Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Deep Neural Network Architectures Robust to Adversarial Examples

Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. How- ever, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.
Between a rock and a hard place: an insight into the psychological therapist’s experience of having safeguarding concerns for their clients' children

This study explores the therapists' experience when managing safeguarding concerns about their clients' children, with the aim of raising awareness and bringing the child into the therapists' mind. A review of the literature indicated that this is an undeveloped area, with existing research lacking attention to the impact of the therapeutic relationship on this ethical dilemma. IPA (interpretative phenomenological analysis) was used to analyse the findings from six semistructured interviews with a mix of psychological therapists who had experienced concerns for their clients' children while working in non-NHS settings. This approach allows for an in-depth exploration into the subjective experience of the participant, while also recognising the reflective role of the researcher. The findings indicate that therapists are struggling with a lack of confidence and experience in managing their child protection concerns, with an indication of a training need. This lack of confidence, combined with a range of complex emotions evoked in such work, increased the participants' need for support and supervision. Participants felt the need to alter their therapeutic practice and become more directive in order to ascertain clearer details about their concerns. The findings have implications for the training and continued professional development requirements for psychological therapists. There is an indication for training focused on managing concerns in the context of the therapeutic relationship, more training around assessing the level of risk and the development of a prompt sheet with reflective questions to aid therapists in their thinking. An identified limitation of this study is the range of participants in terms of gender and ethnicity; further research could explore more thoroughly whether these factors influence how concerns are managed. There is also scope to explore more specifically the impact of the therapeutic placement, to identify whether those working within large organisations, such as the NHS, feel more supported and contained in this type of work than those working more independently.
Correlations between Groups of Premotor Neurons Carry Information about Prehension

How distinct parameters are bound together in brain activity is unknown. Combination coding by interneuronal interactions is one possibility, but, to coordinate parameters, interactions between neuronal pairs must carry information about them. To address this issue, we recorded neural activity from multiple sites in the premotor cortices of monkeys that memorized reach direction and grasp type followed by actual prehension. We found that correlations between individual spiking neurons are generally weak and carry little information about prehension. In contrast, correlations and synchronous interactions between small groups of neurons, quantified by multiunit activity (MUA), are an order of magnitude stronger. A substantial fraction of the information carried by pairwise interactions between MUAs is about combinations of reach and grasp. This contrasts with the information carried by individual neurons and individual MUAs, which is mainly about reach and/or grasp but much less about their combinations. The main contribution of pairwise interactions to the coding of reach–grasp combinations is when animals memorize prehension parameters, consistent with an internal composite representation. The informative interactions between neuronal groups may facilitate the coordination of reach and grasp into coherent prehension.
Oracle Performance for Visual Captioning

The task of associating images and videos with a natural language description has attracted a great amount of attention recently. Rapid progress has been made in terms of both developing novel algorithms and releasing new datasets. Indeed, the state-of-the-art results on some of the standard datasets have been pushed into the regime where it has become more and more difficult to make significant improvements. Instead of proposing new models, this work investigates the possibility of empirically establishing performance upper bounds on various visual captioning datasets without extra data labelling effort or human evaluation. In particular, it is assumed that visual captioning is decomposed into two steps: from visual inputs to visual concepts, and from visual concepts to natural language descriptions. One would be able to obtain an upper bound when assuming the first step is perfect and only requiring training a conditional language model for the second step. We demonstrate the construction of such bounds on MS-COCO, YouTube2Text and LSMDC (a combination of M-VAD and MPII-MD). Surprisingly, despite of the imperfect process we used for visual concept extraction in the first step and the simplicity of the language model for the second step, we show that current state-of-the-art models fall short when being compared with the learned upper bounds. Furthermore, with such a bound, we quantify several important factors concerning image and video captioning: the number of visual concepts captured by different models, the trade-off between the amount of visual elements captured and their accuracy, and the intrinsic difficulty and blessing of different datasets.
IP Storage: The Challenge Ahead

