The Compression Optimality of Asymmetric Numeral Systems.

Source coding has a rich and long history. However, a recent explosion of multimedia Internet applications (such as teleconferencing and video streaming, for instance) renews interest in fast compression that also squeezes out as much redundancy as possible. In 2009 Jarek Duda invented his asymmetric numeral system (ANS). Apart from having a beautiful mathematical structure, it is very efficient and offers compression with a very low coding redundancy. ANS works well for any symbol source statistics, and it has become a preferred compression algorithm in the IT industry. However, designing an ANS instance requires a random selection of its symbol spread function. Consequently, each ANS instance offers compression with a slightly different compression ratio. The paper investigates the compression optimality of ANS. It shows that ANS is optimal for any symbol sources whose probability distribution is described by natural powers of 1/2. We use Markov chains to calculate ANS state probabilities. This allows us to precisely determine the ANS compression rate. We present two algorithms for finding ANS instances with a high compression ratio. The first explores state probability approximations in order to choose ANS instances with better compression ratios. The second algorithm is a probabilistic one. It finds ANS instances whose compression ratios can be made as close to the best ratio as required. This is done at the expense of the number θ of internal random "coin" tosses. The algorithm complexity is O(θL3), where L is the number of ANS states. The complexity can be reduced to O(θLlog2L) if we use a fast matrix inversion. If the algorithm is implemented on a quantum computer, its complexity becomes O(θ(log2L)3).

Exponentially Decreasing Critical Detection Efficiency for Any Bell Inequality.

We address the problem of closing the detection efficiency loophole in Bell experiments, which is crucial for real-world applications. Every Bell inequality has a critical detection efficiency η that must be surpassed to avoid the detection loophole. Here, we propose a general method for reducing the critical detection efficiency of any Bell inequality to arbitrary low values. This is accomplished by entangling two particles in N orthogonal subspaces (e.g., N degrees of freedom) and conducting N Bell tests in parallel. Furthermore, the proposed method is based on the introduction of penalized N-product (PNP) Bell inequalities, for which the so-called simultaneous measurement loophole is closed, and the maximum value for local hidden-variable theories is simply the Nth power of the one of the Bell inequality initially considered. We show that, for the PNP Bell inequalities, the critical detection efficiency decays exponentially with N. The strength of our method is illustrated with a detailed study of the PNP Bell inequalities resulting from the Clauser-Horne-Shimony-Holt inequality.

Mapping the complexity of dementia: factors influencing cognitive function at the onset of dementia.

BACKGROUND: Dementia is a multi-factorial condition rather than a natural and inevitable consequence of ageing. Some factors related to dementia have been studied much more extensively than others. To gain an overview of known or suspected influential factors is a prerequisite to design studies that aim to identify causal relationships and interactions between factors. This article aims to develop a visual model that a) identifies factors related to cognitive decline that signal the onset of dementia, b) structures them by different domains and c) reflects on and visualizes the possible causal links and interactions between these factors based on expert input using a causal loop diagram. METHOD: We used a mixed-method, step-wise approach: 1. A systematic literature review on factors related to cognitive decline; 2. A group model building (GMB) workshop with experts from different disciplines; 3. Structured discussions within the group of researchers. The results were continuously synthesized and graphically transformed into a causal loop diagram. RESULTS: The causal loop diagram comprises 73 factors that were structured into six domains: physical (medical) factors (23), social health factors (21), psychological factors (14), environmental factors (5), demographic factors (5) and lifestyle factors (3). 57 factors were identified in the systematic literature review, additionally 16 factors, mostly of the social health cluster, were identified during the GMB session and the feedback rounds. CONCLUSION: The causal loop diagram offers a comprehensive visualisation of factors related to cognitive decline and their interactions. It supports the generation of hypotheses on causal relationships and interactions of factors within and between domains.

Information Causality without Concatenation.

