Miracle-dz/UniFAR-FTUs huggingface dataset:
A dynamic analytic method for risk-aware controlled martingale problems

We present a new, tractable method for solving and analyzing risk-aware control problems over finite and infinite, discounted time-horizons where the dynamics of the controlled process are described as a martingale problem. Supposing general Polish state and action spaces, and using generalized, relaxed controls, we state a risk-aware dynamic optimal control problem of minimizing risk of costs described by a generic risk function. We then construct an alternative formulation that takes the form of a nonlinear programming problem, constrained by the dynamic, {i.e.} time-dependent, and linear Kolmogorov forward equation describing the distribution of the state and accumulated costs. We show that the formulations are equivalent, and that the optimal control process can be taken to be Markov in the controlled process state, running costs, and time. We further prove that under additional conditions, the optimal value is attained. An example numeric problem is presented and solved.
An Opportunistic Bandit Approach for User Interface Experimentation

Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. User experience is playing an essential role in this competition, and retailers are continuously experimenting and optimizing their user interface for better user experience. The cost of experimentation is dominated by the opportunity cost of providing a suboptimal service to the customers. Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data. In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features. We achieve significant regret reduction by mitigating costly exploration and providing extra contextual information that helps to guide the testing process. Moreover, we analyze the advantages and challenges of using opportunistic bandits for online retail experimentation.
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy

Word translation is an integral part of language translation. In machine translation, each language is considered a domain with its own word embedding. The alignment between word embeddings allows linking semantically equivalent words in multilingual contexts. Moreover, it offers a way to infer cross-lingual meaning for words without a direct translation. Current methods for word embedding alignment are either supervised, i.e. they require known word pairs, or learn a cross-domain transformation on fixed embeddings in an unsupervised way. Here we propose an end-to-end approach for word embedding alignment that does not require known word pairs. Our method, termed Word Alignment through MMD (WAM), learns embeddings that are aligned during sentence translation training using a localized Maximum Mean Discrepancy (MMD) constraint between the embeddings. We show that our method not only out-performs unsupervised methods, but also supervised methods that train on known word translations.
Improving Query Safety at Pinterest

Query recommendations in search engines is a double edged sword, with undeniable benefits but potential of harm. Identifying unsafe queries is necessary to protect users from inappropriate query suggestions. However, identifying these is non-trivial because of the linguistic diversity resulting from large vocabularies, social-group-specific slang and typos, and because the inappropriateness of a term depends on the context. Here we formulate the problem as query-set expansion, where we are given a small and potentially biased seed set and the aim is to identify a diverse set of semantically related queries. We present PinSets, a system for query-set expansion, which applies a simple yet powerful mechanism to search user sessions, expanding a tiny seed set into thousands of related queries at nearly perfect precision, deep into the tail, along with explanations that are easy to interpret. PinSets owes its high quality expansion to using a hybrid of textual and behavioral techniques (i.e., treating queries both as compositional and as black boxes). Experiments show that, for the domain of drugs-related queries, PinSets expands 20 seed queries into 15,670 positive training examples at over 99\% precision. The generated expansions have diverse vocabulary and correctly handles words with ambiguous safety. PinSets decreased unsafe query suggestions at Pinterest by 90\%.
Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

A geometric nonlinear observer algorithm for Simultaneous Localization and Mapping (SLAM) developed on the Lie group of $\mathbb {SLAM}_{n}(3)$ is proposed. The presented novel solution estimates the vehicle’s pose (i.e., attitude and position) with respect to features simultaneously positioning the reference features in the global frame. The proposed estimator on manifold is characterized by predefined measures of transient and steady-state performance. Dynamically reducing boundaries guide the error function of the system to reduce asymptotically to the origin from its starting position within a large given set. The proposed observer has the ability to use the available velocity and feature measurements directly. Also, it compensates for unknown constant bias attached to velocity measurements. Numerical results reveal effectiveness of the proposed observer.
Efficient Transport Logistics : An Approach for Urban Freight Transport in Austria