Advanced networking technology has led to the genesis of the storage area network model, where host servers can access storage as a service from various devices connected to the network. While the initial approach to storage area networks has involved specialized networking technology, the emergence of Gigabit Ethernet technology has raised the question of whether we can use commodity IP networks for storage. This paper examines the issues involving IP storage networks and presents a performance analysis to dispel some of the myths and outline some of the challenges.
A statistically selected Chandra sample of 20 galaxy clusters - II. Gas properties and cool core/non-cool core bimodality

We investigate the thermodynamic and chemical structure of the intracluster medium (ICM) across a statistical sample of 20 galaxy clusters analysed with the Chandra X-ray satellite. In particular, we focus on the scaling properties of the gas density, metallicity and entropy and the comparison between clusters with and without cool cores (CCs). We find marked differences between the two categories except for the gas metallicity, which declines strongly with radius for all clusters (Z ∝ r −0.31 ), outside ∼0.02r 500. The scaling of gas entropy is non-self-similar and we find clear evidence of bimodality in the distribution of logarithmic slopes of the entropy profiles. With only one exception, the steeper sloped entropy profiles are found in CC clusters whereas the flatter slope population are all non-CC clusters. We explore the role of thermal conduction in stabilizing the ICM and conclude that this mechanism alone is sufficient to balance cooling in non-CC clusters. However, CC clusters appear to form a distinct population in which heating from feedback is required in addition to conduction. Under the assumption that non-CC clusters are thermally stabilized by conduction alone, we find the distribution of Spitzer conduction suppression factors, f c, to be lognormal, with a log (base 10) mean of −1.50 ± 0.03 (i.e. f c = 0.032) and log standard deviation 0.39 ± 0.02.
Robust speech recognition over mobile and IP networks in burst-like packet loss

This paper addresses the problem of achieving robust distributed speech recognition in the presence of burst-like packet loss. To compensate for packet loss a number of techniques are investigated to provide estimates of lost vectors. Experimental results on both a connected digits task and a large vocabulary continuous speech recognition task show that simple methods, such as repetition, are not as effective as interpolation methods which are better able to preserve the dynamics of the feature vector stream. Best performance is given by maximum a-posteriori (MAP) estimation of lost vectors which utilizes statistics of the feature vector stream. At longer burst lengths the performance of these compensation techniques deteriorates as the temporal correlation in the received feature vector stream reduces. To compensate for this interleaving is proposed which aims to disperse bursts of loss into a series of unconnected smaller bursts. Results show substantial gains in accuracy, to almost that of the no loss condition, when interleaving is combined with estimation techniques, although this is at the expense of introducing delay. This leads to the proposal that, for a distributed speech recognition application, it is more beneficial to trade delay for accuracy rather than trading bit-rate for accuracy as in forward error correction schemes.
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.
Job insecurity, employee anxiety, and commitment: The moderating role of collective trust in management

ABSTRACT This article examines the moderating effect of collective trust in management on the relation between job insecurity (both objective and subjective) and employee outcomes (work-related anxiety and organisational commitment). This is contextualised in the modern British workplace which has seen increased employment insecurity and widespread cynicism. We use matched employer-employee data extracted from the British Workplace Employment Relations Survey (WERS) 2011, which includes over 16,000 employees from more than 1100 organisations. The multilevel analyses confirm that objective job insecurity (loss of important elements of a job such as cuts in pay, overtime, training, and working hours) are significantly correlated with high levels of work-related anxiety and lower levels of organisational commitment. These correlations are partially mediated by subjective job insecurity (perception of possible job loss). More importantly, collective trust in management (a consensus of management being reliable, honest and fair) significantly attenuates the negative impact of objective job insecurity on organisational commitment, and reduces the impact of subjective job insecurity on work-related anxiety. Theoretical and practical implications and limitations of these effects are discussed.
Peroxisome Proliferator-Activated Receptor (PPAR ) Potentiates, whereas PPAR Attenuates, Glucose- Stimulated Insulin Secretion in Pancreatic -Cells

