Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state's constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that \HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler.

In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. This has motivated a series of papers that analyze tractable synthetic domains and postulate precise mechanisms that may underlie ICL. However, the use of relatively distinct setups that often lack a sequence modeling nature to them makes it unclear how general the reported insights from such studies are. Motivated by this, we propose a synthetic sequence modeling task that involves learning to simulate a finite mixture of Markov chains. As we show, models trained on this task reproduce most well-known results on ICL, hence offering a unified setting for studying the concept. Building on this setup, we demonstrate we can explain a model's behavior by decomposing it into four broad algorithms that combine a fuzzy retrieval vs. inference approach with either unigram or bigram statistics of the context. These algorithms engage in a competition dynamics to dominate model behavior, with the precise experimental conditions dictating which algorithm ends up superseding others: e.g., we find merely varying context size or amount of training yields (at times sharp) transitions between which algorithm dictates the model behavior, revealing a mechanism that explains the transient nature of ICL. In this sense, we argue ICL is best thought of as a mixture of different algorithms, each with its own peculiarities, instead of a monolithic capability. This also implies that making general claims about ICL that hold universally across all settings may be infeasible.

In this paper, we create "Love in Action" (LIA), a body language-based social game utilizing video cameras installed in public spaces to enhance social relationships in real-world. In the game, participants assume dual roles, i.e., requesters, who issue social requests, and performers, who respond social requests through performing specified body languages. To mediate the communication between participants, we build an AI-enhanced video analysis system incorporating multiple visual analysis modules like person detection, attribute recognition, and action recognition, to assess the performer's body language quality. A two-week field study involving 27 participants shows significant improvements in their social friendships, as indicated by Self-reported questionnaires. Moreover, user experiences are investigated to highlight the potential of public video cameras as a novel communication medium for socializing in public spaces.

Traditionally, the satisfaction relation in modal logic is defined as a relation w φ between a possible world w and a formula φ. In such a setting, formula φ expresses a property of possible worlds. For example, statement w "There are black holes" expresses the fact that world w has a property of containing black holes. It is also possible to consider logical systems that capture properties of agents rather than of possible worlds. In such systems, satisfaction relation a φ is a relation between an agent a and a formula φ.

Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. While there are already some works on backdoor attacks on Graph Neural Networks (GNNs), the backdoor trigger in the graph domain is mostly injected into random positions of the sample. There is no work analyzing and explaining the backdoor attack performance when injecting triggers into the most important or least important area in the sample, which we refer to as trigger-injecting strategies MIAS and LIAS, respectively. Our results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant. Furthermore, we explain these two strategies' similar (better) attack performance through explanation techniques, which results in a further understanding of backdoor attacks in GNNs.

Any normal person can tell the Lia chatbot on Twitter is not real. One quick look at her profile shows a digitally-created humanoid, one that has all the hallmarks of a bot. The shadows are not quite right, the flecks in her eyes are too perfect, and there's a slightly cartoonish look. The fact that someone took the time to create a visual representation of a chatbot is quite impressive. In a video, Lia introduces herself and explains her ambitions.

Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In order to enhance the computational efficiency of fuzzy inference engine in multi-input-single-output (MISO) fuzzy systems, this paper aims mainly to investigate three MISO fuzzy hierarchial inference engines based on fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy (LIA) with them. For a given aggregation function, the fuzzy implication which satisfies (LIA) with this aggregation function is then characterized. Finally, we construct three fuzzy hierarchical inference engines in MISO fuzzy systems applying aforementioned theoretical developments.

Glancing at Barcelona's still-unfinished Sagrada Família Roman Catholic basilica, with its famous sandcastle-like exterior, it is easy to get the wrong idea about its architect, Antoni Gaudí, as a carefree, loosey-goosey artist. The whimsical exterior hides a geometrically sophisticated, structurally advanced design--a big part of the reason this grand basilica, begun in 1882, has taken so many decades to build, remaining the world's longest-running ongoing architectural project. This complexity required an utterly different approach to modeling than what architects had typically deployed. Instead of using two-dimensional drawings to guide builders, Gaudí relied heavily on large, high-fidelity plaster models--models that needed to be reverse engineered and rebuilt after extensive damage during the Spanish Civil War. In a separate project, Gaudí pioneered the use of hanging-chain models that enable changes in real time; though he did not use these interactive models on the Sagrada Família, they guided his thinking and prefigured the so-called parametric design software that has been instrumental to the acceleration of the project's pace in recent years.

The more we are advancing, the more we are relying on technology to make our lives easier, and Artificial Intelligence is just an extension of that. In this article, I will explain what AI is, why it's important for us to be aware of its existence and where AI will take us in the future. AI has come a long way from being just a concept or idea on paper to an actuality that we can see and use today. From Siri to self-driving cars, it touches every aspect of our lives and allows us to do things more efficiently than ever before. But there are also dangers associated with artificial intelligence -- such as job loss because robots can work faster and cheaper than humans, or even an all-out robot uprising!

Lia's creator Soul Machines is developing digital humans, complete with digital brains, who are portrayed by actual humans. Verizon's Labs showcases innovators like Soul Machines to explore how 5G networks support cutting edge technology that contributes to the betterment of society. Having the speed and bandwidth of a 5G connection is critical to ensuring that digital interactions feel humanized. In human-to-human engagement, the brain rapidly identifies and processes data points such as tone and non-verbal cues. In digital-to-human engagement, mimicking human-like interactions requires 5G's bandwidth and speed.