Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming the ‘ 1 Non-Embeddability Barrier: Algorithms for Product Metrics

A common approach for solving computational problems over a difﬁcult metric space is to embed the “hard” metric into L 1 , which admits efﬁcient algorithms and is thus considered an “easy” metric. This approach has proved successful or partially successful for important spaces such as the edit distance, but it also has inherent limitations: it is provably impossible to go below certain approximation for some metrics. We propose a new approach, of embedding the difﬁcult space into richer host spaces, namely iterated products of standard spaces like ‘ 1 and ‘ ∞ . We show that this class is rich since it contains useful metric spaces with only a constant distortion, and, at the same time, it is tractable and admits efﬁcient algorithms. Using this approach, we obtain for example the ﬁrst nearest neighbor data structure with O (log log d ) approximation for edit distance in non-repetitive strings (the Ulam metric). This approximation is exponentially better than the lower bound for embedding into L 1 . Furthermore, we give constant factor approximation for two other computational problems. Along the way, we answer positively a question posed in [Ajtai, Jayram, Kumar, and Sivakumar, STOC 2002]. One of our algorithms has already found applications for smoothed edit distance over 0-1 strings [Andoni and Krauthgamer, ICALP 2008].
Nerve Net: A Regional-Area Network for Resilient Local Information Sharing and Communications

Information and communications systems in Japan were badly damaged by the great earthquake and tsunami on March 11, 2011. Their importance as societal infrastructure was recognized strongly. Since 2006, NICT has been developing a resilient network, called Nerve Net. It is a regional-area network for access to the Internet and for resilient information sharing and communications even if the access is impossible in emergency situations. It is composed of multiple base stations interconnected each other by using a variety of Ethernet-based wired or wireless transmission systems as optical/metal Ethernet, WiFi, FWA, satellite, and unmanned aerial vehicle (UAV), configuring a mesh-topological network. Each base station consists of a LAN switch and a CPU board for controlling the switch, providing packet transmission and applications using distributed database among all the base stations over a mesh network. Compared with existing Internet-depend, tree-topological network systems, Nerve Net is tolerant to link disconnections and system failures because of mesh structure and fast route switching on layer 2, and is able to continue providing applications such as phone, messaging and information sharing without a connection to the Internet because each base station has functions of DHCP, DNS, SIP proxy, and mobility management. 30-basestation-scale test bed has been operated more than one year and a pilot test has started in Onagawa town, Miyagi prefecture, hit by the tsunami.
Tool Support for Distributed Software Engineering

Developing a software system in collaboration with other partners, and on different geographical locations is a big challenge for organizations. In this article we first discuss a system that automates build and test processes: SoftFab. This system has been successfully applied in practice in the context of multisite projects. Then, we discuss a case where it was applied to a more challenging type of collaboration: a multipartner development environment. Furthermore, we investigate the underlying concepts of SoftFab and use them to define a list of features for systems that support distributed software engineering
Meme selection, variation, and transmission in multi-agent system

The incorporation of intelligent agents into the realms of Virtual Environments (VE) has demonstrated a high potential in improving user interactivity, participation and satisfaction in these virtual worlds. In order to stay relevant to the demands and expectations of users in a Virtual Environment, agents must be able to adapt quickly and learn from other agents in order to perform well. The novelty of this paper lies in leveraging the potential of a new perspective of memetic computing and cultural transmission in a multi-agent environment through cultural evolution. This paper shares the concept from the perspective of not only vertically but also horizontal transmission of knowledge through memes selection, variation and transmission which calls for empirically verifying whether such a paradigm is viable for improving agents' learning capabilities in a multiagent environment. This research seeks to use the notion of memes in order to equip virtual agents with the essential learning capabilities that are closer to humanity.
Motion primitives and 3D path planning for fast flight through a forest

This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives.
Theoretical Investigation Into Longitudinal Cathodal Field Steering in Spinal Cord Stimulation

