Miracle-dz/UniFAR-FTUs huggingface dataset:
Zebroids : Carrier-based Replacement Policies to Minimize Availability Latency in Vehicular Ad-hoc Networks

Zebroids are mobile devices that carry a referenced data item from a server containing that data item to a client that requested it. An environment employs zebroids in order to minimize the availability latency incurred by the client. A device acts as a zebroid when it is in close vicinity of a server and travels along a path that rendezvous with the client. Over time, the storage capacity of the zebroid might become occupied with data items. To carry the requested data item, it must evict one or more of its existing data items. The primary contribution of this study is to quantify the latency-overhead tradeoff associated with alternative replacement policies that might be employed by a zebroid. We employ a simulation study to quantify the tradeoffs associated with policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and a simple random policy. Obtained results highlight the parameter space where the use of zebroids provides superior performance when compared with a static data placement strategy that does not use zebroids. We verify this and the other observed trends using a simple analytical approximation of latency for a 2D random walk mobility model.
TRANS-CNN-Based Gesture Recognition for mmWave Radar

In order to improve the real-time performance of gesture recognition by a micro-Doppler map of mmWave radar, the point cloud based gesture recognition for mmWave radar is proposed in this paper. Two steps are carried out for mmWave radar-based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave radar sensor IWR1642 is used as a benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx radar sensor is developed in our lab, and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real time with limited training data in comparison with the existing methods.
Bacterial artificial chromosomes establish replication timing and sub-nuclear compartment de novo as extra-chromosomal vectors

Abstract The role of DNA sequence in determining replication timing (RT) and chromatin higher order organization remains elusive. To address this question, we have developed an extra-chromosomal replication system (E-BACs) consisting of ∼200 kb human bacterial artificial chromosomes (BACs) modified with Epstein-Barr virus (EBV) stable segregation elements. E-BACs were stably maintained as autonomous mini-chromosomes in EBNA1-expressing HeLa or human induced pluripotent stem cells (hiPSCs) and established distinct RT patterns. An E-BAC harboring an early replicating chromosomal region replicated early during S phase, while E-BACs derived from RT transition regions (TTRs) and late replicating regions replicated in mid to late S phase. Analysis of E-BAC interactions with cellular chromatin (4C-seq) revealed that the early replicating E-BAC interacted broadly throughout the genome and preferentially with the early replicating compartment of the nucleus. In contrast, mid- to late-replicating E-BACs interacted with more specific late replicating chromosomal segments, some of which were shared between different E-BACs. Together, we describe a versatile system in which to study the structure and function of chromosomal segments that are stably maintained separately from the influence of cellular chromosome context.
Configuration and adaptation of binary software components

Existing black-box adaptation techniques are insufficiently powerful for a large class of real-world tasks. Meanwhile, white-box techniques are language-specific and overly invasive. We argue for the inclusion of special-purpose adaptation features in a configuration language, and outline the benefits of targetting binary representations of software. We introduce Cake, a configuration language with adaptation features, and show how its design is being shaped by two case studies.
Epitaxial perovskite oxide devices fabricated by lift-off technology

Perovskite oxides are a fascinating class of materials owing to their ability to display diverse functional properties while retaining similar crystal structures. In the past decades intense research efforts was focused on achieving novel/enhanced functionalities in perovskite films and interfaces, requiring also the development of new patterning technologies for structuring high quality hetero-interfaces and epitaxial multilayers. This thesis demonstrates the development of a novel lithographic technology, termed as "epitaxial lift-off patterning strategy", for in situ patterning of high temperature grown epitaxial complex perovskite oxide multilayers and detailed investigations of various lift-off fabricated functional devices. Employing the epitaxial lift-off strategy, precise patterns of the conducting heterointerface between LaAlO3 and SrTiO3 (001) were successfully fabricated without using any physical ion etching step and preserving the high quality interfacial transport properties. It was also demonstrated that the lift-off pathway can precisely pattern even more complex interface systems such as SrCuO2-LAO-STO(001), containing high mobility carriers. In addition, the patterning process was modified and integrated with e-beam lithographic process, enabling fabrication of nano-scale devices to enable the future studies of low-dimensional confinement on high mobility interface conductivity as well as interfacial magnetism. To demonstrate the versatility of the method, the thesis includes detailed discussion on the fabrication of all-oxide freestanding piezo-MEMS devices which are capable of functioning as very high sensitivity mass sensors. The fabrication is enabled by the lift-off pathway, facilitating a single step lift-off patterning of heteroepitaxial oxide multilayers. These freestanding epitaxial devices were studied in detail to characterize different intrinsic (material mediated) and extrinsic (from the operational medium) mechanisms causing vibrational damping, which were optimized to achieve a high quality factor of the electromechanical resonance leading to enhanced mass sensitivity. These results establish that the developed lift-off patterning strategy is a potential pathway to accomplish high performance micro and nano-devices incorporating delicate perovskite heterointerfaces and/or functional epitaxial perovskite multilayers.
A Control-oriented Macroscopic Traffic Flow Model for Urban Diverse Intersections

