Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Solution of Sparse One-Dimensional Dynamic Programming Problems

Parallel computation offers the potential for quickly solving large computational problems. However, it is often a non-trivial task to effectively use parallel computers. Solution methods must sometimes be reformulated to exploit parallelism; the reformulations are often more complex than their slower serial counterparts. We illustrate these points by studying the parallelization of sparse one-dimensional dynamic programming problems, those which do not obviously admit substantial parallelization. We propose a new method for parallelizing such problems, develop analytic models which help us to identify problems which parallelize well, and compare the performance of our algorithm with existing algorithms on a multiprocessor. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.
Approximate Dynamic Programming via a Smoothed Linear Program Citation

We present a novel linear program for the approximation of the dynamic programming costto-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural ‘projection’ of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program—the ‘smoothed approximate linear program’— is distinct from such approaches and relaxes the restriction to lower bounding approximations in an appropriate fashion while remaining computationally tractable. Doing so appears to have several advantages: First, we demonstrate substantially superior bounds on the quality of approximation to the optimal cost-to-go function afforded by our approach. Second, experiments with our approach on a challenging problem (the game of Tetris) show that the approach outperforms the existing LP approach (which has previously been shown to be competitive with several ADP algorithms) by an order of magnitude.
Explorer Learning Fast Sparsifying Transforms

Given a dataset, the task of learning a transform that allows sparse representations of the data bears the name of dictionary learning. In many applications, these learned dictionaries represent the data much better than the static well-known transforms (Fourier, Hadamard etc.). The main downside of learned transforms is that they lack structure and therefore they are not computationally efficient, unlike their classical counterparts. These posse several difficulties especially when using power limited hardware such as mobile devices, therefore discouraging the application of sparsity techniques in such scenarios. In this paper we construct orthogonal and nonorthogonal dictionaries that are factorized as a product of a few basic transformations. In the orthogonal case, we solve exactly the dictionary update problem for one basic transformation, which can be viewed as a generalized Givens rotation, and then propose to construct orthogonal dictionaries that are a product of these transformations, guaranteeing their fast manipulation. We also propose a method to construct fast square but nonorthogonal dictionaries that are factorized as a product of few transforms that can be viewed as a further generalization of Givens rotations to the non-orthogonal setting. We show how the proposed transforms can balance very well data representation performance and computational complexity. We also compare with classical fast and learned general and orthogonal transforms.
Comparison of the Paths From Consumer Involvement Types To Ad Responses Between Corporate Advertising And Product Advertising

The study reported here examines knowledge derived from past work on involvement in product advertising and explores whether consumer involvement works the same way when consumers respond to corporate ads. The use of corporate advertising in the United States has increased, but only limited research has been done to explore the nature of it. The study found key differences between product and corporate ads in terms of ad involvement effects on consumers' attitude/behavioral intentions. In corporate advertising, predispositional involvement influences consumers' ad attitudes and behavioral intentions only when mediated by ad involvement, whereas predispositional involvement has both a direct and an indirect influence on attitude and intentions in product ads. A key implication of this is that creating advertising involvement is even more critical in the corporate advertising setting as compared to product advertising. In explaining the results, the authors posit reasons such differences exist. Also, the results contribute to the debate on potential outcomes of corporate advertising by showing that corporate advertising can lead to increased product purchase intentions for a corporations' products even though the corporate ads are not product-focused.
Designing of two-voltage power supply systems for 110 kV/medium voltage substations

The paper tackles the problems of designing redundancy feeding lines for supplying medium voltage (MV) municipal power networks in the conditions of increased load of 110 kV/MV transformer stations. Designed feeding systems lie in direct MV connections between 110 kV/MV transformer stations. The 110 kV/MV transformers were planned to be replaced and MV switching stations modernized. A mathematical model with an objective function for minimizing the total capital cost of the planned network was worked out. A genetic algorithm was used for solving the optimization task. Exemplary calculations for two power networks, results of analyses and final conclusions close the paper.
Compact Topological Map

