Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive Loss

The Judiciary and Fiscal Crises: An Institutional Critique

Clinical decision making requires knowledge, experience and analytical/non-analytical types of decision processes. As clinicians progress from novice to expert, research indicates decision-making becomes less reliant on foundational biomedical knowledge and more on previous experience. In this study, we investigated how knowledge and experience were reflected in terms of differences in neural areas of activation. Novice and expert clinicians diagnosed simple or complex (easy, hard) cases while functional magnetic resonance imaging (fMRI) data were collected. Our results highlight key differences in the neural areas activated in novices and experts during the clinical decision-making process. fMRI data were collected from ten second year medical students (novices) and ten practicing gastroenterologists (experts) while they diagnosed sixteen (eight easy and eight hard) clinical cases via multiple-choice questions. Behavioral data were collected for diagnostic accuracy (correct/incorrect diagnosis) and time taken to assign a clinical diagnosis. Two analyses were performed with the fMRI data. First, data from easy and hard cases were compared within respective groups (easy>hard, hard>easy). Second, neural differences between novices and experts (novice>expert, expert>novice) were assessed. Experts correctly diagnosed more cases than novices and made their diagnoses faster than novices on both easy and hard cases (all p's<0.05). Time taken to diagnose hard cases took significantly longer for both novices and experts. While similar neural areas were activated in both novices and experts during the decision making process, we identified significant hemispheric activation differences between novice and expert clinicians when diagnosing hard clinical cases. Specifically, novice clinicians had greater activations in the left anterior temporal cortex and left ventral lateral prefrontal cortex whereas expert clinicians had greater activations in the right dorsal lateral, right ventral lateral, and right parietal cortex. Hemispheric differences in activation were not observed between novices and experts while diagnosing easy clinical cases. While clinical decision-making engaged the prefrontal cortex (PFC) in both novices and experts, interestingly we observed expertise related differences in the regions and hemispheres of PFC activation between these groups for hard clinical cases. Specifically, in novices we observed activations in left hemisphere neural regions associated with factual rule-based knowledge, whereas in experts we observed right hemisphere activation in neural regions associated with experiential knowledge. Importantly, at the neural level, our data highlight differences in so called type 2 clinical decision-making processes related to prior knowledge and experience.

Legal Realism Untamed

How Many Cases are Easy?

This article shows that we can approach both ‘epistemic’ and ‘conceptual’ problems of causation in tort with the aid of a moral idea sketched out by HLA Hart and developed into a more general account by TM Scanlon. Applied to causation, that idea is that we may justify principles that require people to meet a certain standard of causation and principles that specify a standard of proof for that purpose by appealing to the value for people of the opportunities that the relevant principles make available to them. The idea is developed in the context of epistemic questions, then extended to conceptual ones. The idea explains both why we may apply certain familiar standards of causation in easy cases, and why and how far we ought to relax or modify those standards in hard cases.

Legal Reasoning and Argumentation

Self‐ascriptions of desire seem to differ in their epistemic security. There are easy cases in which a sincere self‐ascription immediately counts as knowledgeable, and there are hard cases in which it is an open question whether an agent actually knows that they have the desire that they take themselves to have. In this paper, I suggest an explanation according to which whether a self‐ascription of desire is easy or hard depends on whether one is familiar with the content of the self‐ascribed desire.

This book chapter compares civil litigation in the courts of first and second instance in Taiwan in 2010–2015 with that in U.S. federal courts in 2010–2013. The two judicial systems, as expected, are different in many ways. Settlement rates in Taiwan, even broadly defined, were below 25%; in U.S. federal court, they exceeded 70%. In Taiwan, summary judgments were basically non-existent; in U.S. federal court, they represented nearly a third of merits judgments. Rates of appeal in Taiwan were nearly 10 times higher (27% versus 3%) than in the U.S. federal courts. And yet judges in Taiwan, at least those in the court of first instance, handled cases more quickly than their colleagues in the U.S. federal courts—indeed, twice as fast. Yet, the two judicial systems responded similarly when encountering simple debt-collection cases. These cases, large in number in both systems, failed to settle as standard theories would predict. Instead, these disputes were frequently resolved through default judgments. This chapter provides suggestions for future empirical comparative studies of civil procedure and litigation.

