Motor control drives visual bodily judgements

The ‘embodied cognition’ framework proposes that our motor repertoire shapes visual perception and cognition. But recent studies showing normal visual body representation in individuals born without hands challenges the contribution of motor control on visual body representation. Here, we studied hand laterality judgements in three groups with fundamentally different visual and motor hand experiences: two-handed controls, one-handers born without a hand (congenital one-handers) and one-handers with an acquired amputation (amputees). Congenital one-handers, lacking both motor and first-person visual information of their missing hand, diverged in their performance from the other groups, exhibiting more errors for their intact hand and slower reaction-times for challenging hand postures. Amputees, who have lingering non-visual motor control of their missing (phantom) hand, performed the task similarly to controls. Amputees’ reaction-times for visual laterality judgements correlated positively with their phantom hand’s motor control, such that deteriorated motor control associated with slower visual laterality judgements. Finally, we have implemented a computational simulation to describe how a mechanism that utilises a single hand representation in congenital one-handers as opposed to two in controls, could replicate our empirical results. Together, our findings demonstrate that motor control is a driver in making visual bodily judgments

Illicit Activity Detection in Large-Scale Dark and Opaque Web Social Networks

Many online chat applications live in a grey area between the legitimate web and the dark net. The Telegram network in particular can aid criminal activities. Telegram hosts “chats” which consist of varied conversations and advertisements. These chats take place among automated “bots” and human users. Classifying legitimate activity from illegitimate activity can aid law enforcement in finding criminals. Social network analysis of Telegram chats presents a difficult problem. Users can change their username or create new accounts. Users involved in criminal activity often do this to obscure their identity. This makes establishing the unique identity behind a given username challenging. Thus we explored classifying users from their language usage in their chat messages.The volume and velocity of Telegram chat data place it well within the domain of big data. Machine learning and natural language processing (NLP) tools are necessary to classify this chat data. We developed NLP tools for classifying users and the chat group to which their messages belong. We found that legitimate and illegitimate chat groups could be classified with high accuracy. We also were able to classify bots, humans, and advertisements within conversations

Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information

The Symbolic Aggregate approXimation (SAX) is a very popular symbolic dimensionality reduction technique of time series data, as it has several advantages over other dimensionality reduction techniques. One of its major advantages is its efficiency, as it uses precomputed distances. The other main advantage is that in SAX the distance measure defined on the reduced space lower bounds the distance measure defined on the original space. This enables SAX to return exact results in query-by-content tasks. Yet SAX has an inherent drawback, which is its inability to capture segment trend information. Several researchers have attempted to enhance SAX by proposing modifications to include trend information. However, this comes at the expense of giving up on one or more of the advantages of SAX. In this paper we investigate three modifications of SAX to add trend capturing ability to it. These modifications retain the same features of SAX in terms of simplicity, efficiency, as well as the exact results it returns. They are simple procedures based on a different segmentation of the time series than that used in classic-SAX. We test the performance of these three modifications on 45 time series datasets of different sizes, dimensions, and nature, on a classification task and we compare it to that of classic-SAX. The results we obtained show that one of these modifications manages to outperform classic-SAX and that another one slightly gives better results than classic-SAX.Comment: International Conference on Modeling Decisions for Artificial Intelligence - MDAI 2020: Modeling Decisions for Artificial Intelligence pp 230-23

A Theory of Composition for Differential Obliviousness

Differential obliviousness (DO) access pattern privacy is a privacy notion which guarantees that the access patterns of a program satisfy differential privacy. Differential obliviousness was studied in a sequence of recent works as a relaxation of full obliviousness. Earlier works showed that DO not only allows us to circumvent the logarithmic-overhead barrier of fully oblivious algorithms, in many cases, it also allows us to achieve polynomial speedup over full obliviousness, since it avoids padding to the worst-case behavior of fully oblivious algorithms. Despite the promises of differential obliviousness (DO), a significant barrier that hinders its broad application is the lack of composability. In particular, when we apply one DO algorithm to the output of another DO algorithm, the composed algorithm may no longer be DO (with reasonable parameters). More specifically, the outputs of the first DO algorithm on two neighboring inputs may no longer be neighboring, and thus we cannot directly benefit from the DO guarantee of the second algorithm. In this work, we are the first to explore a theory of composition for differentially oblivious algorithms. We propose a refinement of the DO notion called $(\epsilon, \delta)$-neighbor-preserving-DO, or $(\epsilon, \delta)$-NPDO for short, and we prove that our new notion indeed provides nice compositional guarantees. In this way, the algorithm designer can easily track the privacy loss when composing multiple DO algorithms. We give several example applications to showcase the power and expressiveness of our new NPDO notion. One of these examples is a result of independent interest: we use the compositional framework to prove an optimal privacy amplification theorem for the differentially oblivious shuffle model. In other words, we show that for a class of distributed differentially private mechanisms in the shuffle-model, one can replace the perfectly secure shuffler with a DO shuffler, and nonetheless enjoy almost the same privacy amplification enabled by a shuffler

Models of plastic depinning of driven disordered systems

Two classes of models of driven disordered systems that exhibit history-dependent dynamics are discussed. The first class incorporates local inertia in the dynamics via nonmonotonic stress transfer between adjacent degrees of freedom. The second class allows for proliferation of topological defects due to the interplay of strong disorder and drive. In mean field theory both models exhibit a tricritical point as a function of disorder strength. At weak disorder depinning is continuous and the sliding state is unique. At strong disorder depinning is discontinuous and hysteretic.Comment: 3 figures, invited talk at StatPhys 2

Addressing robustness in time-critical, distributed, task allocation algorithms.

The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT’s baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT’s robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT’s robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.N/

Depinning and plasticity of driven disordered lattices

We review in these notes the dynamics of extended condensed matter systesm, such as vortex lattices in type-II superconductors and charge density waves in anisotropic metals, driven over quenched disorder. We focus in particular on the case of strong disorder, where topological defects are generated in the driven lattice. In this case the repsonse is plastic and the depinning transition may become discontinuous and hysteretic.Comment: 21 pages, 6 figures. Proceedings the XIX Sitges Conference on Jamming, Yielding, and Irreversible Deformations in Condensed Matter, Sitges, Barcelona, Spain, June 14-18, 200

Deterrence in Cyberspace: An Interdisciplinary Review of the Empirical Literature

The popularity of the deterrence perspective across multiple scientific disciplines has sparked a lively debate regarding its relevance in influencing both offenders and targets in cyberspace. Unfortunately, due to the invisible borders between academic disciplines, most of the published literature on deterrence in cyberspace is confined within unique scientific disciplines. This chapter therefore provides an interdisciplinary review of the issue of deterrence in cyberspace. It begins with a short overview of the deterrence perspective, presenting the ongoing debates concerning the relevance of deterrence pillars in influencing cybercriminals’ and cyberattackers’ operations in cyberspace. It then reviews the existing scientific evidence assessing various aspects of deterrence in the context of several disciplines: criminology, law, information systems, and political science. This chapter ends with a few policy implications and proposed directions for future interdisciplinary academic research

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals