The glissadic overshoot is characterized by an unwanted type of movement known as glissades. The glissades are a short ocular movement that describe the failure of the neural programming of saccades to move the eyes in order to reach a specific target. In this paper we develop a procedure to determine if a specific saccade have a glissade appended to the end of it. The use of the third partial sum of the Gauss series as mathematical model, a comparison between some specific parameters and the RMSE error are the steps made to reach this goal. Finally a machine learning algorithm is trained, returning expected responses of the presence or not of this kind of ocular movement.

We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve) and the bribery actions are limited to: adding an approval to a vote, deleting an approval from a vote, or moving an approval within a vote from one candidate to the other. We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV). We find the landscape of complexity results quite rich, going from polynomial-time algorithms through NP-hardness with constant-factor approximations, to outright inapproximability. Moreover, in general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee (i.e., adding approvals only for this preferred candidate, or moving approvals only to him or her). We also study parameterized complexity of our problems, with a focus on parameterizations by the numbers of voters or candidates.

Four of the newfound quadruply imaged quasars are shown here: From top left and moving clockwise, the objects are: GraL J1537-3010 or "Wolf's Paw;" GraL J0659 1629 or "Gemini's Crossbow;" GraL J1651-0417 or "Dragon's Kite;" GraL J2038-4008 or "Microscope Lens." The fuzzy dot in the middle of the images is the lensing galaxy, the gravity of which is splitting the light from the quasar behind it in such a way to produce four quasar images. By modeling these systems and monitoring how the different images vary in brightness over time, astronomers can determine the expansion rate of the universe and help solve cosmological problems. With the help of machine-learning techniques, a team of astronomers has discovered a dozen quasars that have been warped by a naturally occurring cosmic "lens" and split into four similar images. Quasars are extremely luminous cores of distant galaxies that are powered by supermassive black holes.

What will the market size be in 2027 and what will the growth rate be? What are the key market trends? What is driving this market? What are the challenges to market growth? Who are the key vendors in this market space?

We develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP). Amortized or approximate computational methods increase efficiency, but can come with unpredictable performance costs. In this work, we present CATs--Confident Adaptive Transformers--in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence. Our method trains additional prediction heads on top of intermediate layers, and dynamically decides when to stop allocating computational effort to each input using a Figure 1: Our CAT model G can save computational resources meta consistency classifier. To calibrate our by exiting early on certain inputs--while guaranteeing early prediction stopping rule, we formulate a predictive consistency with the full model F. unique extension of conformal prediction.

In Clarifications are crucial to robust dialogues, and Sections 4 and A we test the practical implications pragmatic factors -- notably those shaped by the of our recipe by identifying and characterizing (according world modalities situating the conversation -- have to their modalities) the clarifications in a a key role to play. Referring expressions have in corpus of long dialogues in English. In Section 5 vision a modality in which to ground clarifications we turn to the claim that clarifications are rare in concerning objects in the world (de Vries et al., dialogue datasets (Ginzburg, 2012), and that current 2017); navigation instructions have in movement data-hungry algorithms cannot learn them. We a modality in which to ground clarifications concerning argue that whether they are rare or not depends collaborative wayfinding (Thomason et al., on pragmatic factors of the conversation and the 2019). Clarifications grounded in situationally relevant modality of the grounded clarification, and discuss modalities boost the redundancy required to the impact of six such factors. After presenting learn to use language without explicit supervision, potential objections and our responses in Section 6, as they make explicit the process of negotiating the we conclude in Section 7 by noting ethical issues communicative intent. But despite its importance, raised by socioperceptive dialogue systems that work on clarification remains scattered.

In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.

Self-supervised learning has attracted great interest due to its tremendous potentials in learning discriminative representations in an unsupervised manner. Along this direction, contrastive learning achieves current state-of-the-art performance. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efficiency, e.g., taking about ten times more training epochs than supervised learning for comparable recognition accuracy. In this paper, we discover two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency. Under-clustering means that the model cannot efficiently learn to discover the dissimilarity between inter-class samples when the negative sample pairs for contrastive learning are insufficient to differentiate all the actual object categories. Over-clustering implies that the model cannot efficiently learn the feature representation from excessive negative sample pairs, which include many outliers and thus enforce the model to over-cluster samples of the same actual categories into different clusters. To simultaneously overcome these two problems, we propose a novel self-supervised learning framework using a median triplet loss. Precisely, we employ a triplet loss tending to maximize the relative distance between the positive pair and negative pairs to address the under-clustering problem; and we construct the negative pair by selecting the negative sample of a median similarity score from all negative samples to avoid the over-clustering problem, guaranteed by the Bernoulli Distribution model. We extensively evaluate our proposed framework in several large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results demonstrate the superior performance of our model over the latest state-of-the-art methods by a clear margin.

Data imbalance remains one of the factors negatively affecting the performance of contemporary machine learning algorithms. One of the most common approaches to reducing the negative impact of data imbalance is preprocessing the original dataset with data-level strategies. In this paper we propose a unified framework for imbalanced data over- and undersampling. The proposed approach utilizes radial basis functions to preserve the original shape of the underlying class distributions during the resampling process. This is done by optimizing the positions of generated synthetic observations with respect to the potential resemblance loss. The final Potential Anchoring algorithm combines over- and undersampling within the proposed framework. The results of the experiments conducted on 60 imbalanced datasets show outperformance of Potential Anchoring over state-of-the-art resampling algorithms, including previously proposed methods that utilize radial basis functions to model class potential. Furthermore, the results of the analysis based on the proposed data complexity index show that Potential Anchoring is particularly well suited for handling naturally complex (i.e. not affected by the presence of noise) datasets.

Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising results. However, due to being in the early stages of development, these also have some substantial shortcomings. One of the abilities that are absent in the current methods is further utilising advice by reusing, which is especially crucial in the practical settings considering the budget and cost constraints in peer-to-peer. In this study, we present an approach to enable the student agent to imitate previously acquired advice to reuse them directly in its exploration policy, without any interventions in the learning mechanism itself. In particular, we employ a behavioural cloning module to imitate the teacher policy and use dropout regularisation to have a notion of epistemic uncertainty to keep track of which state-advice pairs are actually collected. As the results of experiments we conducted in three Atari games show, advice reusing via generalisation is indeed a feasible option in deep RL and our approach can successfully achieve this while significantly improving the learning performance, even when paired with a simple early advising heuristic.