large-scale structure of the Universe over unprecedented volumes with high-density tracers. This advancement will make robust measurements of three-point clustering statistics possible. In preparation for this improvement, we investigate how several methodological choices can influence inferences based on the bispectrum about galaxy bias and shot noise. We first measure the real-space bispectrum of dark-matter haloes extracted from 298 N-body simulations covering a volume of approximately 1000 Gpc3. We then fit a series of theoretical models based on tree-level perturbation theory to the numerical data. To achieve this, we estimate the covariance matrix of the measurement errors by using 10,000 mock catalogues generated with the PINOCCHIO code. We study how the model constraints are influenced by the binning strategy for the bispectrum configurations and by the form of the likelihood function. We also use Bayesian model-selection techniques to single out the optimal theoretical description of our data. We find that a three-parameter bias model combined with Poissonian shot noise is necessary to model the halo bispectrum up to scales of kmax≲ 0.08 Mpc-1, although fitting formulae that relate the bias parameters can be helpful to reduce the freedom of the model without compromising accuracy. Our data clearly disfavour local Eulerian and local Lagrangian bias models and do not require corrections to Poissonian shot noise. We anticipate that model-selection diagnostics will be particularly useful to extend the analysis to smaller scales as, in this case, the number of model parameters will grow significantly large.
Dusty Cloud Acceleration with Multiband Radiation

We perform two-dimensional and three-dimensional simulations of cold, dense clouds, which are accelerated by radiation pressure on dust relative to a hot, diffuse background gas. We examine the relative effectiveness of acceleration by ultraviolet (UV) and infrared (IR) radiation fields, both independently and acting simultaneously on the same cloud. We study clouds that are optically thin to IR emission but with varying UV optical depths. Consistent with previous work, we find relatively efficient acceleration and long cloud survival times when the IR band flux dominates over the UV flux. However, when the UV flux is dominant or even a modest percentage (∼5%–10%) of the IR irradiating flux, it can act to compress the cloud, first crushing it and then disrupting the outer layers. This drives mixing of the outer regions of the dusty gas with the hot diffuse background to the point where most dust is not likely to survive or stay coupled to the gas. Hence, the cold cloud is unable to survive for a long enough timescale to experience significant acceleration before disruption even though efficient IR cooling keeps the majority of the gas close to the radiative equilibrium temperature (T ≲ 100 K). We discuss the implications for observed systems, concluding that radiation pressure driving is most effective when the light from star-forming regions is efficiently reprocessed into the IR.
Hybrid Camera Pose Estimation with Online Partitioning

This paper presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to on-line monocular systems. Breaking through the limitations of fixed-size temporal partitioning in most conventional pose estimation mechanisms, the proposed approach significantly improves the accuracy of local bundle adjustment by gathering spatially-strongly-connected cameras into each block. With the dynamic initialization using intermediate computation values, our proposed self-adaptive Levenberg-Marquardt solver achieves a quadratic convergence rate to further enhance the efficiency of the local optimization. Moreover, the dense data association between blocks by virtue of our co-visibility-based partitioning enables us to explore and implement motion averaging to efficiently align the blocks globally, updating camera motion estimations on-the-fly. Experiment results on benchmarks convincingly demonstrate the practicality and robustness of our proposed approach by outperforming conventional bundle adjustment by orders of magnitude.