We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated

From its role in kissing to mythological healing powers, mistletoe's roots run deep. This novella was the earliest and most popular of Dickens' Christmas stories. The kissing under mistletoe (left) and evergreen decoration hanging from the ceiling are vestiges of pre-Christian winter rites. Breakthroughs, discoveries, and DIY tips sent every weekday. It's hard to imagine a holiday season without Bing Crosby's Christmas standard Originally written from the perspective of a soldier stationed overseas during World War II, his longing for the simple comforts of home and reconnecting with his loved ones at Christmas is almost palpable: " Mistletoe just inexplicably feels familiar. Every December, the evergreen sprig s that spent the offseason hidden in our subconscious are suddenly all around us. Mistletoe is the long-lost acquaintance that we instantly recognize and embrace, yet whose backstory has been lost to us. "When I talk to people about parasitic plants, I know mistletoe is the one that they'll immediately recognize even if they don't really know it's a parasite," Virginia Tech plant biologist Jim Westwood tells . Author Washington Irving, best known for The Legend of Sleepy Hollow and is often credited with helping popularize the parasitic evergreen shrub in the United States. He wrote about the plant in an 1820 collection of short stories, but the roots of mistletoe go much deeper elsewhere in the world. Dating back to Ancient Greece and Rome, leafy mistletoe has long excited the imagination. Mistletoe served as a centerpiece of Celtic Rituals and Norse myths, where it bestowed life and fertility and served as an aphrodisiac, a plant of parley, an antidote for poisons, and a means of safe passage to and from Hades. According to The Living Lore, since the plant can thrive in the high branches of its host without soil, "many cultures saw mistletoe as a sacred plant, existing in liminal spaces between life and death, earth and sky, and human and divine." In Old Norse mythology, Baldr, the son of the god Odin and the goddess Frigg, was slain with a mistletoe spear. Some interpretations suggest that, "kissing under the mistletoe symbolizes forgiveness, echoing Frigg's grief and eventual reconciliation with the plant." Many early physicians and scientists saw mistletoe as a cure-all for the woes of the world. It was used to treat various diseases and conditions including epilepsy, infertility, and ulcers. In Pliny's, the writer and physician describes the Celtic ritual of oak and mistletoe. High priests dressed in white harvested mistletoe with golden sickles from the branches of sacred oak trees to make an elixir that could counteract any poison and render any barren animal fertile. "It's easy to imagine how people become fixated on mistletoe plants," says Westwood. "It stays green all winter growing in its host tree.

We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated

A rich vehicle routing problem is considered, allowing multiple trips of heterogeneous vehicles stationed at geographically distributed vehicle depots having access to different modes of transportation. The problem arises from the real-world requirement of optimizing the disaster response time by minimizing the makespan of vehicular routes. Multiple diversely-functional vertices are considered, including Transhipment Ports as inter-modal resource transfer stations. Both simultaneous and split pickup and delivery are considered, for multiple cargo types, along with Vehicle-Cargo and Transhipment Port-Cargo compatibilities. The superiority of the proposed cascaded minimization approach is demonstrated over the existing makespan minimization approaches through our developed Mixed-Integer Linear Programming formulation. To solve the problem quickly for practical implementation in a Disaster Management-specific Decision Support System, an extensive Heuristic Algorithm is devised which utilizes Decision Tree based structuring of possible routes; the Decision Tree approach helps to inherently capture the compatibility issues, while also explore the solution space through stochastic weights. Preferential generation of small route elements is performed, which are integrated into route clusters; we consider multiple different logical integration approaches, as well as shuffling the logics to simultaneously produce multiple independent solutions. Finally, perturbations of the different solutions are done to find better neighbouring solutions. The computational performance of the PSR-GIP Heuristic, on our created novel datasets, indicates that it is able to give good solutions swiftly for practical problems involving large integer instances that the MILP is unable to solve.

Unmanned Aerial Vehicles (UAVs), also known as drones, have gained popularity in various fields such as agriculture, emergency response, and search and rescue operations. UAV networks are susceptible to several security threats, such as wormhole, jamming, spoofing, and false data injection. Time Delay Attack (TDA) is a unique attack in which malicious UAVs intentionally delay packet forwarding, posing significant threats, especially in time-sensitive applications. It is challenging to distinguish malicious delay from benign network delay due to the dynamic nature of UAV networks, intermittent wireless connectivity, or the Store-Carry-Forward (SCF) mechanism during multi-hop communication. Some existing works propose machine learning-based centralized approaches to detect TDA, which are computationally intensive and have large message overheads. This paper proposes a novel approach DATAMUt, where the temporal dynamics of the network are represented by a weighted time-window graph (TWiG), and then two deterministic polynomial-time algorithms are presented to detect TDA when UAVs have global and local network knowledge. Simulation studies show that the proposed algorithms have reduced message overhead by a factor of five and twelve in global and local knowledge, respectively, compared to existing approaches. Additionally, our approaches achieve approximately 860 and 1050 times less execution time in global and local knowledge, respectively, outperforming the existing methods.

