Miracle-dz/UniFAR-FTUs huggingface dataset:
A k-Hop Collaborate Game Model: Extended to Community Budgets and Adaptive Nonsubmodularity

Revenue maximization (RM) is one of the most important problems in social networks, which attempts to find a small subset of users that make the expected revenue maximized. It has been studied in depth before. However, most of the existing literature was based on nonadaptive seeding strategies and simple information diffusion models. It considered the number of influenced users as a measurement unit to quantify the revenue. Until the emergence of the collaborate game model, it considered the activity as a basic object to compute the revenue. An activity initiated by a user can only influence those users whose distances are within ${k}$ -hop from the initiator. Based on that, we adopt an adaptive seed strategy and formulate an RM under the size budget (RMSB) problem. If taking into account the product’s promotion, we extend it to an RM under the community budget problem, where the influence can be distributed over the whole network uniformly. We can prove that our objective function is adaptive monotone and not adaptive submodular, but it is adaptive submodular in some special cases. We study these two problems under both the special submodular cases and general nonsubmodular cases, and propose RMSBSolver and RMCBSolver to solve them with strong theoretical guarantees, respectively. In particular, we give a data-dependent approximation ratio by adaptive primal curvature for the RMSB in general nonsubmodular cases. Finally, we evaluate our proposed algorithms by conducting experiments on real datasets, and show the effectiveness and accuracy of our solutions.
Counterfactuals and Dependencies on Causal Teams: Expressive Power and Deduction Systems

We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. As an intermediate step towards the completeness, we axiomatize the languages over a generalized version of causal team semantics, which turns out to be interesting also in its own right.
Department of Botany and Plant Pathology

We are seeking for highly motivated individuals who value achievement, are proactive, creative, detailoriented, flexible and constructive team players. Candidates should have completed their Ph.D. degree on a relevant area in Biological Sciences within the last 2-3 years. Proven ability to present and publish research data and excellent English communication skills both oral and written are required.
A steady length function for Ricci flows

A fundamental step in the analysis of singularities of Ricci flow was the discovery by Perelman of a monotonic volume quantity which detected shrinking solitons in (arXiv:math/0211159). A similar quantity was found by Feldman, Ilmanen, and Ni in 2005 which detected expanding solitons. The current work introduces a modified length functional as a first step towards a steady soliton monotonicity formula. This length functional generates a distance function in the usual way which is shown to satisfy several differential inequalities which saturate precisely on manifolds satisfying a modification of the steady soliton equation.
Efficient Online Classification and Tracking on Resource-constrained IoT Devices

Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area, and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this article,* we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.
Sum-Product Networks: A Survey

A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability distributions. They are closely related to probabilistic graphical models, in particular to Bayesian networks with multiple context-specific independencies. Their main advantage is the possibility of building tractable models from data, i.e., models that can perform several inference tasks in time proportional to the number of edges in the graph. They are somewhat similar to neural networks and can address the same kinds of problems, such as image processing and natural language understanding. This paper offers a survey of SPNs, including their definition, the main algorithms for inference and learning from data, several applications, a brief review of software libraries, and a comparison with related models.
Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities

In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an ℓ1-regularized method which encourages sparsity of the coefficients of the Fourier transform of the recovered utilities. Our method works by accessing very few Nash equilibria and their noisy utilities. Under certain technical conditions, our method also recovers the exact structure of these utility functions, and thus, the exact structure of the game. Furthermore, our method only needs a logarithmic number of samples in terms of the number of players and runs in polynomial time. We follow the primal-dual witness framework to provide provable theoretical guarantees. A full version of this paper is accessible at: https://www.cs. purdue.edu/homes/abarik/abarik_nonpara_icassp_full. pdf
Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.
Properties of green valley galaxies in relation to their selection criteria

Abstract The distribution of galaxies has been studied to show the difference between the blue cloud and red sequence and to define the green valley region. However, there are still many open questions regarding the importance of the green valley for understanding the morphological transformation and evolution of galaxies, how galaxies change from late-type to early-type and the role of AGN in galaxy formation and evolution scenario. The work focused on studying in more details the properties of green valley galaxies by testing the six most used selection criteria, differences between them, and how they may affect the main results and conclusions. The main findings are that, by selecting the green valley galaxies using different criteria, we are selecting different types of galaxies in terms of their stellar masses, sSFR, SFR, spectroscopic classification and morphological properties, where the difference was more significant for colour criteria than for sSFR and SFR vs. M* criteria.
Fabrication of deep-sub-micrometer NbN/AlN/NbN epitaxial junctions on a Si-substrate