Fatty acids (FAs) are known to be important regulators of insulin secretion from pancreatic -cells. FA-coenzyme A esters have been shown to directly stimulate the secretion process, whereas long-term exposure of -cells to FAs compromises glucose-stimulated insulin secretion (GSIS) by mechanisms unknown to date. It has been speculated that some of these long-term effects are mediated by members of the peroxisome proliferator-activated receptor (PPAR) family via an induction of uncoupling protein-2 (UCP2). In this study we show that adenoviral coexpression of PPAR and retinoid X receptor (RXR ) in INS-1E -cells synergistically and in a doseand ligand-dependent manner increases the expression of known PPAR target genes and enhances FA uptake and -oxidation. In contrast, ectopic expression of PPAR /RXR increases FA uptake and deposition as triacylglycerides. Although the expression of PPAR /RXR leads to the induction of UCP2 mRNA and protein, this is not accompanied by reduced hyperpolarization of the mitochondrial membrane, indicating that under these conditions, increased UCP2 expression is insufficient for dissipation of the mitochondrial proton gradient. Importantly, whereas expression of PPAR /RXR attenuates GSIS, the expression of PPAR /RXR potentiates GSIS in rat islets and INS-1E cells without affecting the mitochondrial membrane potential. These results show a strong subtype specificity of the two PPAR subtypes and on lipid partitioning and insulin secretion when systematically compared in a -cell context. (Endocrinology 146: 3266–3276, 2005)
Rate Adaptation Games in Wireless LANs: Nash Equilibrium and Price of Anarchy

In Wireless LANs, users may adapt their transmission rates depending on the radio conditions of their links so as to maximize their throughput. Recently, there has been a significant research effort in developing distributed rate adaptation schemes. Unlike previous works that mainly focus on channel tracking, this paper characterizes the optimal reaction of a rate adaptation protocol to the contention information received from the MAC. We formulate this problem analytically. We study both competitive and cooperative user behaviors. In the case of competition, users selfishly adapt their rates so as to maximize their own throughput, whereas in the case of cooperation they adapt their rates so as to maximize the overall system throughput. We show that the Nash Equilibrium reached in the case of competition is inefficient (i.e. the price of anarchy goes to infinity as the number of users increases), and provide insightful properties of the socially optimal rate adaptation schemes. We find that recently proposed collision-aware rate adaptation algorithms decrease the price of anarchy. We also propose a novel collision-aware rate adaptation algorithm that further reduces the price of anarchy.
Do Weak Institutions Prolong Crises? On the Identification, Characteristics, and Duration of Declines During Economic Slumps

This paper studies periods of prolonged contractions in output per capita in a sample of 145 countries from 1950 to 2014. Economic slumps are defined as abrupt interruptions of a period of growth by several regime switches. Slumps start with a sharp contraction along with a trend break, which is followed by another switch when growth stabilizes again. The paper then analyzes the correlates of these slumps, focusing on the length and depth of the contraction, from the beginning of the slump to its trough. The results establish three new stylized facts: (i) weak political institutions predate crises whereas political reforms tend to follow them, (ii) the length and depth of economic declines are robustly correlated with executive constraints and ethnic heterogeneity, and (iii) there is a robust interaction between these two variables, suggesting that institutions constraining leaders are important for stabilizing growth. This is particularly relevant for Sub-Saharan Africa, where politics are often ethnic and decision makers are comparatively unconstrained.
ECO-ANXIETY, TRAGEDY, AND HOPE: PSYCHOLOGICAL AND SPIRITUAL DIMENSIONS OF CLIMATE CHANGE

This article addresses the problem of “eco-anxiety” by integrating results from numerous fields of inquiry. While climate change may cause direct psychological and existential impacts, already now vast numbers of people experience indirect impacts in forms of depression, socio-ethical paralysis, and loss of wellbeing. This is not always evident, because people have in response developed psychological and social defenses, including “socially constructed silence. ” I argue that this situation causes the need to frame climate change narratives as emphasizing hope in the midst of tragedy. Framing the situation simply as a threat or a possibility does not work. Religious communities and the use of methods which include spirituality have an important role in enabling people to process their deep emotions and existential questions. I draw also from my experiences from Finland in enabling co-operation between natural scientists and theologians in order to address climate issues.
A heterogeneous two-server network system with balking and a Bernoulli vacation schedule