Objective. When using spinal cord stimulation (SCS) for chronic pain management, precise longitudinal positioning of the cathode is crucial to generate an electrical field capable of targeting the neural elements involved in pain relief. Presently used methods have a poor spatial resolution and lack postoperative flexibility needed for fine tuning and reprogramming the stimulation field after lead displacement or changes in pain pattern. We describe in this article a new method, “electrical field steering,” to control paresthesia in SCS. The method takes advantage of newer stimulator design and a programming technique allowing for “continuous” adjustment of contact combination while controlling stimulation current for each contact separately.
Fast Prototyping and Deployment of Context-Aware Smart Outdoor Environments

The article describes a tool for the fast prototyping and deployment of context-aware applications, in particular to welcome visitors in urban areas. The system has been conceived to guarantee continuous access to everybody, everywhere, at any time, and therefore it does not rely on any special device to connect visitors to the intelligent environment. In fact, we assume that visitors are equipped with low-end mobile phones embedded with bluetooth technology, which provide approximate positioning information. In spite of this, the system must be able to assess the current context in terms of user location, preferences, current activity, and to suggest city-tours and activities which meets the most the visitor's expectations. The article shows how, basing on Google maps API and OWLDL ontologies, the rapid prototyping and rapid deployment of outdoor context-aware applications based on bluetooth messaging and positioning information can be achieved.
PSNUS: Web People Name Disambiguation by Simple Clustering with Rich Features

We describe about the system description of the PSNUS team for the SemEval-2007 Web People Search Task. The system is based on the clustering of the web pages by using a variety of features extracted and generated from the data provided. This system achieves Fα=0.5 = 0.75 and Fα=0.2 = 0.78 for the final test data set of the task.
Association of Cell Adhesion Molecules Contactin-6 and Latrophilin-1 Regulates Neuronal Apoptosis

In view of important neurobiological functions of the cell adhesion molecule contactin-6 (Cntn6) that have emerged from studies on null-mutant mice and autism spectrum disorders patients, we set out to examine pathways underlying functions of Cntn6 using a proteomics approach. We identified the cell adhesion GPCR latrophilin-1 (Lphn1, a.k.a. CIRL1/CL, ADGRL1) as a binding partner for Cntn6 forming together a heteromeric cis-complex. Lphn1 expression in cultured neurons caused reduction in neurite outgrowth and increase in apoptosis, which was rescued by coexpression of Cntn6. In cultured neurons derived from Cntn6-/- mice, Lphn1 knockdown reduced apoptosis, suggesting that the observed apoptosis was Lphn1-dependent. In line with these data, the number of apoptotic cells was increased in the cortex of Cntn6-/- mice compared to wild-type littermate controls. These results show that Cntn6 can modulate the activity of Lphn1 by direct binding and suggests that Cntn6 may prevent apoptosis thereby impinging on neurodevelopment.
Probabilistic Inference Modulo Theories

We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers). While many solutions to probabilistic inference over logic representations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean algorithms with constant complexity in the domain size (the number of values that variables can take). We also detail a solver for summations with difference arithmetic and show experimental results from a scenario in which SGDPLL(T) is much faster than a state-of-the-art probabilistic solver.
Glial cell line‐derived neurotrophic factor promotes the arborization of cultured striatal neurons through the p42/p44 mitogen‐activated protein kinase pathway

Glial cell line‐derived neurotrophic factor (GDNF) promotes the survival or differentiation of several types of neurons. This study examines GDNF‐induced signal transduction and biological effects in cultured striatal neurons. Results show that GDNF addition to striatal cultures transiently increased the protein levels of phosphorylated p42/p44, but did not change the levels of phosphorylated Akt. GDNF effects on phosphorylated p42/p44 levels were blocked by the mitogen‐activated protein kinase (MAPK) pathway specific inhibitors (PD98059 and U0126). Activation of the p42/p44 MAPK pathway by GDNF led to an increase in the degree of dendritic arborization and axon length of both GABA‐ and calbindin‐positive neurons but had no effect on their survival and maturation. These GDNF‐mediated effects were suppressed in the presence of the inhibitor of the MAPK pathway (PD98059). Furthermore, the addition of the phosphatidylinositol 3‐kinase pathway specific inhibitor (LY294002) blocked GDNF‐mediated striatal cell differentiation suggesting that the basal activity of this pathway is needed for the effects of GDNF. Our results indicate that treatment of cultured striatal cells with GDNF specifically activates the p42/p44 MAPK pathway, leading to an increase in the arborization of GABA‐ and calbindin‐positive neurons. © 2005 Wiley‐Liss, Inc.
Fuzzy Logic Temperature Control on Baby Incubator Transport Battery Efficiency