To alleviate traffic congestion that results from the growth of e-commerce we propose an approach in the city of Linz, Austria by relying on shared distribution centers from different companies. We develop two algorithms to find out the optimal location for the hubs and calculate the shortest path between locations. Results showed that in an urban environment, the implementation of hubs results in a reduction of the number of delivery vehicles. It reduces driving distances from hub to the customers, and also benefits the drivers that need to return home every day.
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers

Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic game-theoretic settings.
Plant preference in relation to life history traits in the zoophytophagous predator Dicyphus hesperus

Dicyphus hesperus Knight (Heteroptera: Miridae) is an omnivorous predator used to control pests of greenhouse vegetables. Plant preferences and life history traits were studied using nine plant species: Lycopersicon esculentum Mill. (Solanaceae), Capsicum annuum L. (Solanaceae), Verbascum thapsus L. (Scrophulariaceae), Nepeta cataria L. (Lamiaceae), Stachys albotomentosa (Lamiaceae), Nicotiana tabacum L. (Solanaceae), Vicia sativa L. (Fabaceae), Zea mays L. (Gramineae), and Chrysanthemum coronarium L. (Asteraceae). Plants were selected from among potential target crops, natural hosts, plants used for mass rearing, and plants on which D. hesperus has not been reported. Plant preference was measured by multi‐choice host plant selection and oviposition assays. Development and reproduction were measured on each of the plant species on both a plant diet alone and on a plant diet supplemented with Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) eggs. Dicyphus hesperus females and nymphs expressed a preference for some plants over others. Plant preference ranged from low preference plants, such as Z. mays, V. sativa, C. coronarium, and C. annuum, to high preference plants such as V. thapsus, N. tabacum, and S. albotomentosa. When E. kuehniella eggs were supplied, there were few differences in the development time and fecundity of D. hesperus among plants, with the exception of corn and broad bean, where fecundity was lower. On a plant diet alone, nymphs were able to complete their development on V. thapsus, C. annuum, and N. cataria. However, mortality and development time were much lower on V. thapsus than on C. annuum and N. cataria. On most of the plant species D. hesperus did not lay any eggs when fed on a plant diet alone. On V. thapsus, females laid a few eggs and lived longer than when fed on prey. Dicyphus hesperus females tended to prefer host plants on which nymph survival without prey was greatest.
Compiler Directed Speculative Intermittent Computation

This paper presents CoSpec, a new architecture/compiler co-design scheme that works for commodity in-order processors used in energy-harvesting systems. To achieve crash consistency without requiring unconventional architectural support, CoSpec leverages speculation assuming that power failure is not going to occur and thus holds all committed stores in a store buffer (SB), as if they were speculative, in case of mispeculation. CoSpec compiler first partitions a given program into a series of recoverable code regions with the SB size in mind, so that no region overflows the SB. When the program control reaches the end of each region, the speculation turns out to be successful, thus releasing all the buffered stores of the region to NVM. If power failure occurs during the execution of a region, all its speculative stores disappear in the volatile SB, i.e., they never affect program states in NVM. Consequently, the interrupted region can be restarted with consistent program states in the wake of power failure. To hide the latency of the SB release, i.e., essentially NVM writes, at each region boundary, CoSpec overlaps the NVM writes of the current region with the speculative execution of the next region. Such instruction level parallelism gives an illusion of out-of-order execution on top of the in-order processor, achieving a speedup of more than 1.2X when there is no power outage. Our experiments on a set of real energy harvesting traces with frequent outages demonstrate that CoSpec outperforms the state-of-the-art scheme by 1.8~3X on average.
Normalization Matters in Zero-Shot Learning

An ability to grasp new concepts from their descriptions is one of the key features of human intelligence, and zero-shot learning (ZSL) aims to incorporate this property into machine learning models. In this paper, we theoretically investigate two very popular tricks used in ZSL: "normalize+scale" trick and attributes normalization and show how they help to preserve a signal's variance in a typical model during a forward pass. Next, we demonstrate that these two tricks are not enough to normalize a deep ZSL network. We derive a new initialization scheme, which allows us to demonstrate strong state-of-the-art results on 4 out of 5 commonly used ZSL datasets: SUN, CUB, AwA1, and AwA2 while being on average 2 orders faster than the closest runner-up. Finally, we generalize ZSL to a broader problem -- Continual Zero-Shot Learning (CZSL) and test our ideas in this new setup. The source code to reproduce all the results is available at this https URL.
Deep Residual Mixture Models

