Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction

We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate weights as its output. However, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN. We reduce the complexity of this problem by incorporating a hashing technique, where the candidate weights given by the parameter prediction network are selected using a predefined hash function to determine individual weights in the dynamic parameter layer. The proposed network---joint network with the CNN for ImageQA and the parameter prediction network---is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks

Tit-for-Tat voting by contestants in the TV quiz-show ‘The Weakest Link’

Background It has not escaped the notice of researchers that TV quiz-shows like ‘The Weakest Link’ (WL) make ideal observational field experiments because they comprise the key ingredients of game theory: a finite group of players must select from a fixed set of actions to play for well defined payoffs. For example, WL has been used to assess the optimal banking strategy in economic decision making (Haan, Los & Riyanto, 2011), the trade-off between risk and return strategies in game playing (Février & Linnemer, 2006; Barmish & Boston, 2009), as a test of gender and race discrimination in voting (Levitt, 2004; Antonovics, Arcidiacono & Walsh, 2005; Goddard, 2012) and to demonstrate ‘neighbour’ effects in voting practice (Goddard, Ashley & Hunter, 2011). Research Questions:- We tested for three kinds of voting bias by players of WL. i.) spatial, ii.) gender and iii.) ‘Tit-for-Tat’ (TFT). Methodology-i.) Rules of WL:- A group of players (n=9) accumulated a pot of money by fielding a first round of questions. Next, each player identified one of their fellows as the ‘weakest’ in that round. The player accruing the majority of votes was summarily eliminated from the show. A second accumulation round of questions preceded another elimination vote, and so on, until the group was whittled down to the final pair, who then played out a tie-breaker to determine an outright winner. Methodology- ii.) Analysis:- The observed frequencies of votes cast in the first and second rounds of 72 episodes of WL were recorded. Simple probability theory was then used to calculate the corresponding expected frequencies due to chance. Significant departures from these expected patterns, identified by χ2 tests, indicated voting bias. Findings:- TFT voting occurred when recipients of round 1 votes responded in kind by voting for the perpetrator in round 2. TFT votes occurred significantly more often than expected, and, significantly more often than those made by the equivalent controls who had not received a vote in round 1. Spatial and gender biases were found: players avoided voting for direct neighbours and females received significantly more votes than males. Interpretation:-We suggest that TFT was played as a deliberate, explicit strategy, but, spatial/gender voting anomalies emerged implicitly. To elaborate, we suggest that a player’s voting decision was informed by two sources of information: situational, the game-specific, public performance of the other players, and, dispositional, their individual, internal, subjective-dependent attributions. In rounds where situational information was unequivocal, so the weakest player was easily identified by the other players (hi-consensus), there was no voting bias. However, significant biases emerged as uncertainty increased (consensus decreased) about the identity of the weakest player. In the absence of clear-cut situational information, because all players performed equally well (or badly!), players resorted to their private, bias-prone dispositional information source. Conclusion:- The format of WL quiz-shows provided an ideal context to analyse forced-choice decision making and the implicit biases and explicit strategies therein

An experiment to test ‘the neighbour effect’

The previous literature on neighbour relations is in general positively biased, focusing on how neighbours cooperate to foster good community relations yet, the relations are not always positive and conflict can arise (Nieuwenhuis, Völker, & Flap, 2013). We suggest that in the realm of social influence an underlying neighbour effect exists whereby people are biased towards being positive to their direct spatial neighbours. We used the unlikely source of ‘The Weakest Link’ TV quiz programme to show a neighbour effect. Contestants significantly avoided voting their direct neighbours as the weakest member of the group (Goddard, Hylton, Parke & Noh, 2013). We designed an experiment to test whether this neighbour effect occurred in other scenarios. Participants (n=233) were year one undergraduates attending their first orientation lecture. Each P was given an instruction sheet that indicated their unique seat number and were asked to cast a vote for a fellow student in the lecture. Their vote conferred either a positive or negative outcome for its recipient (by increasing or decreasing the number of raffle tickets accrued for a subsequent lottery for course related materials.) Participants that cast a negative vote demonstrated a significant neighbour effect by avoiding voting their nearest neighbour. However, the reverse pattern was found for participants issuing a positive vote. We demonstrated the neighbour effect and suggest it is a robust and strong bias in social influence, operating at an unconscious and implicit level. It remains to be seen how this neighbour effect contributes to community based neighbour relations

Effects of valence in decision making

Background/Aim:- A study of voting decisions in The Weakest Link TV game show has shown the contestants tend to avoid their nearest neighbours (Goddard, Hylton, Parke & Noh, 2013), presumably this is because the vote carries negative connotations. Therefore, the aim of this study was to test whether vote valence affects voting behaviour in other voting scenarios. Procedure:- Participants were undergraduate Psychology students (n=233) attending an orientation lecture during their first induction week. A unique seat number was assigned and once seated, participants were given a response sheet, with a seating plan of the lecture, and instructed to cast a vote for someone else in the lecture theatre by placing an ‘X’ on the seating plan. Vote valence varied (-5,-1,0,+1, +5) according to how many ‘vouchers’ were to be added/subtracted in an ensuing lottery for course related ‘prizes’. Result/Discussion:- Participants significantly avoided voting for their nearest neighbour when their vote carried a negative weight, However, those making a positive valence vote, showed an opposite pattern whereby, they were more likely to pick their nearest neighbour. We suggest that for our participants the ‘fairness norm’ was stronger in negative valence voters compared to positive valence voters (Leliveld, Beest, Dijk & Tenbrunsel, 2009). Hence, ‘valence’ and the ‘neighbour effect’ are robust biasing elements in decision making probably operating at an unconscious, implicit level