Baby incubator transport is a life support tool used to maintain the body temperature of newborn babies during transportation from one place to another, such as from a hospital to an intensive care center with more complete facilities. The problem that often occurs in transport incubators is limitations in the power system. Baby incubator transport uses a battery as the main power source. However, the limited battery power can cause risks to the baby if there is a problem with the power system or the battery runs out. This study aims to monitor the remaining battery voltage in a transport baby incubator that uses fuzzy logic to control the temperature inside and will compare with the performance of PID control. This research uses a fuzzy logic method to control temperature and maximize battery power. In this study, researchers only looked at the efficiency of the fuzzy logic method in temperature control and the battery that will be used. The research uses a display that will display the battery voltage and current values, battery power percentage, skin temperature, chamber temperature, humidity and the selected temperature control. The module that has been made is then compared with the Digital Multimeter measuring instrument. From the results of data collection, the measurement of the remaining battery voltage between the sensor reading and the measuring instrument has a difference where at a temperature of 34 ºC it is 2.1%, at a temperature of 35 ºC it is 2% and at a temperature of 36 ºC it is 3.9%. When compared to research using PID control, fuzzy logic takes longer to reach the desired temperature and demands more battery power when compared to PID control.
3D PRM based real-time path planning for UAV in complex environment

This paper presents a real-time path planning algorithm for unmanned aerial vehicle (UAV) in complex 3D environments. In this study, the work space is represented by free voxels, which have enough space tolerance to satisfy the safety needs of the UVA. In order to obtain the connectivity of free voxels effectively, the environment is divided into bounding box array and the connectivity evaluation is carried out in each bounding box. A modified Probabilistic Roadmap Method (PRM) is introduced by random sampling in bounding box array to ensure a more evenly distribution of roadmap nodes in 3D space. Based on the voxel connectivity, the selected nodes compose a roadmap, which is applied for path searching by A∗ algorithm for feasible path. Experimental results show that the proposed path planning approach can significantly reduce the computation time than traditional PRM method.
Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

As deep learning continues to evolve, we have observed huge breakthroughs in the fields of medical imaging, video and frame generation, optical character recognition (OCR), and other domains. In the field of data analysis and document processing, the recognition of handwritten numerals plays a crucial role. This work has led to remarkable changes in OCR, historical handwritten document analysis, and postal automation. In this study, we present a novel framework to overcome this challenge, going beyond digit recognition in only one language. Unlike common methods that focus on a limited set of languages, our method provides a comprehensive solution for recognition of handwritten digit images in 12 different languages. These specific languages are chosen because most of them have fairly distant representations in latent space. We utilize transfer learning, as it reduces the computational cost and maintains the quality of enhanced images and the models’ recognition accuracy. Another strength of our approach is the innovative attention-based module called the MRA module. Our experiments confirm that by applying this module, major progress is made in both image quality and the accuracy of handwritten digit recognition. Notably, we reached high precisions, surpassing nearly 2% improvement in specific languages compared to earlier techniques. In this work, we present a robust and cost-effective approach that handles multilingual handwritten numeral recognition across a wide range of languages. The code and further implementation details are available at https://github.com/CVLab-SHUT/HandWrittenDigitRecognition.
Detection of Opinions and Facts. A Cognitive Approach

Amodelofepisodicmemoryisderivedtoproposealgorithms oftextcategorizationwithsemanticspacemodels. PerformancesoftwoalgorithmsnamedTargetvectorand Sub-targetvectorarecontrastedusingtextualmaterialofthe text-miningcontext‘DEFT09’. Theexperiencereportedhere havebeenrealizedontheenglishcorpuswhichiscomposed of articles of the economic newspaper “The Financial Times”. Theaimofthetaskwastocategorizetextsinfunctionofthe factualityorsubjectivitytheyexpressed.Resultsconfirm(i) thattheepisodicmemorymetaphorprovidesaconvenient frameworktoproposeefficientalgorithmfortext categorization,and(ii)thatSub-targetvectoralgorithm outperforms the Target vector algorithm.
Probabilistic computations: Toward a unified measure of complexity

