Adaptively Weighted Top-N Recommendation for Organ Matching

Reducing the shortage of organ donations to meet the demands of patients on the waiting list has being a major challenge in organ transplantation. Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most “suitable” patients. Currently, organ matching decisions are only made by matching scores calculated via scoring models, which are built by the first principles. However, these models may disagree with the actual post-transplantation matching performance (e.g., patient's post-transplant quality of life (QoL) or graft failure measurements). In this paper, we formulate the organ matching decision-making as a top-N recommendation problem and propose an Adaptively Weighted Top-N Recommendation (AWTR) method. AWTR improves performance of the current scoring models by using limited actual matching performance in historical datasets as well as the collected covariates from organ donors and patients. AWTR sacrifices the overall recommendation accuracy by emphasizing the recommendation and ranking accuracy for top-N matched patients. The proposed method is validated in a simulation study, where KAS [ 60 ] is used to simulate the organ-patient recommendation response. The results show that our proposed method outperforms seven state-of-the-art top-N recommendation benchmark methods.

EXPRESS: Distinct abilities associated with matching same identity faces vs. discriminating different faces: Evidence from individual differences in prosopagnosics and controls

Previous face matching studies provide evidence that matching same identity faces (match trials) and discriminating different face identities (non-match trials) rely on distinct processes. For example, instructional studies geared towards improving face matching in applied settings have often found selective improvements in match or non-match trials only. Additionally, a small study found that developmental prosopagnosics (DPs) have specific deficits in making match but not non-match judgments. In the current study, we sought to replicate this finding in DPs and examine how individual differences across DPs and controls in match vs. non-match performance relate to featural vs. holistic processing abilities. 43 DPs and 27 controls matched face images shown from similar front views or with varied lighting or viewpoint. Participants also performed tasks measuring featural (eyes/mouth) and holistic processing (part-whole task). We found that DPs showed worse overall matching performance than controls and that their relative match vs. non-match deficit depended on image variation condition, indicating that DPs do not consistently show match- or non-match-specific deficits. When examining the association between holistic and featural processing abilities and match vs. non-match trials in the entire group of DPs and controls, we found a very clear dissociation: Match trials significantly correlated with eye processing ability (r=.48) but not holistic processing (r=.11), whereas non-match trials significantly correlated with holistic processing (r=.32) but not eye processing (r=.03). This suggests that matching same identity faces relies more on eye processing while discriminating different faces relies more on holistic processing.

Lexical Alignment to Non-native Speakers

Two picture-matching-game experiments investigated if lexical-referential alignment to non-native speakers is enhanced by a desire to aid communicative success (by saying something the conversation partner can certainly understand), a form of audience design. In Experiment 1, a group of native speakers of British English that was not given evidence of their conversation partners’ picture-matching performance showed more alignment to non-native than to native speakers, while another group that was given such evidence aligned equivalently to the two types of speaker. Experiment 2, conducted with speakers of Castilian Spanish, replicated the greater alignment to non-native than native speakers without feedback. However, Experiment 2 also showed that production of grammatical errors by the confederate produced no additional increase of alignment even though making errors suggests lower communicative competence. We suggest that this pattern is consistent with another collaborative strategy, the desire to model correct usage. Together, these results support a role for audience design in alignment to non-native speakers in structured task-based dialogue, but one that is strategically deployed only when deemed necessary.

A ring monopole quad band antenna loaded with metamaterial and slots for wireless applications

The present wireless applications demand a compact, multi-operated, and stable radiation pattern antenna with good gain and impedance matching performance. To accomplish this requirement. In this paper, we propose a compact metamaterial structure loaded quad band antenna. The structural specifications/layout of the antenna consists of a circular ring monopole fed by a microstrip line. The ground part of the antenna is loaded with a metamaterial rectangular split-ring resonator (RSRR), an L-shaped slot, and two horizontally placed rectangular slots parallel to each other. No external matching circuit is utilized and impedance matching is solely controlled by the placement of slots. The antenna shows operation at 2.1 GHz (2.01-2.24 GHz, a bandwidth of 230 MHz (WLAN)), 4.5 GHz (4.35-4.66 GHz, a bandwidth of 310 MHz (C-band)), 5.5 GHz (5.37-5.77 GHz bandwidth of 400 MHz (WiMAX)), and 7.2 GHz (7.08-7.33 GHz, a bandwidth of 250 MHz (satellite band)). The antenna exhibits good gain and stable radiation pattern in both the plane and thus can be utilized for aforementioned applications.