Several work have shown that a topological map is a good tool to describe and handle a labeled image. Indeed, it allows describing the full topology of the image: its subdivision in cells (vertices, edges, faces) and all the incidence and adjacency relations between these cells, contrary to simpler models like region adjacency graphs. Thanks to this description, a topological map allows computing and updating several features that can mix topological and geometrical information. One major problem of topological map, that often limits its use, is the memory size required by its representation. In this paper, we solve this drawback by proposing two compact representations of topological maps that offer different space/time efficiency compromises. These two representations are based on the links between topological maps and linel maps, where each edge corresponds to a linel, and on an implicit encoding of linel darts. Our experiments show a major reduction on memory space comparing to previous encoding, and allows us to envisage to process big images without memory constraint.
Feasible Region: An Actuation-Aware Extension of the Support Region

In legged locomotion, the projection of the robot's Center of Mass (CoM) being inside the convex hull of the contact points is a commonly accepted sufficient condition to achieve static balancing. However, some of these configurations cannot be realized because the joint-torques required to sustain them would be above their limits (actuation limits). In this article, we rule out such configurations and define the feasible region, a revisited support region that guarantees both global static stability in the sense of tip-over and slippage avoidance and of existence of a set of joint-torques that are able to sustain the robot's body weight. We show that the feasible region can be employed for the online selection of feasible footholds and CoM trajectories to achieve statically stable locomotion on rough terrains, also in presence of load-intensive tasks. Key results of our approach include the efficiency in the computation of the feasible region using an Iterative Projection (IP) algorithm and the successful execution of hardware experiments on the HyQ robot, that was able to negotiate obstacles of moderate dimensions while carrying an extra 10-kg payload.
Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

The initial centroid is a fairly challenging problem in the k-means method because it can affect the clustering results. In addition, choosing the starting centroid of the cluster is not always appropriate, especially, when the number of groups increases. The random technique is often used to overcome this problem, but it produces a variety of solutions because the initial centroid initialization uses a random way. Therefore, we propose Distance Part’s (DP) method to solve initial cluster initialization problems on the k-means method (DP-KMeans). DP-KMeans is a new approach for initial centroid; this approach works by way of data is partitioned based on the sorted data from largest to smallest value distance to the reference point. This method is called by DP-KMeans, because the data of partition is based on the sorted data distance to the reference point. In this study, four datasets by the UCI machine learning repository are used to evaluate the proposed method. The output process shows that the proposed method produces influential results with the lowest sum of square error for k = 4 are 10.606, 705.144, 13.450, 97.767. Finally, it can be concluded that DP-KMeans can improve the k-means performance on the initial centroid problem.
Direct Object Recognition Without Line-Of-Sight Using Optical Coherence

Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain information of the hidden object. It is possible to realize non-line-of-sight (NLOS) recognition with these speckle patterns. We introduce a novel approach based on speckle pattern recognition with deep neural network, which is simpler and more robust than other NLOS recognition methods. Simulations and experiments are performed to verify the feasibility and performance of this approach.
Mechamagnets: Designing and Fabricating Haptic and Functional Physical Inputs with Embedded Magnets

We present Mechamagnets, a technique for facilitating the design and fabrication of haptic and functional inputs for physical interfaces. This technique consists of a set of 3D printed spatial constraints which facilitate different physical movements, as well as unpowered haptic profiles created by embedding static magnets in 3D printed parts. We propose the Mechamagnets taxonomy to map the design space of this technique for designers and makers. Furthermore, we leverage the use of magnets by instrumenting these objects with linear Hall effect sensors to create functional digital inputs. We showcase Mechamagnets with a series of novel physical interfaces made with this technique.
A Comprehensive Comparison of Unsupervised Network Representation Learning Methods

