Parameterized complexity of the MINCCA problem on graphs of bounded decomposability

In an edge-colored graph, the cost incurred at a vertex on a path when two incident edges with different colors are traversed is called reload or changeover cost. The "Minimum Changeover Cost Arborescence" (MINCCA) problem consists in finding an arborescence with a given root vertex such that the total changeover cost of the internal vertices is minimized. It has been recently proved by G\"oz\"upek et al. [TCS 2016] that the problem is FPT when parameterized by the treewidth and the maximum degree of the input graph. In this article we present the following results for the MINCCA problem: - the problem is W[1]-hard parameterized by the treedepth of the input graph, even on graphs of average degree at most 8. In particular, it is W[1]-hard parameterized by the treewidth of the input graph, which answers the main open problem of G\"oz\"upek et al. [TCS 2016]; - it is W[1]-hard on multigraphs parameterized by the tree-cutwidth of the input multigraph; - it is FPT parameterized by the star tree-cutwidth of the input graph, which is a slightly restricted version of tree-cutwidth. This result strictly generalizes the FPT result given in G\"oz\"upek et al. [TCS 2016]; - it remains NP-hard on planar graphs even when restricted to instances with at most 6 colors and 0/1 symmetric costs, or when restricted to instances with at most 8 colors, maximum degree bounded by 4, and 0/1 symmetric costs.Comment: 25 pages, 11 figure

The chaining lemma and its application

We present a new information-theoretic result which we call the Chaining Lemma. It considers a so-called “chain” of random variables, defined by a source distribution X(0)with high min-entropy and a number (say, t in total) of arbitrary functions (T1,…, Tt) which are applied in succession to that source to generate the chain (Formula presented). Intuitively, the Chaining Lemma guarantees that, if the chain is not too long, then either (i) the entire chain is “highly random”, in that every variable has high min-entropy; or (ii) it is possible to find a point j (1 ≤ j ≤ t) in the chain such that, conditioned on the end of the chain i.e. (Formula presented), the preceding part (Formula presented) remains highly random. We think this is an interesting information-theoretic result which is intuitive but nevertheless requires rigorous case-analysis to prove. We believe that the above lemma will find applications in cryptography. We give an example of this, namely we show an application of the lemma to protect essentially any cryptographic scheme against memory tampering attacks. We allow several tampering requests, the tampering functions can be arbitrary, however, they must be chosen from a bounded size set of functions that is fixed a prior

Neural computations of threat in the aftermath of combat trauma

© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. By combining computational, morphological, and functional analyses, this study relates latent markers of associative threat learning to overt post-traumatic stress disorder (PTSD) symptoms in combat veterans. Using reversal learning, we found that symptomatic veterans showed greater physiological adjustment to cues that did not predict what they had expected, indicating greater sensitivity to prediction errors for negative outcomes. This exaggerated weighting of prediction errors shapes the dynamic learning rate (associability) and value of threat predictive cues. The degree to which the striatum tracked the associability partially mediated the positive correlation between prediction-error weights and PTSD symptoms, suggesting that both increased prediction-error weights and decreased striatal tracking of associability independently contribute to PTSD symptoms. Furthermore, decreased neural tracking of value in the amygdala, in addition to smaller amygdala volume, independently corresponded to higher PTSD symptom severity. These results provide evidence for distinct neurocomputational contributions to PTSD symptoms

Changes in mental health among U.S. military veterans during the COVID-19 pandemic: A network analysis

Increases of symptoms of posttraumatic stress disorder (PTSD), anxiety and depression have been observed among individuals exposed to potentially traumatic events in the first months of the COVID-19 pandemic. Similarly, associations among different aspects of mental health, such as symptoms of PTSD and suicidal ideation, have also been documented. However, studies including an assessment prior to the onset and during the height of the pandemic are lacking. We investigated changes in symptoms of PTSD, depression, anxiety, suicidal ideation, and posttraumatic growth in a population-based sample of 1232 U.S. military veterans who experienced a potentially traumatic event during the first year of the pandemic. Symptoms were assessed prior to (fall/winter 2019) and one year into the pandemic (fall/winter 2020). We compared changes in symptom interrelations using network analysis, and assessed their associations with pandemic-related PTSD and posttraumatic growth symptoms. A subtle increase in psychopathological symptoms and a decrease in posttraumatic growth was observed one year into the pandemic. The peripandemic network was more densely connected, and pandemic-related PTSD symptoms were positively associated with age, anxiety, worst-event PTSD symptoms, and pandemic-related posttraumatic growth. Our findings highlight the resilience of veterans exposed to a potentially traumatic event during the first year of a pandemic. Similarly, the networks did not fundamentally change from prepandemic to one year into the pandemic. Despite this relative stability on a group level, individual reactions to potentially traumatic events could have varied substantially. Clinicians should individualize their assessments but be aware of the general resilience of most veterans

Predictable arguments of knowledge

We initiate a formal investigation on the power of predictability for argument of knowledge systems for NP. Specifically, we consider private-coin argument systems where the answer of the prover can be predicted, given the private randomness of the verifier; we call such protocols Predictable Arguments of Knowledge (PAoK). Our study encompasses a full characterization of PAoK, showing that such arguments can be made extremely laconic, with the prover sending a single bit, and assumed to have only one round (i.e., two messages) of communication without loss of generality. We additionally explore PAoK satisfying additional properties (including zero-knowledge and the possibility of re-using the same challenge across multiple executions with the prover), present several constructions of PAoK relying on different cryptographic tools, and discuss applications to cryptography