Knowledge Graphs (KGs) have seen increasing use across various domains -- from biomedicine and linguistics to general knowledge modelling. In order to facilitate the analysis of knowledge graphs, Knowledge Graph Embeddings (KGEs) have been developed to automatically analyse KGs and predict new facts based on the information in a KG, a task called "link prediction". Many existing studies have documented that the structure of a KG, KGE model components, and KGE hyperparameters can significantly change how well KGEs perform and what relationships they are able to learn. Recently, the Topologically-Weighted Intelligence Generation (TWIG) model has been proposed as a solution to modelling how each of these elements relate. In this work, we extend the previous research on TWIG and evaluate its ability to simulate the output of the KGE model ComplEx in the cross-KG setting. Our results are twofold. First, TWIG is able to summarise KGE performance on a wide range of hyperparameter settings and KGs being learned, suggesting that it represents a general knowledge of how to predict KGE performance from KG structure. Second, we show that TWIG can successfully predict hyperparameter performance on unseen KGs in the zero-shot setting. This second observation leads us to propose that, with additional research, optimal hyperparameter selection for KGE models could be determined in a pre-hoc manner using TWIG-like methods, rather than by using a full hyperparameter search.

We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or trunk diffusion process is associated with a primary variable (e.g., graph structure), and additional offshoot or stem processes are dedicated to dependent variables (e.g., graph properties or labels). A new strategy, which we call loop guidance, effectively orchestrates the flow of information between the trunk and the stem processes during sampling. This approach allows us to uncover intricate interactions and dependencies, and unlock new generative capabilities. We provide extensive experiments to demonstrate strong performance gains of the proposed method over contemporary baselines in the context of conditional graph generation, underscoring the potential of Twigs in challenging generative tasks such as inverse molecular design and molecular optimization.

In this paper we introduce TWIG (Topologically-Weighted Intelligence Generation), a novel, embedding-free paradigm for simulating the output of KGEs that uses a tiny fraction of the parameters. TWIG learns weights from inputs that consist of topological features of the graph data, with no coding for latent representations of entities or edges. Our experiments on the UMLS dataset show that a single TWIG neural network can predict the results of state-of-the-art ComplEx-N3 KGE model nearly exactly on across all hyperparameter configurations. To do this it uses a total of 2590 learnable parameters, but accurately predicts the results of 1215 different hyperparameter combinations with a combined cost of 29,322,000 parameters. Based on these results, we make two claims: 1) that KGEs do not learn latent semantics, but only latent representations of structural patterns; 2) that hyperparameter choice in KGEs is a deterministic function of the KGE model and graph structure. We further hypothesise that, as TWIG can simulate KGEs without embeddings, that node and edge embeddings are not needed to learn to accurately predict new facts in KGs. Finally, we formulate all of our findings under the umbrella of the ``Structural Generalisation Hypothesis", which suggests that ``twiggy" embedding-free / data-structure-based learning methods can allow a single neural network to simulate KGE performance, and perhaps solve the Link Prediction task, across many KGs from diverse domains and with different semantics.

The launch of ChatGPT, an artificial intelligence chatbot, late last year marked a new era in AI - and sparked widespread fears over the effect of artificial intelligence on the job market. Its abilities to write poems, screenplays, take exams and simulate entire chat rooms have led some to suggest it could rapidly take over jobs in customer service, copywriting and even the legal profession. Microsoft invested $10 billion in ChatGPT and said that the technology will change how people interact with computers. 'I believe that ChatGPT could replace 20 percent of the workforce as is,' AI expert Richard DeVere, Head of Social Engineering for Ultima, told DailyMail.com. 'ChatGPT is no fad – it's a new technological revolution.

To receive the Vogue Business newsletter, sign up here. Digital fashion company The Dematerialised is offering to swap physical clothing for digital fashion in a bid to lure more shoppers to Web3 and encourage resale. The Dematerialised is now accepting payments via Twig, a London-based startup that lets people trade in physical clothing for immediate cash that can be used to spend on the digital fashion NFT marketplace. Twig uses artificial intelligence to analyse the value of items in submitted photos, then transfers money to a card that people can then use to checkout on The Dematerialised. Twig then handles reselling or donating the goods itself.