Traffic flow on roads is a non-linear, stochastic phenomenon, with complex interactions between vehicles. This paper discussed the dynamic nature of urban intersections, and presents a novel traffic flow evolution model at a time scale and of a level of detail suitable for on-line estimation, simulation and control. The intersection is considered as interconnected components of urban road network. The macroscopic model proposed here extends the platoon-dispersion model by redefining inflow and outflow vectors, and by also specifying the control vector. Two variables were added to the model, i.e. the turning proportion and the lane assignment scheme. The turning proportion is an obvious variant, and yet the lane assignment is time-varying in some especial condition. Simple stochastic matrix equations described the macroscopic traffic behavior of each movement. This will allow the simulation of quite large road networks by composing many intersection models. The model is validated over real traffic data with abrupt changes in the demand of flow.
Comparing Hierarchical Data in External Memory

We present an external-memory algorithm for computing a minimum-cost edit script between two rooted, ordered, labeled trees. The I/O, RAM, and CPU costs of our algorithm are, respectively, 4 7 5 , 6 , and 1 5 , where and are the input tree sizes, is the block size, , and . This algorithm can make effective use of surplus RAM capacity to quadratically reduce I/O cost. We extend to trees the commonly used mapping from sequence comparison problems to shortest-path problems in edit graphs.
Latent-Variable PCFGs: Background and Applications

Latent-variable probabilistic context-free grammars are latent-variable models that are based on context-free grammars. Non-terminals are associated with latent states that provide contextual information during the top-down rewriting process of the grammar. We survey a few of the techniques used to estimate such grammars and to parse text with them. We also give an overview of what the latent states represent for English Penn treebank parsing, and provide an overview of extensions and related models to these grammars.
A recurrent fuzzy-neural model for dynamic system identification

This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with internal feedback and time delay synapses. The network is trained by means of a novel learning algorithm, named Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM), based on the concept of constrained optimization. The proposed algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structures. An adaptation mechanism of the maximum parameter change is presented as well. The proposed dynamic model, equipped with the learning algorithm, is applied to several temporal problems, including modeling of a NARMA process and the noise cancellation problem. Performance comparisons are conducted with a series of static and dynamic systems and some existing recurrent fuzzy models. Simulation results show that DFNN compares favorably with its competing rivals and thus it can be considered for efficient system identification.
Only when miss universe snatches me: teasing in MMS messaging

When new technology is adopted to everyday life, existing patterns of behavior relevant to the application are transferred to this new media. In this process, some things will also change and evolve.This paper takes an empirical look at mobile multimedia messaging. Within this new technological environment I focus on teasing, an established form of social control prevalent also in MMS (Multimedia Messaging Services) Messaging.I draw from conversation analysis [15] and ethnomethodology [5]. My analysis describes the sequential structures of teases in MMS messages. I will conclude with discussion on the effects images have on mundane social conduct.
Revealing a Novel Potential Pest of Plum Trees in the Caucasus: A Species Resembling the European Leaf-Mining Stigmella plagicolella, Nepticulidae

Simple Summary While feeding, the larvae of pygmy moths (Nepticulidae) consume part of a leaf’s photosynthetic tissue, thus causing damage to the host plant. Although these insects play a natural role in terrestrial ecosystems, they are perceived as pests in horticulture. This study investigates the identity of a potential Nepticulidae pest infesting plum trees in the Caucasus, initially presumed to be the widespread European Stigmella plagicolella (Stainton). Contrary to expectations, our detailed taxonomic analysis revealed a new species of pygmy moth with distinct male genitalia morphology and molecular differences from its European counterpart. This discovery challenges previous records and highlights the need for accurate pest identification. We hypothesize that the proximity of the Caucasus to Europe emphasizes the importance of monitoring and preventing unintentional pest spread, particularly given potential climate change impacts on the distribution and behavior of pests. Our findings contribute to both practical pest management and scientific understanding of evolutionary processes shaping the Caucasian and European biota. Abstract In instances of severe infestations, Nepticulidae larvae can inflict damage on cultivated plants. Previously, it was assumed that the Prunus-feeding Nepticulidae have continuous distribution from Europe to the neighboring Caucasus. During recent fieldwork in the Caucasus, leaf mines were found on plum trees that initially resembled those of Stigmella plagicolella (Stainton) in Europe. However, upon rearing the adults, significant differences emerged, leading to the hypothesis that a different Prunus-feeding species exists in the Caucasus; this challenges previous records in Western Asia. This paper presents the outcomes of our morphological, molecular, and statistical investigations, unveiling S. colchica sp. nov., a previously unknown potential plum-tree pest. Distinguished by male genitalia characteristics, the new species differs from S. plagicolella. The inter- and intraspecific divergences between S. colchica sp. nov. and S. plagicolella range from 3.5% to 6.02%. Moreover, the utilized delimitation algorithms reliably clustered two species separately, as does our mitotype network. A statistical analysis also shows a discernible trend between the leaf mines of S. colchica sp. nov. and S. plagicolella. This unexpected discovery not only documents a new potential pest, enhancing our understanding of the Caucasian fauna, but also contributes to the broader biological inventory.
Adaptive Gait Control of a Biped Robot Based on Realtime Sensing of the Ground Profile