1. Introduction The study of expected running time of algoritruns is an interesting subject from both a theoretical and a practical point of view. Basically there exist two approaches to this study. In the first approach (we shall call it the distributional approach), some "natural" distribution is assumed for the input of a problem, and one looks for fast algorithms under this assumption (see Knuth [8J). For example, in sorting n numbers, it is usually assumed that all n! initial orderings of the numbers are equally likely. A common criticism of this approach is that distributions vary a great deal in real life situations; fu.rthermore, very often the true distribution of the input is simply not known. An alternative approach which attempts to overcome this shortcoming by allowing stochastic moves in the computation has recently been proposed. This is the randomized approach made popular by Habin [lOJ(also see Gill[3J, Solovay and Strassen [13J), although the concept was familiar to statisticians (for exa'1lple, see Luce and Raiffa [9J). Note that by allowing stochastic moves in an algorithm, the input is effectively being randomized. We shall refer to such an algoritlvn as a randomized algorithm. These two approaches lead naturally to two different definitions of intrinsic complexity of a problem, which we term the distributional complexity and the randomized complexity, respectively. (Precise definitions and examples will be given in Sections 2 and 3.) To solidify the ideas, we look at familiar combinatorial problems that can be modeled by decision trees. In particular, we consider (a) the testing of an arbitrary graph property from an adjacency matrix (Section 2), and (b) partial order problems on n We will show that for these two classes of problems, the two complexity measures always agree by virtue of a famous theorem, the Minimax Theorem of Von Neumann [14J. The connection between the two approaches lends itself to applications. With two different views (and in a sense complementary to each other) on the complexity of a problem, it is frequently easier to derive upper and lower bounds. For example, using adjacency matrix representation for a graph, it can be shown that no randomized algorithm can determine 2 the existence of a perfect matching in less than O(n) probes. Such lower bounds to the randomized approach were lacking previously. As another example of application , we can prove that for the partial order problems in (b), assuming uniform …
When Gaze Turns into Grasp

Previous research has provided evidence for a neural system underlying the observation of another person's hand actions. Is the neural system involved in this capacity also important in inferring another person's motor intentions toward an object from their eye gaze? In real-life situations, humans use eye movements to catch and direct the attention of others, often without any accompanying hand movements or speech. In an event-related functional magnetic resonance imaging study, subjects observed videos showing a human model either grasping a target object (grasping condition) or simply gazing (gaze condition) at the same object. These two conditions were contrasted with each other and against a control condition in which the human model was standing behind the object without performing any gazing or grasping action. The results revealed activations within the dorsal premotor cortex, the inferior frontal gyrus, the inferior parietal lobule, and the superior temporal sulcus in both grasping and gaze conditions. These findings suggest that signaling the presence of an object through gaze elicits in an observer a similar neural response to that elicited by the observation of a reach-to-grasp action performed on the same object.
Exosomes and Microvesicles: Identification and Targeting By Particle Size and Lipid Chemical Probes

Exosomes and microvesicles are two classes of submicroscopic vesicle released by cells into the extracellular space. Collectively referred to as extracellular vesicles, these membrane containers facilitate important cell–cell communication by carrying a diverse array of signaling molecules, including nucleic acids, proteins, and lipids. Recently, the role of extracellular vesicle signaling in cancer progression has become a topic of significant interest. Methods to detect and target exosomes and microvesicles are needed to realize applications of extracellular vesicles as biomarkers and, perhaps, therapeutic targets. Detection of exosomes and microvesicles is a complex problem as they are both submicroscopic and of heterogeneous cellular origins. In this Minireview, we highlight the basic biology of extracellular vesicles, and address available biochemical and biophysical detection methods. Detectible characteristics described here include lipid and protein composition, and physical properties such as the vesicle membrane shape and diffusion coefficient. In particular, we propose that detection of exosome and microvesicle membrane curvature with lipid chemical probes that sense membrane shape is a distinctly promising method for identifying and targeting these vesicles.