Tightest Admissible Shortest Path

The shortest path problem in graphs is fundamental to AI. Nearly all variants of the problem and relevant algorithms that solve them ignore edge-weight computation time and its common relation to weight uncertainty. This implies that taking these factors into consideration can potentially lead to a performance boost in relevant applications. Recently, a generalized framework for weighted directed graphs was suggested, where edge-weight can be computed (estimated) multiple times, at increasing accuracy and run-time expense. We build on this framework to introduce the problem of finding the tightest admissible shortest path (TASP); a path with the tightest suboptimality bound on the optimal cost. This is a generalization of the shortest path problem to bounded uncertainty, where edge-weight uncertainty can be traded for computational cost. We present a complete algorithm for solving TASP, with guarantees on solution quality. Empirical evaluation supports the effectiveness of this approach.Comment: arXiv admin note: text overlap with arXiv:2208.1148

Spreading the Oprah Effect: The Diffusion of Demand Shocks in a Recommendation Network

We study the magnitude and persistence of the diffusion of exogenous demand shocks on an ecommerce recommendation network. The demand shocks are generated by book reviews on the Oprah Winfrey Show and in the NYTimes, and the recommendation network is generated by Amazon’s copurchase network. We find a strikingly high level of diffusion of exogenous shock through such networks. Neighboring books experience a dramatic increase in their demand levels, even though they are not actually featured on the review. An average of 40% of neighbors, even 4 clicks away see a statistically significant increase in their demand levels; this effect is indicative of the depth of contagion in online recommendation networks following exogenous shocks. We also document how clustered networks “trap” a higher fraction of the contagion closer to the reviewed book, and we provide summaries of the persistence and relative magnitude of the demand inflation of the neighborhood

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

A generalized light-driven model of community transitions along coral reef depth gradients

Aim: Coral reefs shift between distinct communities with depth throughout the world. Yet, despite over half a century of research on coral reef depth gradients, researchers have not addressed the driving force of these patterns. We present a theoretical, process-based model of light’s influence on the shallow to mesophotic reef transition as a single quantitative framework. We also share an interactive web application. Moving beyond depth as an ecological proxy will enhance research conducted on deeper coral reefs. Location: Global; subtropical and tropical coral reefs, oligotrophic and turbid coastal waters. Time period: Present day (2020). Major taxa: Scleractinia. Methods: We constructed ordinary differential equations representing the preferred light environments of shallow and mesophotic Scleractinia. We projected these as depth bands using light attenuation coefficients from around the world, and performed a sensitivity analysis. Results: We found light relationships alone are sufficient to capture major ecological features across coral reef depth gradients. Our model supports the depth limits currently used in coral reef ecology, predicting a global range for the shallow-upper mesophotic boundary at 36.1 m ± 5.6 and the upper-lower mesophotic boundary at 61.9 m ± 9.6. However, our model allows researchers to move past these fixed depth limits, and quantitatively predict the depths of reef zones in locations around the world. Main conclusions: The use of depth as a proxy for changes in coral reef communities offers no guidance for environmental variation between sites. We have shown it is possible to use light to predict the depth boundaries of reef zones as a continuous variable, and to accommodate this variability. Predicting the depths of reef zones in unusual light environments suggests that shallow-water turbid reefs should be considered as mesophotic coral ecosystems. Nonetheless, the current depth-based heuristics are relatively accurate at a global level

Mutagen-Specific Mutation Signature Determines Global microRNA Binding

Micro-RNAs (miRNAs) are small non-coding RNAs that regulate gene products at the post-transcriptional level. It is thought that loss of cell regulation by miRNAs supports cancer development. Based on whole genome sequencing of a melanoma tumor, we predict, using three different computational algorithms, that the melanoma somatic mutations globally reduce binding of miRNAs to the mutated 3′UTRs. This phenomenon reflects the nature of the characteristic UV-induced mutation, C-to-T. Furthermore, we show that seed regions are enriched with Guanine, thus rendering miRNAs prone to reduced binding to UV-mutated 3′UTRs. Accordingly, mutation patterns in non UV-induced malignancies e.g. lung cancer and leukemia do not yield similar predictions. It is suggested that UV-induced disruption of miRNA-mediated gene regulation plays a carcinogenic role. Remarkably, dark-skinned populations have significantly higher GC content in 3′UTR SNPs than light-skinned populations, which implies on evolutionary pressure to preserve regulation by trans-acting oligonucleotides under conditions with excess UV radiation

