Miracle-dz/UniFAR-FTUs huggingface dataset:
Where Are They Now?: Longitudinal Follow-Up and Prognosis of Adolescent Suicide Attempters

Background: While a history of suicide attempts has been identified as the most powerful risk factor among adults, it is not clear if this is also true for the adolescent population. Our aim was to examine the differences between attempters and nonattempters in the years following a documented suicide attempt and to investigate the adolescents' prognosis in terms of suicidal behavior and adjustment. Method: Military records at induction and during active military service were used to compare 105 adolescent suicide attempters with 105 matched controls. All were rated on cognitive/educational performance and psychosocial adaptation, psychological health diagnoses, and performance during their military service. Results: Suicide attempters had higher school dropout rates and lower scores on educational indicators. They registered more incidents of disciplinary and adjustment problems in the military. However, the overall prognosis of the suicide attempters appeared surprisingly good. No significant differences were found between the groups in suicide risk or in behavior in their military service. Limitations: Data were derived from the computerized records and no direct interviews were conducted with the participants. Conclusion: Attempted suicide in adolescence appears to be different in nature from attempted suicide in adulthood, and can be viewed as an indicator of social distress rather than as major risk factor of completed suicide. Implications in terms of intervention and prevention are discussed.
Wavelet-based progressive compression scheme for triangle meshes: wavemesh

We propose a new lossy to lossless progressive compression scheme for triangular meshes, based on a wavelet multiresolution theory for irregular 3D meshes. Although remeshing techniques obtain better compression ratios for geometric compression, this approach can be very effective when one wants to keep the connectivity and geometry of the processed mesh completely unchanged. The simplification is based on the solving of an inverse problem. Optimization of both the connectivity and geometry of the processed mesh improves the approximation quality and the compression ratio of the scheme at each resolution level. We show why this algorithm provides an efficient means of compression for both connectivity and geometry of 3D meshes and it is illustrated by experimental results on various sets of reference meshes, where our algorithm performs better than previously published approaches for both lossless and progressive compression.
Moving Agent Pedestrians Through Space and Time

The choreography of pedestrian movement is important to many domains of interest, particularly in the geographical sciences. Agent-based models have become a popular tool for simulating movement, allowing experimentation with scenarios in computer models that might not be amenable to real-world investigation. The fidelity of agent-based movement models is naturally most acute when the models driving their synthetic characters reproduce the geography of their behaviors appropriately: by placing people in the right places, at the right times, doing the right things, in the right contexts. Most simulation environments for moving agent pedestrians, however, rely on simple, abstract physical heuristics to drive synthetic characters and they focus on generating plausible coarse-grained movement patterns, which might not always map to real-world pedestrian behavior. Moreover, existing approaches often produce serious mechanical artifacts in simulation. I contend that agent-based models of pedestrian movement can benefit more fully from a comprehensive infusion of realistic movement behavior and I present the case for, and proven usefulness of, a geographic engine for driving synthetic actors in simulation. Whereas many existing approaches use particle physics, my approach is sourced in theory and observation, modeling lower, medium-, and higher level behavioral geographies for perceiving and sorting objects, route planning and wayfinding, orientation and locomotion, physical steering, mediating interactions, and determining the space and time for scheduling and realizing activities, with the results that the scheme that I present can automatically generate realistic-looking and realistic-behaving synthetic pedestrians for experimentation.
Compiler and runtime techniques for software transactional memory optimization

Software transactional memory (STM) systems are an attractive environment to evaluate optimistic concurrency. We describe our experience of supporting and optimizing an STM system at both the managed runtime and compiler levels. We describe the design policies of our STM system and the statistics collected by the runtime to identify performance bottlenecks and guide tuning decisions. We present an initial work on supporting automatic instrumentation of the STM primitives for C/C++ and Java programs in the IBM XL compiler and J9 Java virtual machine. We evaluate and discuss the performance of several transactional programs running on our system. Copyright © 2008 John Wiley & Sons, Ltd.
Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling

Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction—however, with stagnant progress in the last 3-5 years. We conduct a large-scale transfer learning study which tests different Ima-geNet backbones, always using the same read out architecture and learning protocol adopted from DeepGaze II. By re-placing the VGG19 backbone of DeepGaze II with ResNet50 features we improve the performance on saliency prediction from 78% to 85%. However, as we continue to test better ImageNet models as backbones—such as EfﬁcientNetB5—we observe no additional improvement on saliency prediction. By analyzing the backbones further, we ﬁnd that generalization to other datasets differs substantially, with models being consistently overconﬁdent in their ﬁxation predictions. We show that by combining multiple backbones in a principled manner a good conﬁdence calibration on unseen datasets can be achieved. This yields a signiﬁcant leap in benchmark performance in and out-of-domain with a 15 percent point improvement over DeepGaze II to 93% on MIT1003, marking a new state of the art on the MIT/Tuebingen Saliency Benchmark in all available metrics (AUC: 88.3%, sAUC: 79.4%, CC: 82.4%).
Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Evolutionary Algorithms-How to Cope With Plateaus of Constant Fitness and When to Reject Strings of the Same Fitness

The most simple evolutionary algorithm, the so-called (1+1)EA accepts a child if its fitness is at least as large (in the case of maximization) as the fitness of its parent. The variant (1 + 1)∗EA only accepts a child if its fitness is strictly larger than the fitness of its parent. Here two functions related to the class of long path functions are presented such that the (1 + 1)EA maximizes one of it in polynomial time and needs exponential time for the other while the (1+1)∗EA has the opposite behavior. These results prove that small changes of an evolutionary algorithm may change its behavior significantly. Since the (1 + 1)EA and the (1 + 1)∗EA differ only on plateaus of constant fitness, the results also show how evolutionary algorithms behave on such plateaus. The (1 + 1)EA can pass a path of constant fitness and polynomial length in polynomial time. Finally, for these functions it is shown that local performance measures like the quality gain and the progress rate do not describe the global behavior of evolutionary algorithms.
Neural Models for Key Phrase Extraction and Question Generation

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.
Towards Automatic Generation of Natural Language Generation Systems

Systems that interact with the user via natural language are in their infancy. As these systems mature and become more complex, it would be desirable for a system developer if there were an automatic method for creating natural language generation components that can produce quality output efficiently. We conduct experiments that show that this goal appears to be realizable. In particular we discuss a natural language generation system that is composed of SPoT, a trainable sentence planner, and FER-GUS, a stochastic surface, realizer. We show how these stochastic NLG components can be made to work together, that they can be ported to new domains with apparent ease, and that such NLG components can be integrated in a real-time dialog system.
Understanding user gestures for manipulating 3D objects from touchscreen inputs

Multi-touch interfaces have emerged with the widespread use of smartphones. Although a lot of people interact with 2D applications through touchscreens, interaction with 3D applications remains little explored. Most of 3D object manipulation techniques have been created by designers and users are generally put aside from the design creation process. We conducted a user study to better understand how non-technical users interact with a 3D object from touchscreen inputs. The experiment has been conducted while users manipulated a 3D cube with three points of view for rotations, scaling and translations (RST). Sixteen users participated and 432 gestures were analyzed. To classify data, we introduce a taxonomy for 3D manipulation gestures with touchscreens. Then, we identify a set of strategies employed by users to realize the proposed cube transformations. Our findings suggest that each participant uses several strategies with a predominant one. Finally, we propose some guidelines to help designers in the creation of more user friendly tools.
RPPanalyzer: Analysis of reverse-phase protein array data

SUMMARY RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity. Finally, the data can be visualized in heatmaps, boxplots, time course plots and correlation plots. RPPanalyzer is a flexible tool and tolerates a huge variety of different experimental designs. AVAILABILITY The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/.
Dual Information Systems: Supporting Organizational Working and Learning by Making Organizational Memory Transparent

The conceptual design of most computer-based information systems reflects a dualism of technology. During the development phase, part of the work-domain-related knowledge is formalized and encoded in the software, making it difficult for users to reflect on and use this knowledge. This design--use dualism contributes to the deterioration of the interpretive flexibility of information systems. In this article, we outline an information systems architecture called Dual Information Systems (DIS) that incorporates the concepts of an organizational memory information system (OMIS) in a broader framework. DIS help bridge the design--use dualism by providing organizations with a set of services that enable and reinforce both effective, institutionalized working and the questioning and (re)construction of computer-supported work routines. DIS have a 4-layered conceptual structure: (a) people draw on the business layer to work and learn; (b) people use the breakdown layer to handle unexpected break-downs; (c) self.
Teaching Machines to Describe Images with Natural Language Feedback

Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users. In this paper, we bring a human in the loop, and enable a human teacher to give feedback to a learning agent in the form of natural language. A descriptive sentence can provide a stronger learning signal than a numeric reward in that it can easily point to where the mistakes are and how to correct them. We focus on the problem of image captioning in which the quality of the output can easily be judged by non-experts. We propose a phrase-based captioning model trained with policy gradients, and design a critic that provides reward to the learner by conditioning on the human-provided feedback. We show that by exploiting descriptive feedback our model learns to perform better than when given independently written human captions.
Lessons Learned from Distributed Agile Software Projects: A Case-Based Analysis

Agile software development in a distributed setting is challenging. The teams involved in the process face difficulties in communication, personnel selection, work culture, and knowledge management. The shortcomings associated with working in different time zones and the inabilities to develop trusting relationships between developers are well known. Companies often take recourse to agile software development methods in a distributed environment in search of reduced cost, higher efficiency, increased flexibility, and good customization. However, it is not clear whether agile methods can be successfully followed and their benefits realized in a distributed setting. This paper revisits and synthesizes the lessons learnt from twelve case studies detailing successful implementation of distributed agile software projects. The cases are analyzed from the perspective of the agile manifesto to determine how closely they follow its values and principles and to what extent they realize the benefits of the agile methodology. The cases lead to the discovery of disparate and innovative solutions adopted by different companies for overcoming the challenges of distributed agile software development. Some solutions are commonplace and others are unique and their combination in the context of the challenges is enlightening. The list of solutions can suitably guide companies that plan to adopt the agile methodology in distributed software development environments in future.
Learning a discriminative dictionary for facial expression recognition

Dictionary learning for sparse representation classifiers (SRC) has demonstrated great success for many classification problems, i.e., face recognition, object detection, etc. However, it has not enjoyed a similar reception in the facial expression recognition literature. In this paper, we applied dictionary learning methods to the task of facial expression recognition, which is then compared with SVM. In addition, we introduce a new dictionary learning method that incorporates side information, which is contained in the training data but not available in the testing phase. In particular, we introduce a new soft constraint derived from side information and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution to the objective function is efficiently obtained using the K-SVD algorithm. Our algorithm learns the dictionary and an optimal linear classifier jointly. Experimental part demonstrates the effectiveness of the sparse representation classifiers for facial expression recognition problem, and dictionary learning with side information method achieves further improvement on low resolution facial expression recognition.
A Systematic Analysis of Translation Model Search Spaces

Translation systems are complex, and most metrics do little to pinpoint causes of error or isolate system differences. We use a simple technique to discover induction errors, which occur when good translations are absent from model search spaces. Our results show that a common pruning heuristic drastically increases induction error, and also strongly suggest that the search spaces of phrase-based and hierarchical phrase-based models are highly overlapping despite the well known structural differences.
Geodesic Sinkhorn: optimal transport for high-dimensional datasets

Understanding the dynamics and reactions of cells from population snapshots is a major challenge in single-cell transcriptomics. Here, we present Geodesic Sinkhorn, a method for interpolating populations along a data manifold that leverages existing kernels developed for single-cell dimensionality reduction and visualization methods. Our Geodesic Sinkhorn method uses a heat-geodesic ground distance that, as compared to Euclidean ground distances, is more accurate for interpolating single-cell dynamics on a wide variety of datasets and signiﬁcantly speeds up the computation for sparse kernels. We ﬁrst apply Geodesic Sinkhorn to 10 single-cell transcriptomics time series interpolation datasets as a drop-in replacement for existing interpolation methods where it outperforms on all datasets, showing its eﬀectiveness in modeling cell dynamics. Second, we show how to eﬃciently approximate the operator with polynomial kernels allowing us to improve scaling to large datasets. Finally, we deﬁne the conditional Wasserstein-average treatment eﬀect and show how it can elucidate the treatment eﬀect on single-cell populations on a drug screen.
Defensive Few-shot Adversarial Learning