Image Variability and Face Matching

This study investigates whether variability in perceived trait judgements disrupts our ability to match unfamiliar faces. In this preregistered study, 174 participants completed a face matching task where they were asked to indicate whether two face images belonged to the same person or different people (17,748 total data points). Participants completed 51 match trials consisting of images of the same person that differed substantially on one trait (either trustworthiness, dominance, or attractiveness) with minimal differences in the alternate traits. Participants also completed 51 mismatch trials which contained two photos of similar-looking individuals. We hypothesised that participants would make more errors on match trials when images differed in terms of attractiveness ratings than those that differed on trustworthiness or dominance. Contrary to expectations, images that differed in terms of attractiveness were matched most accurately, and there was no relationship between the extent of attractiveness differences and accuracy. There was some evidence that differences in perceived dominance and, to a lesser extent, trustworthiness was associated with lower face matching performance. However, these relationships were not significant when alternate traits were accounted for. The findings of our study suggest that face matching performance is largely robust against variation in trait judgements. fi

Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy

Forensic facial examiners and super-recognizers are highly accurate at comparing two faces to determine identity and outperform the general population. Typically, forensic facial examiners are highly trained, whereas super-recognizers are thought to rely on natural ability. Previous studies have compared the accuracy of facial examiners and super-recognizers but have not studied the detailed behavioral properties of their face matching performance. In this study, we further analyzed data from a previous study which tested facial examiners and super-recognizers on a challenging test of face matching, or facial comparison, ability. In that study, the two groups were equally accurate. Here, we further characterize their behavior. We found distinct behaviors between these two groups, independent of overall accuracy. For differences, we found: 1) Facial examiners took advantage of the full range of the 7-point identity judgment scale; super-recognizers had a preference for the extreme ends of the scale (highly confident decisions); 2) For facial examiners, identity judgments for same-identities and different-identities mirrored each other; those from super-recognizers did not; 3) We evaluated identity judgment agreement across participants. Facial examiners agreed with each other to a greater extent than super-recognizers. We next examined metacognitive awareness of their own ability. While there were qualitative differences in the use of the scale, both groups showed behavioral insight into their own accuracy: more confident people and those who rated the task to be easier tended to be more accurate. These findings suggest that studying facial comparisons may benefit from assessing more than accuracy. This deeper understanding allows us to better interpret judgments according to the nature of a person's facial expertise and experience.

Masked face identification is improved by diagnostic feature training

To slow the spread of COVID-19, many people now wear face masks in public. Face masks impair our ability to identify faces, which can cause problems for professional staff who must identify offenders and members of the public. Here, we investigate whether performance on a masked face matching task can be improved by training participants to compare diagnostic facial features (the ears and facial marks) – a validated training method that improves matching performance for unmasked faces. We find strong evidence this brief diagnostic feature training, which takes less than two minutes to complete, improves matching performance for masked faces by approximately 5%. A control training course, which was unrelated to face identification, had no effect on matching performance. Our findings demonstrate that comparing the ears and facial marks is an effective means of improving face matching performance for masked faces. These findings have implications for professions that regularly perform face identification.

Reconfigurable Filter Design

This chapter discusses recent development of reconfigurable filters. The technical terminology reconfigurable means that a circuit is designed in a way to have various electrical characteristics comparing with one which has a static feature. For the filter design, the various electrical characteristics can be considered as the filter can tune its operating frequency, bandwidth, and/or have multiple operational modes, that is, bandstop or bandpass modes. Also, recently, the filters that can exhibit an improved impedance matching performance over its stopband have been reported. It provides more options for the filter designers to realize the reconfigurable filters having reflective and/or absorptive frequency response types to satisfy a prior given requirement. In this chapter, recently devised filter designs will be covered and essential frequency tuning elements to realize the reconfigurable characteristic will be introduced as well.

Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching

This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation. First, we adopt the knowledge distilled from relevant and well solved tasks to generate high-quality event proposals. Then we incorporate contrastive loss and cycle-consistency loss typically applied to cross-modal retrieval tasks to build semantic matching between the proposals and sentences, which are eventually used to train the caption generation module. In addition, the parameters of matching module are initialized via pre-training based on annotated images to improve the matching performance. Extensive experiments on ActivityNet-Caption dataset reveal the significance of distillation-based event proposal generation and cross-modal retrieval-based semantic matching to weakly supervised DVC, and demonstrate the superiority of our method to existing state-of-the-art methods.