Love-hate neighbour relationship: shifts in neighbour effect of vote valence

An analysis of contestants’ voting behaviours on the popular TV game show ‘The Weakest Link’ revealed a hitherto unknown neighbour effect. When contestants were asked to single out, and openly declare the ‘weakest’ member, they significantly avoided picking their direct neighbour (Goddard, Hylton, Parke & Noh, 2013). This study aimed to test whether this neighbour effect extends beyond the rarefied atmosphere of the TV studio. Participants (n=233) were year one undergraduates attending their first orientation lecture. They were each given an instruction sheet that indicated their unique seat number and they were asked to cast a vote for a fellow student in the lecture. Their vote either conferred a positive, neutral or negative outcome for its recipient, by either increasing, not-affecting or decreasing the number of raffle tickets accrued for a subsequent lottery for course related materials. The observed frequencies of votes were counted for each voter-candidate spatial relationship and then compared with the frequencies that would be expected purely due to chance alone. Participants that cast a negative vote demonstrated a significant neighbour effect by avoiding voting for their nearest neighbours. However, the reverse pattern was found for participants issuing a positive vote. We suggest that the neighbour effect is a robust and strong bias in decision-making, operating at an unconscious, implicit level. We consider the implications of these results for the wider community in the context of neighbourhood relationship

Voting bias: the ‘neighbour effect’

The UK version of ‘The Weakest Link’ TV-game-show has been used to demonstrate a profound voting bias, as contestants significantly avoided voting for their direct neighbour(s) to be eliminated from the show as the weakest link (Goddard et al, 2013). To test this neighbour effect ouside of the TV studio, a sample of Freshers (year I undergraduates n=233) was seated in a lecture theatre and were asked to ‘vote’ for one of their peers seated on the same row.. Their vote either conferred a positive, neutral or negative outcome for its recipient, by ‘increasing’, ‘not-affecting’ or ‘decreasing’ the number of raffle tickets accrued for a subsequent lottery for course-related materials. The results indicated that the participants that cast a negative vote demonstrated a significant neighbour effect by not nominating their nearest neighbour. However, a reverse polarity pattern was found for participants issuing a positive vote (Noh et al, 2014). We suggest that the neighbour effect is a robust and strong bias in human decision making

The ‘neighbour effect’ in giving

When people are forced to select somebody to receive something ‘nasty’, they tend to avoid those physically closest to themselves: their direct neighbours. We extended our analysis by ‘forcing’ participants to select people to receive something ‘nice’. We wanted to test whether the avoidance of selecting neighbours extended to positive as well as negative gifts. In order to test this, 233 first year undergraduates were recruited and allocated a specific numbered seat in the lecture theatre on a pseudo-random basis. They were then given an instruction sheet that explained that they were being requested to make a forced-choice to select somebody to receive a ‘gift’. The ‘gift’ either increased or decreased the lottery tickets the person would receive in a raffle: +1 (nice), +5 (extremely nice), 0 (neutral), -1 (nasty), -5 (extremely nasty). Eligible candidates were any of the other participants on the same seating row and block in the lecture theatre. When participants issued a ‘nasty-gift’, they demonstrated a significant neighbour effect by avoiding their nearest neighbours. However, for ‘nice-gifts’ the opposite occurred and they favoured their neighbours. Therefore, in the condition when their selection benefitted the candidate, they became significantly more likely to pick their neighbours. We suggested that the neighbour effect is a robust and strong bias, which exists as an implicit bias that effects social interactions in the context of gift economy

Epididymocutaneous fistula in a patient with neurogenic bladder

AbstractWe describe the development of an epididymocutaneous fistula in a patient with a complicated genitourinary history, including recurrent epididymitis and neurogenic bladder

A test to confirm the neighbour effect

Introduction:- We demonstrated previously that people show a profound underlying ‘neighbour effect’ by significantly avoiding doing ‘bad’ things to their direct spatial neighbours. This emerged when the ‘bad’ thing was voting for a fellow contestant on the UK version of the ‘Weakest Link’ TV quiz programme (Goddard et al 2013; Noh et al, 2014). Aims:-We wanted to test how robust this ‘neighbour effect’ was by first observing whether it would extend to other voting scenarios and second to test whether it occurred for doing ‘good’ as well as ‘bad’ things. Method:- Participants were first year students seated in a lecture theatre (n=233). They were asked to cast a closed, secret vote, for another person on the same row, by marking a ‘X’ on a seating plan. The vote carried either a positive or negative outcome for its recipient by gaining or removing lottery tickets. Results:- Participants that cast a negative vote demonstrated a significant ‘neighbour effect’ by avoiding voting for their nearest neighbours. However, the reverse pattern was found when participants gave a positive vote. Conclusion:- We suggest that the ‘neighbour effect’ is a robust and strong bias in decision-making

Low-Dose Dual-Energy Computed Tomography for PET Attenuation Correction with Statistical Sinogram Restoration

Dual-energy (DE) X-ray computed tomography (CT) has been proposed as an useful tool in various applications. One promising application is DECT with low radiation doses used for attenuation correction in positron emission tomography (PET). In low-dose DECT, conventional methods for sinogram decomposition have been based on logarithmic transformations and ignored noise properties, leading to very noisy component sinogram estimates. In this paper, we propose two novel sinogram restoration methods that are statistically motivated; penalized weighted least square (PWLS) and penalized likelihood (PL), producing less noisy component sinogram estimates for low-dose DECT than the conventional approaches. The restored component sinograms can improve attenuation correction, thus allowing better image quality in PET. Experiments with a digital phantom indicate that the proposed methods produce less noisy sinograms, reconstructed images, and attenuation correction factors (ACF) than the conventional one, showing promise for CT-based attenuation correction in emission tomography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85933/1/Fessler230.pd