Information causality is a physical principle which states that the amount of randomly accessible data over a classical communication channel cannot exceed its capacity, even if the sender and the receiver have access to a source of nonlocal correlations. This principle can be used to bound the nonlocality of quantum mechanics without resorting to its full formalism, with a notable example of reproducing the Tsirelson's bound of the Clauser-Horne-Shimony-Holt inequality. Despite being promising, the latter result found little generalization to other Bell inequalities because of the limitations imposed by the process of concatenation, in which several nonsignaling resources are put together to produce tighter bounds. In this work, we show that concatenation can be successfully replaced by limits on the communication channel capacity. It allows us to rederive and, in some cases, significantly improve all the previously known results in a simpler manner and apply the information causality principle to previously unapproachable Bell scenarios.

Entropy in Foundations of Quantum Physics.

Entropy can be used in studies on foundations of quantum physics in many different ways, each of them using different properties of this mathematical object [...].

Connections between Mutually Unbiased Bases and Quantum Random Access Codes.

We present a new quantum communication complexity protocol, the promise-quantum random access code, which allows us to introduce a new measure of unbiasedness for bases of Hilbert spaces. The proposed measure possesses a clear operational meaning and can be used to investigate whether a specific number of mutually unbiased bases exist in a given dimension by employing semidefinite programming techniques.

Certifying an Irreducible 1024-Dimensional Photonic State Using Refined Dimension Witnesses.

We report on a new class of dimension witnesses, based on quantum random access codes, which are a function of the recorded statistics and that have different bounds for all possible decompositions of a high-dimensional physical system. Thus, it certifies the dimension of the system and has the new distinct feature of identifying whether the high-dimensional system is decomposable in terms of lower dimensional subsystems. To demonstrate the practicability of this technique, we used it to experimentally certify the generation of an irreducible 1024-dimensional photonic quantum state. Therefore, certifying that the state is not multipartite or encoded using noncoupled different degrees of freedom of a single photon. Our protocol should find applications in a broad class of modern quantum information experiments addressing the generation of high-dimensional quantum systems, where quantum tomography may become intractable.

On the security of semi-device-independent QKD protocols.

While fully device-independent security in (BB84-like) prepare-and-measure quantum key distribution (QKD) is impossible, it can be guaranteed against individual attacks in a semi-device-independent (SDI) scenario, wherein no assumptions are made on the characteristics of the hardware used except for an upper bound on the dimension of the communicated system. Studying security under such minimal assumptions is especially relevant in the context of the recent quantum hacking attacks wherein the eavesdroppers can not only construct the devices used by the communicating parties but are also able to remotely alter their behavior. In this work, we study the security of a SDIQKD protocol based on the prepare-and-measure quantum implementation of a well-known cryptographic primitive, the random access code (RAC). We consider imperfect detectors and establish the critical values of the security parameters (the observed success probability of the RAC and the detection efficiency) required for guaranteeing security against eavesdroppers with and without quantum memory. Furthermore, we suggest a minimal characterization of the preparation device in order to lower the requirements for establishing a secure key.

MQAPsingle: A quasi single-model approach for estimation of the quality of individual protein structure models.

We present a Model Quality Assessment Program (MQAP), called MQAPsingle, for ranking and assessing the absolute global quality of single protein models. MQAPsingle is quasi single-model MQAP, a method that combines advantages of both "pure" single-model MQAPs and clustering MQAPs. This approach results in higher accuracy compared to the state-of-the-art single-model MQAPs. Notably, the prediction for a given model is the same regardless if this model is submitted to our server alone or together with other models. Proteins 2016; 84:1021-1028. © 2015 Wiley Periodicals, Inc.

Information causality as a physical principle.