We propose Deep Residual Mixture Models (DRMMs) which share the many desirable properties of Gaussian Mixture Models (GMMs), but with a crucial benefit: The modeling capacity of a DRMM can grow exponentially with depth, while the number of model parameters only grows quadratically. DRMMs allow for extremely flexible conditional sampling, as the conditioning variables can be freely selected without re-training the model, and it is easy to combine the sampling with priors and (in)equality constraints. DRMMs should be applicable where GMMs are traditionally used, but as demonstrated in our experiments, DRMMs scale better to complex, high-dimensional data. We demonstrate the approach in constrained multi-limb inverse kinematics and image completion.
An Online Algorithm for Computation Offloading in Non-Stationary Environments

We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~35% reduction in latency. Our results highlight the significance of heavily discounting the past rewards in dynamic environments.
Quanta burst photography

Single-photon avalanche diodes (SPADs) are an emerging sensor technology capable of detecting individual incident photons, and capturing their time-of-arrival with high timing precision. While these sensors were limited to singlepixel or low-resolution devices in the past, recently, large (up to 1 MPixel) SPAD arrays have been developed. These single-photon cameras (SPCs) are capable of capturing high-speed sequences of binary single-photon images with no read noise. We present quanta burst photography, a computational photography technique that leverages SPCs as passive imaging devices for photography in challenging conditions, including ultra low-light and fast motion. Inspired by recent success of conventional burst photography, we design algorithms that align and merge binary sequences captured by SPCs into intensity images with minimal motion blur and artifacts, high signal-to-noise ratio (SNR), and high dynamic range. We theoretically analyze the SNR and dynamic range of quanta burst photography, and identify the imaging regimes where it provides significant benefits. We demonstrate, via a recently developed SPAD array, that the proposed method is able to generate high-quality images for scenes with challenging lighting, complex geometries, high dynamic range and moving objects. With the ongoing development of SPAD arrays, we envision quanta burst photography finding applications in both consumer and scientific photography.
Spatial and seasonal variability in cetacean distribution in the fjords of northern Patagonia, Chile

Compared with other Chilean coastal areas, little is known about the diversity and distribution of cetaceans in northern Patagonian fjords. Between December 2000 and November 2001, surveys on platforms of opportunity were undertaken in southern Chile to evaluate species richness and the spatial and seasonal distribution of cetaceans. Nine species were recorded, blue, humpback, and minke whales, Peale’s dolphin, Chilean dolphin, killer whale, false killer whale, bottlenose dolphin, and Cuvier’s beaked whale. The pattern of cetacean distribution displayed significant seasonal differences, with most baleen whales (mysticetes) observed during late summer and autumn, and toothed cetaceans (odontocetes) mostly during spring. Generalized additive models, used to assess the spatial distribution of cetaceans, showed that mysticetes were distributed disproportionately along a north–south gradient, in open gulfs with oceanic influence, and close to shore. In contrast, odontocetes were observed mainly within narrow channels, areas with complex coastal morphology, peaking at different water depths. These findings, although from a single year of data, increase our understanding of habitat determinants of cetacean distribution in southern Chile. The results have the potential to be applied to coastal conservation and management in the region.
Cladistic analysis of Simulium (Trichodagmia) and Simulium (Thyrsopelma) (Diptera: Simuliidae)

Abstract A cladistic analysis of Simulium (Trichodagmia) sensu Crosskey and Howard, using 34 morphological characters of larvae (6 characters), pupa (5) and adults (23), yields nine most parsimonious trees under equal weights (length 101 steps CI 0.49 RI 0.73). Successive weighting based on the maximal rescaled consistency index preferred one of the nine (31.37 steps CI 0.62 RI 0.87 total fitcon3=235.8), which was also one of two trees found under implicit weights with concavity values of 3–6. The cladogram justifies the recognition of two subgenera. Simulium (Trichodagmia) sensu stricto (containing S. muiscorum, sumapazense, S. wygodzinskyorum, S. nigrimanum, S. chalcocoma, S. huairayacu and S. lahillei) is supported by the branchial tip sclerotization and the presence of cibarial teeth, larval body tegument covered with lanceolate hairs, female with simple claw, and gonapophysis size. Simulium (Thyrsopelma) (containing S. scutistriatum, S. hirtipupa, S. orbitale, S. guianense, S. perplexum and S. itaunense) is supported by the hypostomial teeth.
Effect of dietary available phosphorus and organic acids on the performance and egg quality of laying hens