The robustness of deep learning models against adversarial attacks has received increasing attention in recent years. However, both deep learning and adversarial training rely on the availability of a large amount of labeled data and usually do not generalize well to new, unseen classes when only a few training samples are accessible. To address this problem, we explicitly introduce a new challenging problem -- how to learn a robust deep model with limited training samples per class, called defensive few-shot learning in this paper. Simply employing the existing adversarial training techniques in the literature cannot solve this problem. This is because few-shot learning needs to learn transferable knowledge from disjoint auxiliary data, and thus it is invalid to assume the sample-level distribution consistency between the training and test sets as commonly assumed in existing adversarial training techniques. In this paper, instead of assuming such a distribution consistency, we propose to make this assumption at a task-level in the episodic training paradigm in order to better transfer the defense knowledge. Furthermore, inside each task, we design a task-conditioned distribution constraint to narrow the distribution gap between clean and adversarial examples at a sample-level. These give rise to a novel mechanism called multi-level distribution based adversarial training (MDAT) for learning transferable adversarial defense. In addition, a unified $\mathcal{F}_{\beta}$ score is introduced to evaluate different defense methods under the same principle. Extensive experiments demonstrate that MDAT achieves higher effectiveness and robustness over existing alternatives in the few-shot case.
Biomass allocation in a subantarctic clonal plant (Acaena magellanica) under grazing by introduced reindeer

Biomass allocation and growth by the clonal plant Acaena magellanica were characterized for three populations grazed by introduced reindeer on the subantarctic island of South Georgia. Annual growth markers (internode lengths) were used to divide each rhizome into current year's shoots, one-year-old and two-year-old rhizome segments. Total dry weights were significantly smaller in grazed than in ungrazed populations. Leaf biomass of current year's shoots was very much lower in grazed shoots. Rhizome length and number of leaves were less affected than dry weight by grazing, and the reindeer grazing thus seems to mainly influence biomass accumulation rather than morphology in Acaena. Interactions with Festuca contracta in both grazed and ungrazed areas were also studied in a two-year competition experiment. No apparent release of soil resources (as measured by an increase in plant growth) was apparent in plots where Festuca was removed, but the current year's shoots of Acaena were smaller and more numerous in these plots than in controls, especially in the ungrazed area.
Learning Two-Layer Neural Networks, One (Giant) Step at a Time

We study the training dynamics of shallow neural networks, investigating the conditions under which a limited number of large batch gradient descent steps can facilitate feature learning beyond the kernel regime. We compare the influence of batch size and that of multiple (but finitely many) steps. Our analysis of a single-step process reveals that while a batch size of n = O ( d ) enables feature learning, it is only adequate for learning a single direction, or a single-index model. In contrast, n = O ( d 2 ) is essential for learning multiple directions and specialization. Moreover, we demonstrate that “hard” directions, which lack the first ℓ Hermite coefficients, remain unobserved and require a batch size of n = O ( d ℓ ) for being captured by gradient descent. Upon iterating a few steps, the scenario changes: a batch-size of n = O ( d ) is enough to learn new target directions spanning the subspace linearly connected in the Hermite basis to the previously learned directions, thereby a staircase property. Our analysis utilizes a blend of techniques related to concentration, projection-based conditioning, and Gaussian equivalence that are of independent interest. By determining the conditions necessary for learning and specialization, our results highlight the interaction between batch size and number of iterations, and lead to a hierarchical depiction where learning performance exhibits a stairway to accuracy over time and batch size, shedding new light on feature learning in neural networks.
Toward Reactive Vision-Guided Walking on Rough Terrain: An Inverse-Dynamics Based Approach

This work presents a method to handle walking on rough terrain using inverse dynamics control and information from a stereo vision system. The ideal trajectories for the center of mass (CoM) and the next position of the feet are given by a pattern generator. The pattern generator is able to automatically find the footsteps for a given direction. Then, an inverse dynamics control scheme relying on a quadratic programming optimization solver is used to let each foot go from its initial to final position, controlling also the CoM and the waist. A 3D model reconstruction of the ground is obtained through the robot cameras located on its head as a stereo vision pair. The model allows the system to know the ground structure where the swinging foot is going to step on. Thus, contact points can be handled to adapt the foot position to the ground conditions.