We have developed a novel fabrication process for ultra-small, full-epitaxial NbN Josephson junctions on a TiN buffered silicon (Si) substrate. We use an electron beam lithography for junction definition followed by a reactive-ion-etch (RIE). A chemical mechanical polishing process and an RIE by using CHF3 gas formed reliable electrical contacts for the junction electrodes. All junctions, with a junction size down to 0.27 μm in diameter, showed excellent current–voltage characteristics with a clear gap and small sub-gap leakage current. We have confirmed that the quality of the junctions was maintained after the removal of the SiO2 dielectric interlayer.
Learning Representations For Images With Hierarchical Labels

Image classification has been studied extensively but there has been limited work in the direction of using non-conventional, external guidance other than traditional image-label pairs to train such models. In this thesis we present a set of methods to leverage information about the semantic hierarchy induced by class labels. In the first part of the thesis, we inject label-hierarchy knowledge to an arbitrary classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions by using order-preserving embedding-based models, prevalent in natural language, and tailor them to the domain of computer vision to perform image classification. Although, contrasting in nature, both the CNN-classifiers injected with hierarchical information, and the embedding-based models outperform a hierarchy-agnostic model on the newly presented, real-world ETH Entomological Collection image dataset this https URL.
INTEGRAL
 constraints on primordial black holes and particle dark matter

The International Gamma-Ray Astrophysics Laboratory ($INTEGRAL$) satellite has yielded unprecedented measurements of the soft gamma-ray spectrum of our Galaxy. Here we use those measurements to set constraints on dark matter (DM) that decays or annihilates into photons with energies $E\ensuremath{\approx}0.02--2\text{ }\text{ }\mathrm{MeV}$. First, we revisit the constraints on particle DM that decays or annihilates to photon pairs. In particular, for decaying DM, we find that previous limits were overstated by roughly an order of magnitude. Our new, conservative analysis finds that the DM lifetime must satisfy $\ensuremath{\tau}\ensuremath{\gtrsim}5\ifmmode\times\else\texttimes\fi{}{10}^{26}\text{ }\mathrm{s}\ifmmode\times\else\texttimes\fi{}({m}_{\ensuremath{\chi}}/\mathrm{MeV}{)}^{\ensuremath{-}1}$ for DM masses ${m}_{\ensuremath{\chi}}=0.054--3.6\text{ }\text{ }\mathrm{MeV}$. For MeV-scale DM that annihilates into photons $INTEGRAL$ sets the strongest constraints to date. Second, we target ultralight primordial black holes (PBHs) through their Hawking radiation. This makes them appear as decaying DM with a photon spectrum peaking at $E\ensuremath{\approx}5.77/(8\ensuremath{\pi}G{M}_{\mathrm{PBH}})$, for a PBH of mass ${M}_{\mathrm{PBH}}$. We use the $INTEGRAL$ data to demonstrate that, at 95% C.L. , PBHs with masses less than $1.2\ifmmode\times\else\texttimes\fi{}{10}^{17}\text{ }\text{ }\mathrm{g}$ cannot comprise all of the DM, setting the tightest bound to date on ultralight PBHs.
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDR-to-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We then propose to learn three specialized CNNs to reverse these steps. By decomposing the problem into specific sub-tasks, we impose effective physical constraints to facilitate the training of individual sub-networks. Finally, we jointly fine-tune the entire model end-to-end to reduce error accumulation. With extensive quantitative and qualitative experiments on diverse image datasets, we demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
Consistent Multiple Sequence Decoding

Sequence decoding is one of the core components of most visual-lingual models. However, typical neural decoders when faced with decoding multiple, possibly correlated, sequences of tokens resort to simple independent decoding schemes. In this paper, we introduce a consistent multiple sequence decoding architecture, which is while relatively simple, is general and allows for consistent and simultaneous decoding of an arbitrary number of sequences. Our formulation utilizes a consistency fusion mechanism, implemented using message passing in a Graph Neural Network (GNN), to aggregate context from related decoders. This context is then utilized as a secondary input, in addition to previously generated output, to make a prediction at a given step of decoding. Self-attention, in the GNN, is used to modulate the fusion mechanism locally at each node and each step in the decoding process. We show the efficacy of our consistent multiple sequence decoder on the task of dense relational image captioning and illustrate state-of-the-art performance (+ 5.2% in mAP) on the task. More importantly, we illustrate that the decoded sentences, for the same regions, are more consistent (improvement of 9.5%), while across images and regions maintain diversity.
Implementation of the 3D Marchenko method