Quantum physics has remarkable distinguishing characteristics. For example, it gives only probabilistic predictions (non-determinism) and does not allow copying of unknown states (no-cloning). Quantum correlations may be stronger than any classical ones, but information cannot be transmitted faster than light (no-signalling). However, these features do not uniquely define quantum physics. A broad class of theories exist that share such traits and allow even stronger (than quantum) correlations. Here we introduce the principle of 'information causality' and show that it is respected by classical and quantum physics but violated by all no-signalling theories with stronger than (the strongest) quantum correlations. The principle relates to the amount of information that an observer (Bob) can gain about a data set belonging to another observer (Alice), the contents of which are completely unknown to him. Using all his local resources (which may be correlated with her resources) and allowing classical communication from her, the amount of information that Bob can recover is bounded by the information volume (m) of the communication. Namely, if Alice communicates m bits to Bob, the total information obtainable by Bob cannot be greater than m. For m = 0, information causality reduces to the standard no-signalling principle. However, no-signalling theories with maximally strong correlations would allow Bob access to all the data in any m-bit subset of the whole data set held by Alice. If only one bit is sent by Alice (m = 1), this is tantamount to Bob's being able to access the value of any single bit of Alice's data (but not all of them). Information causality may therefore help to distinguish physical theories from non-physical ones. We suggest that information causality-a generalization of the no-signalling condition-might be one of the foundational properties of nature.

QA-RecombineIt: a server for quality assessment and recombination of protein models.

QA-RecombineIt provides a web interface to assess the quality of protein 3D structure models and to improve the accuracy of models by merging fragments of multiple input models. QA-RecombineIt has been developed for protein modelers who are working on difficult problems, have a set of different homology models and/or de novo models (from methods such as I-TASSER or ROSETTA) and would like to obtain one consensus model that incorporates the best parts into one structure that is internally coherent. An advanced mode is also available, in which one can modify the operation of the fragment recombination algorithm by manually identifying individual fragments or entire models to recombine. Our method produces up to 100 models that are expected to be on the average more accurate than the starting models. Therefore, our server may be useful for crystallographic protein structure determination, where protein models are used for Molecular Replacement to solve the phase problem. To address the latter possibility, a special feature was added to the QA-RecombineIt server. The QA-RecombineIt server can be freely accessed at http://iimcb.genesilico.pl/qarecombineit/.

Harvesting entropy for random number generation for internet of things constrained devices using on-board sensors.

Entropy in computer security is associated with the unpredictability of a source of randomness. The random source with high entropy tends to achieve a uniform distribution of random values. Random number generators are one of the most important building blocks of cryptosystems. In constrained devices of the Internet of Things ecosystem, high entropy random number generators are hard to achieve due to hardware limitations. For the purpose of the random number generation in constrained devices, this work proposes a solution based on the least-significant bits concatenation entropy harvesting method. As a potential source of entropy, on-board integrated sensors (i.e., temperature, humidity and two different light sensors) have been analyzed. Additionally, the costs (i.e., time and memory consumption) of the presented approach have been measured. The results obtained from the proposed method with statistical fine tuning achieved a Shannon entropy of around 7.9 bits per byte of data for temperature and humidity sensors. The results showed that sensor-based random number generators are a valuable source of entropy with very small RAM and Flash memory requirements for constrained devices of the Internet of Things.

Optimized ECC Implementation for Secure Communication between Heterogeneous IoT Devices.

The Internet of Things is integrating information systems, places, users and billions of constrained devices into one global network. This network requires secure and private means of communications. The building blocks of the Internet of Things are devices manufactured by various producers and are designed to fulfil different needs. There would be no common hardware platform that could be applied in every scenario. In such a heterogeneous environment, there is a strong need for the optimization of interoperable security. We present optimized elliptic curve Cryptography algorithms that address the security issues in the heterogeneous IoT networks. We have combined cryptographic algorithms for the NXP/Jennic 5148- and MSP430-based IoT devices and used them to created novel key negotiation protocol.

When are Popescu-Rohrlich boxes and random access codes equivalent?

We study a problem of interconvertibility of two supraquantum resources: one is the so-called Popescu-Rohrlich (PR) box, which violates Clauser-Horne-Shimony-Holt inequality up to the maximal algebraic bound, and the second is the so-called random access code (RAC). The latter is a functionality that enables Bob (receiver) to choose one of two bits of Alice. It is known that a PR box supplemented with one bit of communication can be used to simulate a RAC. We ask the converse question: to what extent can a RAC can simulate a PR box? To this end, we introduce a "racbox": a box such that when it is supplemented with one bit of communication it offers a RAC. As said, a PR box can simulate a racbox. The question we raise is whether any racbox can simulate a PR box. We show that a nonsignaling racbox, indeed, can simulate a PR box; hence, these two resources are equivalent. We also provide an example of a signaling racbox that cannot simulate a PR box. We give a resource inequality between racboxes and PR boxes and show that it is saturated.

