This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. We then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low-rank. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow identification of operational regimes where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications.

Traditional theories of social models conceptualize teams as distributed processors, disregarding the interdependence necessary to multi-task. Yet, interdependence characterizes social behavior. Instead, traditional theory favor cooperation, a state of least entropy production (LEP), without understanding the causes, limits or consequences of cooperation. As a simple example of interdependence, foraging prey overgraze forests free of predators. In our model, interdependence creates uncertainty, tradeoffs and signals (e.g., prices, coordination, innovation). Unlike individuals, the ability of teams to multitask reflects a quantum-like entanglement that represents maximum entropy production (MEP) when solving the problems signaled by society to improve its welfare. Our model supports findings that evolution in nature is driven by the MEP from making intelligent choices. Exploiting interdependence improves team intelligence, improves performance and reduces the risk of human error; forced cooperation disorganizes it by increasing the risk of error; e.g., if team cooperation improves teamwork, widespread forced cooperation in an autocracy or bureaucracy reduces social intelligence by adding unnecessary noise to signals. In our model, competition between teams self-organizes outsiders willing to sort through the noise for signals of the choices that improve social welfare (e.g., teams in courtrooms; science; entertainment; sports; businesses). Social systems organized around competition (e.g., stronger signals from robust checks and balances) better control a society by more correctly sizing teams to solve problems with fewer errors compared to autocracies or bureaucracies. Overall, we predict, the density of MEP directed at solving problems in a society with the constraints imposed from strong checks and balances, yet able to freely self-organize its labor and capital within those constraints, is denser.

As autonomous systems are becoming more and more pervasive, they often have to make decisions concerning moral and ethical values. There are many approaches to incorporating moral values in autonomous decision-making that are based on some sort of logical deduction. However, we argue here, in order for decision-making to seem persuasive to humans, it needs to reflect human values and judgments. Employing some insights from our ongoing researchusing features of the blackboard architecture for a context-aware recommender system, and a legal decision-making system that incorporates supra-legal aspects, we aim to explore if this architecture can also be adapted to implement a moral decision-making system that generates rationales that are persuasive to humans. Our vision is that such a system can be used as an advisory system to consider a situation from different moral perspectives, and generate ethical pros and cons of taking a particular course of action in a given context.

We know too that guilt may be alleviated by private confession Theorists conceive of shame and guilt as belonging to the (namely to a priest or a psychotherapist) plus the family of self-conscious emotions (Lewis 1990) (Fischer renouncing of past failings in future. Because of their private and Tangney 1995) (Tangney and Dearing 2002), invoked character, such confessions and atonements, given through self-reflection and self-evaluation. Though both their cost (prayers or fees), render temptation defecting less have evolved to promote cooperation, guilt and shame can probable. Public or open confession of guilt can be coordinated be treated separately. Guilt is an inward private phenomenon, with apology for better effect, and the cost appertained though it can promote apology, and even spontaneous to some common good (like charity), or as individual public confession. Shame is inherently public, though it compensation to injured parties.

We study the complexity of a combinatorial variant of the Shift Bribery problem in elections. In the standard Shift Bribery problem, we are given an election where each voter has a preference order over the set of candidates and where an outside agent, the briber, can pay each voter to rank the briber's favorite candidate a given number of positions higher. The goal is to ensure the victory of the briber's preferred candidate. The combinatorial variant of the problem, introduced in this paper, models settings where it is possible to affect the position of the preferred candidate in multiple votes, either positively or negatively, with a single bribery action. This variant of the problem is particularly interesting in the context of large-scale campaign management problems (which, from the technical side, are modeled as bribery problems). We show that, in general, the combinatorial variant of the problem is highly intractable; specifically, NP-hard, hard in the parameterized sense, and hard to approximate. Nevertheless, we provide parameterized algorithms and approximation algorithms for natural restricted cases.

Human conscious flows can alter brain states. Such brain activities modulate energy consumptions, which can be manifest in the BOLD effect in fMRI experiment. The goal of this study is to identify whether there is difference in such BOLD effects between experienced Tai Chi master in meditation state and normal control subjects. In this experiment, both the meditator and the controls using their conscious to lead a flow periodically circling in their brain in axial, sagittal, and coronal orientations inside a MRI scanner. The experimental results showed significant differences between the meditator and the controls. The most important one is that the meditator activates frontal medial cortex and precuneous regions without any visual excitation, while the controls only utilize visual cortex and precuneous regions without any frontal medial excitation. These seems suggest that for performing the same tasks, the meditator is in cognitive control state, while the controls are in spatial imagination state.

Two big recent revolutions: machine learning technologies; such as “deep learning” in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various “citizen science” projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informat-ics might contribute to happiness sciences. We introduce the “Social Intelligence Genomics and Empathy-Building Study” and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We dis-cuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing.

We have developed a visualization system of dementia care skills based on multimodal communication features. The purpose of our system is to provide effective learning of dementia care to trainees. As dementia care skills are difficult to visualize and describe, they are hard to acquire for trainees. We focus on HumanitudeR; a non-pharmacological comprehensive intervention with verbal and non-verbal communication, which is a care methodology of French-origin for the vulnerable elderlies. The multimodal methodology utilizes four techniques to relate to elderly with dementia (i.e., gaze, speak, touch, opportunities to stand on their feet). We analyzed the care videos of Humanitude instructors to extract multimodal communication features. We designed and filmed video contents demonstrating the extracted features. These have shown to be effective, in combination with practice and reflection, to acquire dementia care skills. The trainees could use the system for self-reflection and teaching.

Conference calls represent a natural but limited communication channel between people. Lack of visual contact and limited bandwidth impoverish social cues people typically use to moderate their behavior. This paper presents a system capable of providing timely aural feedback enabling meeting participants to check themselves. The system is able to sense and recognize problems, reason about them, and make decisions on how and when to provide feedback based on an interaction policy. While a hand-crafted policy based on expert insight can be used, it is non-optimal and can be brittle. Instead, we use reinforcement learning to build a system that can adapt to users by interacting with them. To evaluate the system, we first conduct a user study and demonstrate its utility in getting meeting participants to contribute more equally. We then validate the adaptive feedback policy by demonstrating the agent's ability to adapt its action choices to different types of users.

We revisit a series of studies on the work of multidisciplinary medical teams with a view to identifying opportunities for the use of intelligent systems to support their complex cooperative work, and the challenges that might arise in developing such systems. We focus specially on the activities performed during the multidisciplinary medical team meeting (MDTM) and review the literature on MDTMs, as well as our own longitudinal analysis of several MDTs in a large teaching hospital over a period of ten years.