The Marchenko method can be applied on cross-sections of 3D data using a 2D algorithm, but only a full 3D implementation can properly retrieve all 3D effects present in the data. The 3D implementation of the iterative Marchenko method is in principle a straightforward extension of the 2D method, it only requires an additional surface integration dimension. We present a 3D implementation of the Marchenko method based on an earlier implementation of the 2D Marchenko method. In the discussed implementation, special attention is given to an efficient kernel implementation and limiting the amount of data to read in, by using data compression. The algorithm properly handles 3D events and this is illustrated in the intermediate results of individual iterations of the method. Due to model-time and data-size constraints, the aperture of the data is limited, which leads to finite aperture artefacts that cause a lower convergence rate. We demonstrate the 3D method by retrieving the Green's functions for two models and comparing these functions to reference solutions. The two models are a horizontally layered medium and the complex SEG/EAGE overthrust model. Using the Marchenko method, imaging is applied to both models to show that false images caused by internal multiples are attenuated. Our 3D implementation is fully opensource and is intended to be used in future studies.
Integrating reliability and resilience to support microgrid design

Quantifying the potential benefits of microgrids in the design phase can support the transition of passive distribution networks into microgrids. At current, reliability and resilience are the main drivers for this transition. Therefore, this paper presents a mathematical optimization model to support the retrofitting of distribution networks into microgrids integrating techno-economic, resilience and reliability objectives. Storage and distributed generation are optionally installed to complement renewable generation, enabling the microgrid to supply priority demands during stochastic islanding events with uncertain duration. For a comprehensive quantification and optimization of microgrid resilience and reliability, islanding due to external events is combined with a detailed model of internal faults. Minimizing the interruption costs yields optimal capacities and placements of distributed energy resources and new lines for reconfiguration. The proposed method produces microgrid designs with up to 95% reliability and resilience gain and moderate cost increase in two benchmark distribution networks using data from the US Department of Energy. The developed methodology is scalable to large networks owing to the tailored Column-and-Constraint-Generation approach.
An Exploratory Study of Writing and Revising Explicit Programming Strategies

Knowledge sharing plays a crucial role throughout all software application development activities. When programmers learn and share through media like Stack overflow, GitHub, Meetups, videos, discussion forums, wikis, and blogs, every developer benefits. However, there is one kind of knowledge that developers share far less often: strategic knowledge for how to approach programming problems (e.g., how to debug server-side Python errors, how to resolve a merge conflict, how to evaluate the stability of an API one is considering for adoption). In this paper, we investigate the feasibility of developers articulating and sharing their strategic knowledge, and the use of these strategies to support other developers in their problem-solving. We specifically investigate challenges that developers face in articulating strategies in a form in which other developers can use to increase their productivity. To observe this, we simulated a knowledge-sharing platform, asking experts to articulate one of their own strategies and then asked the second set of developers to try to use the strategies and provide feedback on the strategies to authors. During the study, we asked both strategy authors and users to reflect on the challenges they faced. In analyzing the strategies authors created, the use of the strategies, the feedback that users provided to authors, and the difficulties that authors faced addressing this feedback, we found that developers can share strategic knowledge, but authoring strategies require substantial feedback from diverse audiences to be helpful to programmers with varying prior knowledge. Our results also raise challenging questions about how future work should support searching and browsing for strategies that support varying prior knowledge.
Action Space Shaping in Deep Reinforcement Learning

Reinforcement learning (RL) has been successful in training agents in various learning environments, including video games. However, such work modifies and shrinks the action space from the game’s original. This is to avoid trying "pointless" actions and to ease the implementation. Currently, this is mostly done based on intuition, with little systematic research supporting the design decisions. In this work, we aim to gain insight on these action space modifications by conducting extensive experiments in video game environments. Our results show how domain-specific removal of actions and discretization of continuous actions can be crucial for successful learning. With these insights, we hope to ease the use of RL in new environments, by clarifying what action-spaces are easy to learn.
Neuronal Sequence Models for Bayesian Online Inference

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.