Experimental tests of classical and quantum dimensionality.

We report on an experimental test of classical and quantum dimension. We have used a dimension witness that can distinguish between quantum and classical systems of dimensions two, three, and four and performed the experiment for all five cases. The witness we have chosen is a base of semi-device-independent cryptographic and randomness expansion protocols. Therefore, the part of the experiment in which qubits were used is a realization of these protocols. In our work we also present an analytic method for finding the maximum quantum value of the witness along with corresponding measurements and preparations. This method is quite general and can be applied to any linear dimension witness.

MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria.

Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. The experimental determination of subcellular localization is laborious. Thus, a number of computational methods have been developed to predict the protein location from sequence. However predictions made by different methods often disagree with each other and it is not always clear which algorithm performs best for the given cellular compartment. We benchmarked primary subcellular localization predictors for proteins from Gram-negative bacteria, PSORTb3, PSLpred, CELLO, and SOSUI-GramN, on a common dataset that included 1056 proteins. We found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This motivated us to develop a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. MetaLocGramN runs the primary methods, and based on their output classifies protein sequences into one of five major localizations of the Gram-negative bacterial cell: cytoplasm, plasma membrane, periplasm, outer membrane, and extracellular space. MetaLocGramN achieves the average Matthews correlation coefficient of 0.806, i.e. 12% better than the best individual primary method. MetaLocGramN is a meta-predictor specialized in predicting subcellular localization for proteins from Gram-negative bacteria. According to our benchmark, it performs better than all other tools run independently. MetaLocGramN is a web and SOAP server available for free use by all academic users at the URL http://iimcb.genesilico.pl/MetaLocGramN. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Psychological burden and disease-related quality of life in dialysis patients during the first wave of the COVID-19 pandemic - a cross-sectional observational study. / Obciazenie psychiczne a jakosc zycia zwiazana z choroba u pacjentów dializowanych podczas pierwszej fali pandemii COVID-19 - przekrojowe badanie obserwacyjne.

OBJECTIVES: Since the first reports of the spread of the new SARS-CoV-2 virus, experts have pointed to the possible psychological consequences of the pandemic. In this study, we tried to answer the question of whether the level of perceived stress related to the pandemic affects the quality of life related to the disease and the functioning of patients on peritoneal and hemodialysis. METHODS: Out of 106 patients from the dialysis center of the University Clinical Hospital in Wroclaw during the first wave of the pandemic, 73 patients were enrolled, including 61 hemodialysis (HD) and 12 peritoneal dialysis (PD). The study used The Perceived Stress Scale (PSS), The General Health Questionnaire (GHQ-28), The Impact of Events Scale-Revised (IES-R), and The Kidney Disease and Quality of Life (KDQOL-SF™). RESULTS: Nearly half of the respondents (48%) experienced psychological distress and 5.6% of the respondents showed clinically significant psychopathological symptoms (GHQ). Half of the study group declared a significant occurrence of post-traumatic stress symptoms (IES-R). A high score of subjectively perceived stress related to the pandemic was observed in both study groups. Numerous significant negative correlations were found between the results of the KDQOL subscales and psychopathological symptoms (IES-R and GHQ) without significant differences between the two groups (HD vs. PD). Almost all KDQOL subscales were significantly moderately or strongly correlated with the level of perceived stress related to the pandemic. CONCLUSIONS: The level of subjectively assessed stress related to the pandemic and the severity of psychopathological symptoms, including post-traumatic stress, were significant in the entire group of patients undergoing renal replacement therapy, regardless of the dialysis type. Numerous confirmed relationships between the domains of the quality of life related to the disease and the level of perceived stress and psychopathological symptoms indicate an urgent need to provide effective psychological support to this group of patients and to develop preventive programs in the field of mental health of people undergoing renal replacement therapy.

Coping of Chronically-Ill Patients during the COVID-19 Pandemic: Comparison between Four Groups.