Tracing a phase transition with fluctuations of the largest fragment size: Statistical multifragmentation models and the ALADIN S254 data

A phase transition signature associated with cumulants of the largest fragment size distribution has been identified in statistical multifragmentation models and examined in analysis of the ALADIN S254 data on fragmentation of neutron-poor and neutron-rich projectiles. Characteristics of the transition point indicated by this signature are weakly dependent on the A/Z ratio of the fragmenting spectator source. In particular, chemical freeze-out temperatures are estimated within the range 5.9 to 6.5 MeV. The experimental results are well reproduced by the SMM model.Comment: 7 pages, 3 figures, Proceedings of the International Workshop on Multifragmentation and Related Topics (IWM2009), Catania, Italy, November 2009

Tropical biogeomorphic seagrass landscapes for coastal protection:Persistence and wave attenuation during major storms events

The intensity of major storm events generated within the Atlantic Basin is projected to rise with the warming of the oceans, which is likely to exacerbate coastal erosion. Nature-based flood defence has been proposed as a sustainable and effective solution to protect coastlines. However, the ability of natural ecosystems to withstand major storms like tropical hurricanes has yet to be thoroughly tested. Seagrass meadows both stabilise sediment and attenuate waves, providing effective coastal protection services for sandy beaches. To examine the tolerance of Caribbean seagrass meadows to extreme storm events, and to investigate the extent of protection they deliver to beaches, we employed a combination of field surveys, biomechanical measurements and wave modelling simulations. Field surveys of sea- grass meadows before and after a direct hit by the category 5 Hurricane Irma documented that estab- lished seagrass meadows of Thalassia testudinum re- mained unaltered after the extreme storm event. The flexible leaves and thalli of seagrass and calci- fying macroalgae inhabiting the meadows were shown to sustain the wave forces that they are likely to experience during hurricanes. In addition, the seagrass canopy and the complex biogeomorphic landscape built by the seagrass meadows combine to significantly dissipate extreme wave forces, ensuring that erosion is minimised within sandy beach fore- shores. The persistence of the Caribbean seagrass meadows and their coastal protection services dur- ing extreme storm events ensures that a stable coastal ecosystem and beach foreshore is maintained in tropical regions

Elevated Brain Cannabinoid CB1 Receptor Availability in Posttraumatic Stress Disorder: A Positron Emission Tomography Study

Endocannabinoids and their attending cannabinoid type 1 receptor (CB1) have been implicated in animal models of posttraumatic stress disorder (PTSD). However, their specific role has not been studied in people with PTSD. Herein, we present an in vivo imaging study using positron emission tomography (PET) and the CB1-selective radioligand [11C]OMAR in individuals with PTSD, and healthy controls with lifetime histories of trauma (trauma controls [TC]) and those without such histories (healthy controls [HC]). Untreated individuals with PTSD (N=25) with non-combat trauma histories, and TC (N=12) and HC (N=23) participated in a magnetic resonance (MR) imaging scan and a resting PET scan with the CB1 receptor antagonist radiotracer [11C]OMAR, which measures volume of distribution (VT) linearly related to CB1 receptor availability. Peripheral levels of anandamide, 2-arachidonoylglycerol (2-AG), oleoylethanolamide (OEA), palmitoylethanolamide (PEA), and cortisol were also assessed. In the PTSD group, relative to the HC and TC groups, we found elevated brain-wide [11C]OMAR VT values (F(2,53)=7.96, p=.001; 19.5% and 14.5% higher, respectively) which were most pronounced in women (F(1,53)=5.52, p=.023). Anandamide concentrations were reduced in the PTSD relative to the TC (53.1% lower) and HC (58.2% lower) groups. Cortisol levels were lower in the PTSD and TC groups relative to the HC group. Three biomarkers examined collectively—OMAR VT, anandamide, and cortisol—correctly classified nearly 85% of PTSD cases. These results suggest that abnormal CB1 receptor-mediated anandamide signaling is implicated in the etiology of PTSD, and provide a promising neurobiological model to develop novel, evidence-based pharmacotherapies for this disorder

Factor structure of PTSD, and relation with gender in trauma survivors from India

Background: The factor structure of posttraumatic stress disorder (PTSD) has been extensively studied in Western countries. Some studies have assessed its factor structure in Asia (China, Sri Lanka, and Malaysia), but few have directly assessed the factor structure of PTSD in an Indian adult sample. Furthermore, in a largely patriarchal society in India with strong gender roles, it becomes imperative to assess the association between the factors of PTSD and gender. Objective: The purpose of the present study was to assess the factor structure of PTSD in an Indian sample of trauma survivors based on prevailing models of PTSD defined in the DSM-IV-TR (APA, 2000), and to assess the relation between PTSD factors and gender. Method: The sample comprised of 313 participants (55.9% female) from Jammu and Kashmir, India, who had experienced a natural disaster (N=200) or displacement due to cross-border firing (N=113). Results: Three existing PTSD models—two four-factor models (Emotional Numbing and Dysphoria), and a five-factor model (Dysphoric Arousal)—were tested using Confirmatory Factor Analysis with addition of gender as a covariate. The three competing models had similar fit indices although the Dysphoric Arousal model fit significantly better than Emotional Numbing and Dysphoria models. Gender differences were found across the factors of Re-experiencing and Anxious arousal. Conclusions: Findings indicate that the Dysphoric Arousal model of PTSD was the best model; albeit the fit indices of all models were fairly similar. Compared to males, females scored higher on factors of Re-experiencing and Anxious arousal. Gender differences found across two factors of PTSD are discussed in light of the social milieu in India