SUMMARY Phosphorus is essential for the growth of poultry, particularly for bone development. Eighty percent or more of the total P is in the form of phytate. Monogastrics, such as chickens and pigs, do not secrete the enzyme that breaks down phytates and separates P in grains or oil meals. Organic acids (OA) effectively check the growth of mold and bacteria. Furthermore, adding OA to feed can lower the gastric pH. Low gastric pH accelerates the conversion of pepsinogen to pepsin, which improves the absorption rate of proteins, amino acids, and minerals. This experiment was performed to examine the effect of adding OA to feeds with different available P (AP) levels in terms of egg productivity, egg quality, and IgY levels. Results suggest that the AP level in feed does not have to be higher than 0.3%, when compared with 0.4% AP in the diet. Supplementation of 0.2% OA to the 0.3% AP diet showed the best results in hen-housed egg production, soft-shell plus broken egg production, FCR, and egg yolk IgY. However, the best eggshell color was obtained in the 0.4% AP and 0.2% OA diet.
Novel dual relation and constant in Hawking-Page phase transitions

Universal relations and constants have important applications in understanding a physical theory. In this letter, we explore this issue for Hawking-Page phase transitions in Schwarzschild anti de Sitter black holes. We find a novel exact dual relation between the minimum temperature of the ($d$+1)-dimensional black hole and the Hawking-Page phase transition temperature in $d$ dimensions, reminiscent of the holographic principle. Furthermore, we find that the normalized Ruppeiner scalar curvature is a universal constant at the Hawking-Page transition point. Since the Ruppeiner curvature can be treated as an indicator of the intensity of the interactions amongst black hole microstructures, we conjecture that this universal constant denotes an interaction threshold, beyond which a virtual black hole becomes a real one. This new dual relation and universal constant are fundamental in understanding Hawking-Page phase transitions, and will have new important applications in the holographic principle and the black hole physics in the near future.
Recovering Accurate Labeling Information from Partially Valid Data for Effective Multi-Label Learning

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial Multi-Label Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods.
Optimal Control of SOAs With Artificial Intelligence for Sub-Nanosecond Optical Switching

Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment. Effective off-on switching (settling) times of 542 ps are demonstrated with just 4.8% overshoot, achieving an order of magnitude improvement over previous attempts described in the literature and standard dampening techniques from control theory.
An Efficient Sparse Quadratic Programming Relaxation Based Algorithm for Large-Scale MIMO Detection

Multiple-input multiple-output (MIMO) detection is a fundamental problem in wireless communications and it is strongly NP-hard in general. Massive MIMO has been recognized as a key technology in the fifth generation (5G) and beyond communication networks, which on one hand can significantly improve the communication performance, and on the other hand poses new challenges of solving the corresponding optimization problems due to the large problem size. While various efficient algorithms such as semidefinite relaxation (SDR) based approaches have been proposed for solving the small-scale MIMO detection problem, they are not suitable to solve the large-scale MIMO detection problem due to their high computational complexities. In this paper, we propose an efficient sparse quadratic programming (SQP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as an SQP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the SQP problem. In sharp contrast to the SDRs for the MIMO detection problem, our relaxation does not contain any (positive semidefinite) matrix variable and the numbers of variables and constraints in our relaxation are significantly less than those in the SDRs, which makes it particularly suitable for the large-scale problem. Then we propose a projected Newton based quadratic penalty method to solve the relaxation problem. By extensive numerical experiments, when applied to solve large-scale problems, the proposed algorithm achieves better detection performance and is more robust to the choice of the initial point than a recently proposed generalized power method.