Instantaneous threat detection based on a semantic representation of activities, zones and trajectories

Threat detection is a challenging problem, because threats appear in many variations and differences to normal behaviour can be very subtle. In this paper, we consider threats on a parking lot, where theft of a truck’s cargo occurs. The threats range from explicit, e.g. a person attacking the truck driver, to implicit, e.g. somebody loitering and then fiddling with the exterior of the truck in order to open it. Our goal is a system that is able to recognize a threat instantaneously as they develop. Typical observables of the threats are a person’s activity, presence in a particular zone and the trajectory. The novelty of this paper is an encoding of these threat observables in a semantic, intermediate-level representation, based on low-level visual features that have no intrinsic semantic meaning themselves. The aim of this representation was to bridge the semantic gap between the low-level tracks and motion and the higher-level notion of threats. In our experiments, we demonstrate that our semantic representation is more descriptive for threat detection than directly using low-level features. We find that a person’s activities are the most important elements of this semantic representation, followed by the person’s trajectory. The proposed threat detection system is very accurate: 96.6 % of the tracks are correctly interpreted, when considering the temporal context.

The error of representation: basic understanding

Representation error arises from the inability of the forecast model to accurately simulate the climatology of the truth. We present a rigorous framework for understanding this kind of error of representation. This framework shows that the lack of an inverse in the relationship between the true climatology (true attractor) and the forecast climatology (forecast attractor) leads to the error of representation. A new gain matrix for the data assimilation problem is derived that illustrates the proper approaches one may take to perform Bayesian data assimilation when the observations are of states on one attractor but the forecast model resides on another. This new data assimilation algorithm is the optimal scheme for the situation where the distributions on the true attractor and the forecast attractors are separately Gaussian and there exists a linear map between them. The results of this theory are illustrated in a simple Gaussian multivariate model.

The representation of the West-African Monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat

Weather and climate model simulations of the West African Monsoon (WAM) have generally poor representation of the rainfall distribution and monsoon circulation because key processes, such as clouds and convection, are poorly characterized. The vertical distribution of cloud and precipitation during the WAM are evaluated in Met Office Unified Model simulations against CloudSat observations. Simulations were run at 40-km and 12-km horizontal grid length using a convection parameterization scheme and at 12-km, 4-km, and 1.5-km grid length with the convection scheme effectively switched off, to study the impact of model resolution and convection parameterization scheme on the organisation of tropical convection. Radar reflectivity is forward-modelled from the model cloud fields using the CloudSat simulator to present a like-with-like comparison with the CloudSat radar observations. The representation of cloud and precipitation at 12-km horizontal grid length improves dramatically when the convection parameterization is switched off, primarily because of a reduction in daytime (moist) convection. Further improvement is obtained when reducing model grid length to 4 km or 1.5 km, especially in the representation of thin anvil and mid-level cloud, but three issues remain in all model configurations. Firstly, all simulations underestimate the fraction of anvils with cloud top height above 12 km, which can be attributed to too low ice water contents in the model compared to satellite retrievals. Secondly, the model consistently detrains mid-level cloud too close to the freezing level, compared to higher altitudes in CloudSat observations. Finally, there is too much low-level cloud cover in all simulations and this bias was not improved when adjusting the rainfall parameters in the microphysics scheme. To improve model simulations of the WAM, more detailed and in-situ observations of the dynamics and microphysics targeting these non-precipitating cloud types are required.

Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface

Methods to explicitly represent uncertainties in weather and climate models have been developed and refined over the past decade, and have reduced biases and improved forecast skill when implemented in the atmospheric component of models. These methods have not yet been applied to the land surface component of models. Since the land surface is strongly coupled to the atmospheric state at certain times and in certain places (such as the European summer of 2003), improvements in the representation of land surface uncertainty may potentially lead to improvements in atmospheric forecasts for such events. Here we analyse seasonal retrospective forecasts for 1981–2012 performed with the European Centre for Medium-Range Weather Forecasts’ (ECMWF) coupled ensemble forecast model. We consider two methods of incorporating uncertainty into the land surface model (H-TESSEL): stochastic perturbation of tendencies, and static perturbation of key soil parameters. We find that the perturbed parameter approach considerably improves the forecast of extreme air temperature for summer 2003, through better representation of negative soil moisture anomalies and upward sensible heat flux. Averaged across all the reforecasts the perturbed parameter experiment shows relatively little impact on the mean bias, suggesting perturbations of at least this magnitude can be applied to the land surface without any degradation of model climate. There is also little impact on skill averaged across all reforecasts and some evidence of overdispersion for soil moisture. The stochastic tendency experiments show a large overdispersion for the soil temperature fields, indicating that the perturbation here is too strong. There is also some indication that the forecast of the 2003 warm event is improved for the stochastic experiments, however the improvement is not as large as observed for the perturbed parameter experiment.

Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

Breaching the social contract: crises of democratic representation and patterns of extreme right party support

Why has the extreme right Greek Golden Dawn, a party with clear links to fascism experienced a rise defying all theories that claim that such a party is unlikely to win in post-WWII Europe? And, if we accept that economic crisis is an explanation for this, why has such a phenomenon not occurred in other countries that have similar conducive conditions, such as Portugal and Spain? This article addresses this puzzle by (a) carrying out a controlled comparison of Greece, Portugal and Spain and (b) showing that the rise of the extreme right is not a question of intensity of economic crisis. Rather it is the nature of the crisis, i.e. economic versus overall crisis of democratic representation that facilitates the rise of the extreme right. We argue that extreme right parties are more likely to experience an increase in their support when economic crisis culminates into an overall crisis of democratic representation. Economic crisis is likely to become a political crisis when severe issues of governability impact upon the ability of the state to fulfil its social contract obligations. This breach of the social contract is accompanied by declining levels of trust in state institutions, resulting in party system collapse.

Molecular targets of cannabidiol in neurological disorders

Cannabis has a long history of anecdotal medicinal use and limited licensed medicinal use. Until recently, alleged clinical effects from anecdotal reports and the use of licensed cannabinoid medicines are most likely mediated by tetrahydrocannabinol by virtue of: 1) this cannabinoid being present in the most significant quantities in these preparations; and b) the proportion:potency relationship between tetrahydrocannabinol and other plant cannabinoids derived from cannabis. However, there has recently been considerable interest in the therapeutic potential for the plant cannabinoid, cannabidiol (CBD), in neurological disorders but the current evidence suggests that CBD does not directly interact with the endocannabinoid system except in vitro at supraphysiological concentrations. Thus, as further evidence for CBD’s beneficial effects in neurological disease emerges, there remains an urgent need to establish the molecular targets through which it exerts its therapeutic effects. Here, we conducted a systematic search of the extant literature for original articles describing the molecular phar- macology of CBD. We critically appraised the results for the validity of the molecular targets proposed. Thereafter, we considered whether the molecular targets of CBD identified hold therapeutic potential in relevant neurological diseases. The molecular targets identified include numerous classical ion channels, receptors, transporters, and enzymes. Some CBD effects at these targets in in vitro assays only manifest at high concentrations, which may be difficult to achieve in vivo, particularly given CBD’s relatively poor bioavailability. Moreover, several targets were asserted through experimental designs that demonstrate only correlation with a given target rather than a causal proof. When the molecular targets of CBD that were physiologically plausible were considered for their potential for exploitation in neurological therapeu- tics, the results were variable. In some cases, the targets identified had little or no established link to the diseases considered. In others, molecular targets of CBD were entirely consistent with those already actively exploited in relevant, clinically used, neurological treatments. Finally, CBD was found to act upon a number of targets that are linked to neurological therapeutics but that its actions were not consistent with modulation of such targets that would derive a therapeutically beneficial outcome. Overall, we find that while >65 discrete molecular targets have been reported in the literature for CBD, a relatively limited number represent plausible targets for the drug’s action in neurological disorders when judged by the criteria we set. We conclude that CBD is very unlikely to exert effects in neurological diseases through modulation of the endocannabinoid system. Moreover, a number of other molecular targets of CBD reported in the literature are unlikely to be of relevance owing to effects only being observed at supraphysiological concentrations. Of interest and after excluding unlikely and implausible targets, the remaining molecular targets of CBD with plausible evidence for involvement in therapeutic effects in neurological disorders (e.g., voltage-dependent anion channel 1, G protein-coupled receptor 55, CaV3.x, etc.) are associated with either the regulation of, or responses to changes in, intracellular calcium levels. While no causal proof yet exists for CBD’s effects at these targets, they represent the most probable for such investigations and should be prioritized in further studies of CBD’s therapeutic mechanism of action.