In this paper, we study a two-server Markovian network system with balking and a Bernoulli schedule under a single vacation policy where servers have different service rates. After every service, only one server may take a vacation or continue to stay in the system. The vacation time follows an exponential distribution. An arriving customer finding both servers free will choose the faster server. If the customer finds both servers are not free, then this customer may join the system or balk. For this system, we obtain the steady state condition, the stationary distribution of the number of customers in the system and the mean system size by using a matrix geometric method. Some special cases are deduced, which match with earlier exiting results. Extensive numerical illustrations are provided. Motivation for this system model also comes from some computer communication networks with different types of traffic such as real-time traffic and non-real-time traffic, where messages can be processed by two channels (servers) with different transmission rates. The behavior of abandoning messages can be equated with the balking of customers in this system model.
Medication adherence in the management of nocturia: challenges and solutions

Objective Nocturia affects millions of men and women. No prior reviews or meta-analyses have explored the issue of adherence in nocturia patients. The objective of our study was to examine the attributes and their interaction that might impact pharmacological adherence in nocturia care using a conceptual model of adherence. Materials and methods A literature search of the Medline, PubMed, Embase, PsycInfo, and CINAHL databases for studies published between January 1990 and June 2014 was conducted. We developed a conceptual model in order to facilitate our review. Results Currently, multiple treatment options for nocturia exist, depending on the underlying cause. Adherence to nocturia treatment and outcomes are complex and intertwined, and nonadherence to nocturia treatment is common. In 15 studies meeting eligibility criteria, behavioral and pharmacologic interventions for nocturia were associated with reduced nocturia symptoms. Urinary symptoms that are associated with nocturia need individualized management depending on renal and hepatic function, medical comorbidities, and ongoing medication use in a patient. Another important factor related to adherence is the bother. Although nocturia is defined as nighttime-voiding frequency of one or more, not all persons may find this bothersome. The degree of bother is subjective, and may change from person to person. However, there is no information related to the association between bother and adherence to medication or behavioral treatments for nocturia. Medication dosing convenience, preference, and cost play important roles in adherence. We present a patient-centered conceptual model that brings together the various dimensions of medication adherence for nocturia. Conclusion Few studies have explored adherence to medication and related factors in the care of nocturia. Our conceptual model can aid development of interventions to improve adherence to nocturia medications.
Recurrence-shift relations for the polynomial functions of Aldaya, Bisquert, and Navarro-Salas

Using a simple factorization scheme we obtain the recurrence-shift relations of the polynoll1ial functions of Aldaya, llisquert amI Navarro-Salas (ABNS), F~"(~JN x), i. e. , one-step first-orocr differential rclations refcrring to N, as follows. Firstly, we apply thc scheme to the polynomial degree confirming the recurrence relations of Aldaya, Bisquert and Navarro-Salas, but also obtaining another slightly modified pair. Secondly, the factorization scheme is applied to the Gegenhauer polynomials to get the recurrcncc relations with respect to their parameter. Next, we make use of Nagel's result, showing thc connection between Gegenbauer polynomials and the ABNS functions, to writc down the recurrence-shift relations for the latter ones. Such relations may be used in the study of the spatial structure of pair-creation proccsses in an Anti-de Sitter gravitational background.
Separate streams or probabilistic inference? What the N400 can tell us about the comprehension of events

ABSTRACT Since the early 2000s, several event-related potential studies have challenged the assumption that we always use syntactic contextual information to influence semantic processing of incoming words, as reflected by the N400 component. One approach for explaining these findings is to posit distinct semantic and syntactic processing mechanisms, each with distinct time courses. While this approach can explain specific datasets, it cannot account for the wider body of findings. I propose an alternative explanation: a dynamic generative framework in which our goal is to infer the underlying event that best explains the set of inputs encountered at any given time. Within this framework, combinations of semantic and syntactic cues with varying reliabilities are used as evidence to weight probabilistic hypotheses about this event. I further argue that the computational principles of this framework can be extended to understand how we infer situation models during discourse comprehension, and intended messages during spoken communication.