Text2Model: Model Induction for Zero-shot Generalization Using Task Descriptions

We study the problem of generating a training-free task-dependent visual classifier from text descriptions without visual samples. This \textit{Text-to-Model} (T2M) problem is closely related to zero-shot learning, but unlike previous work, a T2M model infers a model tailored to a task, taking into account all classes in the task. We analyze the symmetries of T2M, and characterize the equivariance and invariance properties of corresponding models. In light of these properties, we design an architecture based on hypernetworks that given a set of new class descriptions predicts the weights for an object recognition model which classifies images from those zero-shot classes. We demonstrate the benefits of our approach compared to zero-shot learning from text descriptions in image and point-cloud classification using various types of text descriptions: From single words to rich text descriptions

Example-based Hypernetworks for Out-of-Distribution Generalization

As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge. This paper addresses the issue of multi-source adaptation for unfamiliar domains: We leverage labeled data from multiple source domains to generalize to unknown target domains at training. Our innovative framework employs example-based Hypernetwork adaptation: a T5 encoder-decoder initially generates a unique signature from an input example, embedding it within the source domains' semantic space. This signature is subsequently utilized by a Hypernetwork to generate the task classifier's weights. We evaluated our method across two tasks - sentiment classification and natural language inference - in 29 adaptation scenarios, where it outpaced established algorithms. In an advanced version, the signature also enriches the input example's representation. We also compare our finetuned architecture to few-shot GPT-3, demonstrating its effectiveness in essential use cases. To our knowledge, this marks the first application of Hypernetworks to the adaptation for unknown domains.Comment: First two authors contributed equally to this work. Our code and data are available at: https://github.com/TomerVolk/Hyper-PAD

Energy allocation trade-offs as a function of age in fungiid corals

To compete effectively, living organisms must adjust the allocation of available energy resources for growth, survival, maintenance, and reproduction throughout their life histories. Energy demands and allocations change throughout the life history of an organism, and understanding their energy allocation strategies requires determination of the relative age of individuals. As most scleractinian corals are colonial, the relationship between age and mass/size is complicated by colony fragmentation, partial mortality, and asexual reproduction. To overcome these limitations, solitary mushroom corals, Herpolitha limax from Okinawa, Japan and Fungia fungites from Okinawa and the Great Barrier Reef (GBR), Australia, were used to investigate how energy allocation between these fundamental processes varies as a function of age. Measurements of the relative growth, biochemical profiles, fecundity of individuals of different sizes, and the settlement success of their progeny have revealed physiological trade-offs between growth and reproduction, with increasing body mass ultimately leading to senescence. The importance of energy allocation for reproduction led us to examine the reproductive strategies and sex allocation in the two studied species. In the present study, the smallest individuals of both species studied were found to invest most of their energy in relative growth, showing higher lipid and carbohydrate content than the later stages. In medium-sized corals, this pattern was overturned in favour of reproduction, manifesting in terms of both the highest fecundity and settlement success of the resulting brooded larvae. Finally, a phase of apparent senescence was observed in the largest individuals, characterized by a decrease in most of the parameters measured. In addition, complex reproductive plasticity has been revealed in F. fungites in the GBR, with individual females releasing eggs, embryos, planulae, or a combination of these. These data provide the most direct estimates currently available for physiological, age-related trade-offs during the life history of a coral. The unusual reproductive characteristics of the GBR F. fungites indicate previously unknown layers of complexity in the reproductive biology of corals and have implications for their adaptive potential across a wide geographical scale

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

Harnessing Soluble NK Cell Killer Receptors for the Generation of Novel Cancer Immune Therapy

The natural cytotoxic receptors (NCRs) are a unique set of activating proteins expressed mainly on the surface of natural killer (NK) cells. The NCRs, which include three members; NKp46, NKp44 and NKp30, are critically involved in NK cytotoxicity against different targets, including a wide range of tumor cells derived from various origins. Even though the tumor ligands of the NCRs have not been identified yet, the selective manner by which these receptors target tumor cells may provide an excellent basis for the development of novel anti-tumor therapies. To test the potential use of the NCRs as anti-tumor agents, we generated soluble NCR-Ig fusion proteins in which the constant region of human IgG1 was fused to the extracellular portion of the receptor. We demonstrate, using two different human prostate cancer cell lines, that treatment with NKp30-Ig, dramatically inhibits tumor growth in vivo. Activated macrophages were shown to mediate an ADCC response against the NKp30-Ig coated prostate cell lines. Finally, the Ig fusion proteins were also demonstrated to discriminate between benign prostate hyperplasia and prostate cancer. This may provide a novel diagnostic modality in the difficult task of differentiating between these highly common pathological conditions