—There has been appreciable progress in unsupervised network representation learning (UNRL) approaches over graphs recently with ﬂexible random-walk approaches, new optimization objectives and deep architectures. However, there is no common ground for systematic comparison of embeddings to understand their behavior for different graphs and tasks. In this paper we theoretically group different approaches under a unifying framework and empirically investigate the effectiveness of different network representation methods. In particular, we argue that most of the UNRL approaches either explicitly or implicit model and exploit context information of a node. Consequently, we propose a framework that casts a variety of approaches – random walk based, matrix factorization and deep learning based – into a uniﬁed context-based optimization function. We systematically group the methods based on their similarities and differences. We study the differences among these methods in detail which we later use to explain their performance differences (on downstream tasks). We conduct a large-scale empirical study considering 9 popular and recent UNRL techniques and 11 real-world datasets with varying structural properties and two common tasks – node classiﬁcation and link prediction. We ﬁnd that there is no single method that is a clear winner and that the choice of a suitable method is dictated by certain properties of the embedding methods, task and structural properties of the underlying graph. In addition we also report the common pitfalls in evaluation of UNRL methods and come up with suggestions for experimental design and interpretation of results.
Single- and Multi-FPGA Acceleration of Dense Stereo Vision for Planetary Rovers

Increased mobile autonomy is a vital requisite for future planetary exploration rovers. Stereo vision is a key enabling technology in this regard, as it can passively reconstruct in three dimensions the surroundings of a rover and facilitate the selection of science targets and the planning of safe routes. Nonetheless, accurate dense stereo algorithms are computationally demanding. When executed on the low-performance, radiation-hardened CPUs typically installed on rovers, slow stereo processing severely limits the driving speed and hence the science that can be conducted in situ. Aiming to decrease execution time while increasing the accuracy of stereo vision embedded in future rovers, this article proposes HW/SW co-design and acceleration on resource-constrained, space-grade FPGAs. In a top-down approach, we develop a stereo algorithm based on the space sweep paradigm, design its parallel HW architecture, implement it with VHDL, and demonstrate feasible solutions even on small-sized devices with our multi-FPGA partitioning methodology. To meet all cost, accuracy, and speed requirements set by the European Space Agency for this system, we customize our HW/SW co-processor by design space exploration and testing on a Mars-like dataset. Implemented on Xilinx Virtex technology, or European NG-MEDIUM devices, the FPGA kernel processes a 1,120 × 1,120 stereo pair in 1.7s−3.1s, utilizing only 5.4−9.3 LUT6 and 200−312 RAMB18. The proposed system exhibits up to 32× speedup over desktop CPUs, or 2,810× over space-grade LEON3, and achieves a mean reconstruction error less than 2cm up to 4m depth. Excluding errors exceeding 2cm (which are less than 4% of the total), the mean error is under 8mm.
Passive Vision Region-Based Road Detection

We present a literature review to analyze the state of the art in the area of road detection based upon frontal images. For this purpose, a systematic literature review (SLR) was conducted that focuses on analyzing region-based works, since they can adapt to different surface types and do not depend on road geometry or lane markings. Through the comprehensive study of publications in a 11-year time frame, we analyze the methods that are being used, on which types of surface they are applied, whether they are adaptive in relation to surface changes, and whether they are able to distinguish possible faults or changes in the road, such as potholes, shadows, and puddles.
Hybrid Approaches for our Participation to the n2c2 Challenge on Cohort Selection for Clinical Trials

Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very precise set of rules to detect some samples of positive and negative patients. This silver standard is then used to train a traditional supervised model. The second method is a terminology-based approach where a medical expert selects the appropriate concepts, and a procedure is defined to search the terms and check the structural or temporal constraints. Results: On the n2c2 dataset containing annotated data about 13 selection criteria on 288 patients, we obtained an overall F1-measure of 0.8969, which is the third best result out of 45 participant teams, with no statistically significant difference with the best-ranked team. Discussion: Both approaches obtained very encouraging results and apply to different types of criteria. The weakly supervised method requires explicit descriptions of positive and negative examples in some reports. The terminology-based method is very efficient when medical concepts carry most of the relevant information. Conclusion: It is unlikely that much more annotated data will be soon available for the task of identifying a wide range of patient phenotypes. One must focus on weakly or non-supervised learning methods using both structured and unstructured data and relying on a comprehensive representation of the patients.
WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding

Variational Auto-encoders (VAEs) have been very successful as methods for forming compressed latent representations of complex, often high-dimensional, data. In this paper, we derive an alternative variational lower bound from the one common in VAEs, which aims to minimize aggregate information loss. Using our lower bound as the objective function for an auto-encoder enables us to place a prior on the bulk statistics, corresponding to an aggregate posterior for the entire dataset, as opposed to a single sample posterior as in the original VAE. This alternative form of prior constraint allows individual posteriors more flexibility to preserve necessary information for good reconstruction quality. We further derive an analytic approximation to our lower bound, leading to an efficient learning algorithm - WiSE-ALE. Through various examples, we demonstrate that WiSE-ALE can reach excellent reconstruction quality in comparison to other state-of-the-art VAE models, while still retaining the ability to learn a smooth, compact representation.
Finding cones for K-cooperative systems

We design and test a cone finding algorithm to robustly address nonlinear system analysis through differential positivity. The approach provides a numerical tool to study multi-stable systems, beyond Lyapunov analysis. The theory is illustrated on two examples: a consensus problem with some repulsive interactions and second order agent dynamics, and a controlled duffing oscillator.
DOOR: Ethnicity in Artificial Intelligence

DOOR is an artwork that aims at exposing some of the social and political impact of artificial intelligence, computer vision, and automation. The project uses a commercially available computer vision system that predicts the interactor's ethnicity, and locks or unlocks itself depending on this prediction. The artwork showcases a possible use of computer vision making explicit the fact that every technological implantation crystallises a political worldview.
Venice

Consolidated server racks are quickly becoming the standard infrastructure for engineering, business, medicine, and science. Such servers are still designed much in the way when they were organized as individual, distributed systems. Given that many fields rely on big-data analytics substantially, its cost-effectiveness and performance should be improved, which can be achieved by flexibly allowing resources to be shared across nodes. Here we describe Venice, a family of data-center server architectures that includes a strong communication substrate as a first-class resource. Venice supports a diverse set of resource-joining mechanisms that enables applications to leverage non-local resources efficiently. We have constructed a hardware prototype to better understand the implications of design decisions about system support for resource sharing. We use it to measure the performance of at-scale applications and to explore performance, power, and resource-sharing transparency tradeoffs (i.e., how many programming changes are needed). We analyze these tradeoffs for sharing memory, accelerators, and NICs. We find that reducing/hiding latency is particularly important, the chosen communication channels should match the sharing access patterns of the applications, and of which we can improve performance by exploiting inter-channel collaboration.
Exploring High-Order User Preference on the Knowledge Graph for Recommender Systems

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The outward propagation version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user’s potential interests along links in the KG. The multiple “ripples” activated by a user’s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The inward aggregation version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users’ long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.
Exploring Projection Based Mixed Reality with Tangibles for Nonsymbolic Preschool Math Education

A child's early math development can stem from interactions with the physical world. Accordingly, current tangible interaction studies focus on preschool children's formal (symbolic) mathematics, i.e. number knowledge. However, recent developmental studies stress the importance of nonsymbolic number representation in math learning, i.e. understanding quantity relations without counting(more/less). To our knowledge, there are no tangible systems based on this math concept. We developed an initial tangible based mixed-reality(MR) setup with a small tabletop projector and depth camera. Our goal was observing children's interaction with the setup to guide our further design process towards developing nonsymbolic math training. In this paper, we present our observations from sessions with four 3-to-5 year old children and discuss their meaning for future work. Initial clues show that our MR setup leads to exploratory and mindful interactions, which might be generalizable to other tangible MR systems for child education and could inspire interaction design studies.