In many countries, the COVID-19 pandemic led to healthcare reorganization limiting access to diagnostic or therapeutic procedures for chronically-ill patients. In this article, we describe the psychological consequences and coping strategies of several groups of chronically-ill patients. During the cross-sectional survey conducted in 2020, we enrolled 398 patients with four different chronic conditions (psoriasis, multiple sclerosis, and patients who have undergone a kidney transplant or received dialysis). The study sample was examined regarding the experienced stress levels (Perceived Stress Scale) and coping strategies (Brief-COPE). All four groups of patients most commonly declared using problem-focused coping strategies and least commonly reported the use of avoidant coping. Higher levels of perceived stress strongly correlated with self-blaming. The participants who declared previous psychiatric treatment or psychotherapy were more likely to use self-blaming, behavioral disengagement, substance use, and avoidant coping, while previous psychotherapy additionally correlated with emotion-focused coping. Group comparison identifies patients with a chronic neurological disease, such as multiple sclerosis, at higher risk of a less beneficial coping profile than kidney transplant recipients. Further focus on education and early interventions in at-risk individuals is needed, and widely targeted mental health programs are indicated in order to improve the mental health of patients suffering from chronic diseases.

The utility of comparative models and the local model quality for protein crystal structure determination by Molecular Replacement.

BACKGROUND: Computational models of protein structures were proved to be useful as search models in Molecular Replacement (MR), a common method to solve the phase problem faced by macromolecular crystallography. The success of MR depends on the accuracy of a search model. Unfortunately, this parameter remains unknown until the final structure of the target protein is determined. During the last few years, several Model Quality Assessment Programs (MQAPs) that predict the local accuracy of theoretical models have been developed. In this article, we analyze whether the application of MQAPs improves the utility of theoretical models in MR. RESULTS: For our dataset of 615 search models, the real local accuracy of a model increases the MR success ratio by 101% compared to corresponding polyalanine templates. On the contrary, when local model quality is not utilized in MR, the computational models solved only 4.5% more MR searches than polyalanine templates. For the same dataset of the 615 models, a workflow combining MR with predicted local accuracy of a model found 45% more correct solution than polyalanine templates. To predict such accuracy MetaMQAPclust, a "clustering MQAP" was used. CONCLUSIONS: Using comparative models only marginally increases the MR success ratio in comparison to polyalanine structures of templates. However, the situation changes dramatically once comparative models are used together with their predicted local accuracy. A new functionality was added to the GeneSilico Fold Prediction Metaserver in order to build models that are more useful for MR searches. Additionally, we have developed a simple method, AmIgoMR (Am I good for MR?), to predict if an MR search with a template-based model for a given template is likely to find the correct solution.

Psychopathological Symptoms Among Chronically Ill Patients During SARS-CoV-2 Pandemic in Poland.

Introduction: The global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic had a drastic psychological and economic impact on the global population. Having a chronic disease during the pandemic is associated with numerous limitations and challenges like regular hospital visits, access to health-care units and getting specialized treatment. In addition, chronically ill patients are at great risk of acquiring the SARS-CoV-2 virus and at experiencing a more severe course of illness, due to comorbid conditions as well as more frequent encounters with health-care workers and other patients in medical facilities. The aim of this study was to examine the psychological disturbances, during the pandemic in chronically ill patients. Methods: During the cross-sectional survey conducted between May and October 2020, 398 patients with four different chronic conditions (psoriasis, multiple sclerosis and patients who have undergone a kidney transplant or received dialysis). Study sample was examined regarding the occurrence of psychopathological symptoms (General Health Questionnaire 28) and their perceived stress levels (Perceived Stress Scale). Results: The highest scores were found in the MS group and the lowest scores were found in the kidney transplantation group in every subscale of the GHQ-28. Close to half of the studied population (48.74%, n = 193) patients scored above the cut-off for psychopathology. Conclusion: As the study was conducted during the SARS-CoV-2 pandemic in Poland, it stands to reason that the pandemic affected the psychological wellbeing of chronically ill patients. A COVID-19 infection, being quarantined and having had contact with a person who was infected, did not significantly affect the outcome measures; however, further research is needed to explore this topic.