Nitrosopersulfide (SSNO-) targets soluble guanylyl cyclase and induces vasodilation in vivo

Background Recent experimental evidence suggests that nitric oxide (NO) and hydrogen sulfide signaling pathways are intimately intertwined particularly in the vasculature, with mutual attenuation or potentiation of biological responses under control of the soluble guanylyl cyclase (sGC) / phopshodiesterase (PDE) pathway. There is now compelling evidence that part of the NO/sulfide cross talk has a chemical foundation via the formation of S/N-hybrid molecules including thionitrous acid (HSNO) and nitrosopersulfde (SSNO-). The aim of this study was to characterize the bioactive products of the interaction between sulfide and NO metabolites targeting sGC that may potentially regulate vasodilation. Results We found that the chemical interaction of sulfide with NO or nitrosothiols leads to formation of S/N-hybrid metabolites including SSNO- via intermediate formation of HSNO. Contrary to a recent report in the literature but consistent with the transient nature of HSNO, its formation was not detectable by high-resolution mass spectrometry under physiologically relevant conditions. SSNO- is also formed in non-aqueous media by the reaction of nitrite with oxidized sulfur species including colloidal sulfur and polysulfides. SSNO- is stable in the presence of high concentrations of thiols, release NO, and activates sGC in RFL-6 cells in an NO-dependent fashion. Moreover, SSNO- is a potent vasodilator in aortic rings in vitro and lowers blood pressure in rats in vivo. The presence of high concentrations of SOD or thiols does not affect SSNO- mediated sGC activation, while it potentiates and inhibits the effects of the nitroxyl (HNO) donor Angeli's salt, suggesting that HNO release from SSNO- is not involved in sGC activation. Conclusion The reaction between NO and sulfide leads to fomation of S/N-hybrid molecules including SSNO-, releasing NO, activating sGC and inducing vasodilation. SSNO- is considerably more stable than HSNO at pH 7.4 and thus a more likely biological mediator that can account for the chemical cross-talk between NO and sulfide.

Low rank representation on Riemannian manifold of symmetric positive definite matrices

Sparse coding aims to find a more compact representation based on a set of dictionary atoms. A well-known technique looking at 2D sparsity is the low rank representation (LRR). However, in many computer vision applications, data often originate from a manifold, which is equipped with some Riemannian geometry. In this case, the existing LRR becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to applications. In this paper, we generalize the LRR over the Euclidean space to the LRR model over a specific Rimannian manifold—the manifold of symmetric positive matrices (SPD). Experiments on several computer vision datasets showcase its noise robustness and superior performance on classification and segmentation compared with state-of-the-art approaches.

Reply to: punishing food: what brain activity can tell us about the representation of food in recovered anorexia nervosa

Eating disorders are characterized by aberrant cognitions and behaviors around food. We used a novel functional magnetic resonance imaging task in a sample of recovered anorexia nervosa subjects to study the neural response to both pleasant and aversive food tastes and pictures compared with a group of matched female subjects who had never had the disorder. We report that individuals recovered from anorexia nervosa have an increased neural response to rewarding and aversive food stimuli, in the form of chocolate (e.g., in the ventral striatum) and moldy strawberries (e.g., in the caudate).

ENSO representation in climate models: from CMIP3 to CMIP5

We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.