ABSTRACTObject detection in very-high-resolution (VHR) remote sensing images is one of the important technical means in many fields. In recent years, conventional object detection methods have been completely replaced by Convolutional Neural Network (CNN)-based methods, which are more accurate and efficient. However, most current CNN-based methods applied in VHR image sets have certain defects: (1) Scale preference is common in the framework designs, and the representation ability of feature maps for large and small objects is quite different, so accuracy promotion can hardly be made comprehensively in the detection of different objects. (2) The scale difference of the objects leads to training difficulties. (3) Some high-precision methods require high hardware costs, and the overall frameworks lack practicality. To address such problems, we propose a new object detection method in this paper, namely Balanced Multi-Scale Fusion-based CNN (BMF-CNN). It is a redesigned two-stage detection framework according to the region-based object detection methods, which enabled the detection accuracy of both large and small objects to reach a high level. Through the evaluation in the open VHR remote sensing image sets, we found that BMF-CNN showed a better integrative performance than the current mainstream detection methods.

For an undirected graph G¿=¿(V,E), we say that for l,u,v¿¿¿V, l separates u from v if the distance between u and l differs from the distance from v to l. A set of vertices L¿¿¿V is a feasible solution if for every pair of vertices u,v¿¿¿V there is l¿¿¿L that separates u from v. The metric dimension of a graph is the minimum cardinality of such a feasible solution. Here, we extend this well-studied problem to the case where each vertex v has a non-negative cost, and the goal is to find a feasible solution with a minimum total cost. This weighted version is NP-hard since the unweighted variant is known to be NP-hard. We show polynomial time algorithms for the cases where G is a path, a tree, a cycle, a cograph, a k-edge-augmented tree (that is, a tree with additional k edges) for a constant value of k, and a (not necessarily complete) wheel. The results for paths, trees, cycles, and complete wheels extend known polynomial time algorithms for the unweighted version, whereas the other results are the first known polynomial time algorithms for these classes of graphs even for the unweighted version. Next, we extend the set of graph classes for which computing the unweighted metric dimension of a graph is known to be NPC by showing that for split graphs, bipartite graphs, co-bipartite graphs, and line graphs of bipartite graphs, the problem of computing the unweighted metric dimension of the graph is NPC.

The shortest superstring problem (SSP) is a combinatorial optimization problem which has attracted the interest of many researchers due to its applications in computational molecular biology and in computer science. The SSP is an NP-hard problem, and therefore great effort to develop exact algorithms for it has not been made. On the other hand, several approximation and heuristic algorithms have been implemented indicating the strong effectiveness of the greedy strategies to this problem. Variations of these algorithms can be parallelized providing computational strength in solving real-world instances. Polynomially solvable versions of the problem obtained under specific restrictions to its parameters reveal the boundaries between hard and easy cases. The computational bounds on the approximability of the SSP are a realization of its Max-SNP-hardness, but the weak proved values of them reflect the potential strength of the greedy approximation techniques. The strength of the greedy methods for the SSP is enhanced also by the asymptotic behaviour and the smoothed analysis of the problem in random and real-world instances, respectively. All these issues are presented in this chapter in a concise way covering the whole relevant literature, revealing the knowledge that is already conquered, and paving the path for further development in the study of shortest superstrings. The order of the sections highlights the pass from hardness complexity results for the SSP to efficient algorithms for the problem based on greedy strategies, and to theoretical results that establish the strength of the greedy techniques.

