Causation and Opportunity in Tort

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.

The Judiciary and Fiscal Crises: An Institutional Critique

Legal Realism Untamed

How Many Cases are Easy?

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.

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 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.

Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, an end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks and different PLMs show that CP-Tuning outperforms state-of-the-art methods.

Parameterized complexity of spare capacity allocation and the multicost Steiner subgraph problem

Microfabricated patterned NiFeCo/Cu GMR multilayers have been studied by varying device sensing area. A bridge sensor configuration was used to measure signal and noise as a function of the device size at room temperature, in a dc magnetic field and with a dc sense current. Wide-band noise from 0.1 Hz to 100 Hz was found to be field dependent for both easy and hard axis directions of applied bias field. The noise showed a broad peak which coincides with the highest signal sensitivity for both easy and hard axis cases in agreement with work on other multilayer systems. The demagnetizing fields cause the peak signal to decrease with stripe width. The signal to noise ratio was found to be proportional to the square root of the device area. Narrow-band fluctuation noise in the presence of an ac drive signal in these GMR devices was found to have a sharp peak near to zero de applied field. This was explained in terms of a spin-flop mechanism.

We revisit the weakly supervised cross-modal face-name alignment task; that is, given an image and a caption, we label the faces in the image with the names occurring in the caption. Whereas past approaches have learned the latent alignment between names and faces by uncertainty reasoning over a set of images and their respective captions, in this paper, we rely on appropriate loss functions to learn the alignments in a neural network setting and propose SECLA and SECLA-B.SECLA is a Symmetry-Enhanced Contrastive Learning-based Alignment model that can effectively maximize the similarity scores between corresponding faces and names in a weakly supervised fashion. A variation of the model, SECLA-B, learns to align names and faces as humans do, that is, learning from easy to hard cases to further increase the performance of SECLA. More specifically, SECLA-B applies a two-stage learning framework: (1) Training the model on an easy subset with a few names and faces in each image-caption pair. (2) Leveraging the known pairs of names and faces from the easy cases using a bootstrapping strategy with additional loss to prevent forgetting and learning new alignments at the same time. We achieve state-of-the-art results for both the augmented Labeled Faces in the Wild dataset and the Celebrity Together dataset. In addition, we believe that our methods can be adapted to other multimodal news understanding tasks.

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.