The paper explores Wróblewski’s classical distinction between internal and external justification, highlighting two interpretations. According to the first, a decision is internally justified if it follows from the chosen premises and externally justified if those premises are also justified. From this perspective, some theorists have claimed that internal justification suffices for easy cases, while hard cases require external justification. However, it will be shown that this is a mistake arising from an ambiguity in the term “justification”, and clarifying this ambiguity reveals that both easy and hard cases require the same kind of justification. In the second interpretation, a decision is internally justified if it is derived from existing law and externally justified if its content is morally acceptable. This leads to an examination of the relationship between legal and moral justification. In this regard, Carlos Nino's claim that legal reasoning is a form of moral reasoning will be critically assessed by showing that it ultimately conflates the nature of norms with the nature of the reasons for their acceptance. Finally, it will be argued that moral objectivism is irrelevant for strong justification of judicial decisions (which requires valid reasoning with correct premises). Metaethical scepticism does not undermine substantive moral judgments or weaken moral debate, just as metaethical objectivism does not strengthen them.

Projector video compensation aims to cancel the geometric and photometric distortions caused by non-ideal projection surfaces and environments when projecting videos. Most existing projector compensation methods start by projecting and capturing a set of sampling images, followed by an offline compensation model training step. Thus, abundant user effort is required before the users can watch the video. Moreover, the sampling images have little prior knowledge of the video content and may lead to suboptimal results. To address these issues, this paper builds a video compensation system that can online adapt the compensation parameters. Our approach consists of five threads and can perform compensation, projection, capturing, and short-term and long-term model updates in parallel. Due to the parallel mechanism, rather than projecting and capturing hundreds of sampling images and training the model offline, we can directly use the projected and captured video frames for model updates on the fly. To quickly apply to the new environment, we introduce a deep learning-based compensation model that integrates a fixed transformer-based method and a novel CNN-based network. Moreover, for fast convergence and to reduce error accumulation during fine-tuning, we present a strategy that cooperates with short-term and long-term memory model updates. Experiments show that it significantly outperforms state-of-the-art baselines.

In-scanner motion degrades the quality of magnetic resonance imaging (MRI) thereby reducing its utility in the detection of clinically relevant abnormalities. We collaborate with doctors from NYU Langone's Comprehensive Epilepsy Center and apply a deep learning-based MRI artifact reduction model (DMAR) to correct head motion artifacts in brain MRI scans. Specifically, DMAR employs a two-stage approach: in the first, degraded regions are detected using the Single Shot Multibox Detector (SSD), and in the second, the artifacts within the found regions are reduced using a convolutional autoencoder (CAE). We further introduce a set of novel data augmentation techniques to address the high dimensionality of MRI images and the scarcity of available data. As a result, our model was trained on a large synthetic dataset of 225,000 images generated using 375 whole brain T1-weighted MRI scans from the OASIS-1 dataset. DMAR visibly reduces image artifacts when validated using real-world artifact-affected scans from the multi-center ABIDE study and proprietary data collected at NYU. Quantitatively, depending on the level of degradation, our model achieves a 27.8%-48.1% reduction in RMSE and a 2.88-5.79 dB gain in PSNR on a 5000-sample set of synthetic images. For real-world data without ground-truth, our model reduced the variance of image voxel intensity within artifact-affected brain regions ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}=0.014$</tex> ).

The law often asks judges to determine whether a violation is clearly established or whether legal language clearly or unambiguously supports a position. It demands that litigants not raise arguments or claims that would be easily dismissed. And for their part legal scholars have relied on the distinction between easy and hard cases as they delineate the concept of law itself. Yet, few have empirically studied assessments of legal difficulty. Here, we present the results of two studies that provide insight into the reliability of such assessments from laypeople and actual judges. For both populations, our key finding was this: assessments of case difficulty are highly predictive of the vote breakdown in a case; that is, the larger the size of a majority coalition, the lower the average difficulty rating registered by the members of that coalition (and, conversely, the smaller the size of a minority coalition, the higher the rating registered by its members). These results suggest that, while assessments of case difficulty are tied to case outcomes, they are a complex signal. Experiencing the feeling that a case is unusually difficult is not necessarily an indication that you are dealing with a 50/50 case, as is commonly assumed. Instead, it may be an indication that your position on the merits would be decisively rejected by the vast majority of others confronting the same question.