In this paper, realtime control of dynamic biped locomotion usingsensor information is investigated. We used an ultrasonic rangesensor mounted on the robot to measure the distance from the robot tothe ground surface. During the walking control, the sensor data isconverted into a simple representation of the ground profile inrealtime. We also developed a control architecture based on theLinear Inverted Pendulum Mode which we proposed previously fordynamic walking control. Combining the sensory system and thecontrol system enabled our biped robot, Meltran II, to walk overground of unknown profile successfully.
Examining Provider Perspectives Within Housing First and Traditional Programs

Pathways' Housing First represents a radical departure from traditional programs that serve individuals experiencing homelessness and co-occurring psychiatric and substance use disorders. This paper considered two federally funded comparison studies of Pathways' Housing First and traditional programs to examine whether differences were reflected in the perspectives of frontline providers. Both quantitative analysis of responses to structured questions with closed-ended responses and qualitative analysis of open-ended responses to semistructured questions showed that Pathways providers had greater endorsement of consumer values, less endorsement of systems values, and greater tolerance for abnormal behavior that did not result in harm to others than their counterparts in traditional programs. Comparing provider perspectives also revealed an “implementation paradox;” traditional providers were inhibited from engaging consumers in treatment and services without housing, whereas Housing First providers could focus on issues other than securing housing. As programs increasingly adopt a Housing First approach, implementation challenges remain due to an existing workforce habituated to traditional services.
The Difficulty of Detecting Occult Metastases in Patients with Potentially Resectable Pancreatic Cancer: Development and External Validation of a Preoperative Prediction Model

Occult metastases are detected in 10–15% of patients during exploratory laparotomy for pancreatic cancer. This study developed and externally validated a model to predict occult metastases in patients with potentially resectable pancreatic cancer. Model development was performed within the Dutch Pancreatic Cancer Audit, including all patients operated for pancreatic cancer (January 2013–December 2017). Multivariable logistic regression analysis based on the Akaike Information Criteria was performed with intraoperative pathologically proven metastases as the outcome. The model was externally validated with a cohort from the University Hospital of Verona (January 2013–December 2017). For model development, 2262 patients were included of whom 235 (10%) had occult metastases, located in the liver (n = 143, 61%), peritoneum (n = 73, 31%), or both (n = 19, 8%). The model included age (OR 1.02, 95% CI 1.00–1.03), BMI (OR 0.96, 95% CI 0.93–0.99), preoperative nutritional support (OR 1.73, 95% CI 1.01–2.74), tumor diameter (OR 1.60, 95% CI 1.04–2.45), tumor composition (solid vs. cystic) (OR 2.33, 95% CI 1.20–4.35), and indeterminate lesions on preoperative imaging (OR 4.01, 95% CI 2.16–7.43). External validation showed poor discrimination with a C-statistic of 0.56. Although some predictor variables were significantly associated with occult metastases, the model performed insufficiently at external validation.
Ranking definitions with supervised learning methods

This paper is concerned with the problem of definition search. Specifically, given a term, we are to retrieve definitional excerpts of the term and rank the extracted excerpts according to their likelihood of being good definitions. This is in contrast to the traditional approaches of either generating a single combined definition or simply outputting all retrieved definitions. Definition ranking is essential for the task. Methods for performing definition ranking are proposed in this paper, which formalize the problem as either classification or ordinal regression. A specification for judging the goodness of a definition is given. We employ SVM as the classification model and Ranking SVM as the ordinal regression model respectively, such that they rank definition candidates according to their likelihood of being good definitions. Features for constructing the SVM and Ranking SVM models are defined. An enterprise search system based on this method has been developed and has been put into practical use. Experimental results indicate that the use of SVM and Ranking SVM can significantly outperform the baseline methods of using heuristic rules or employing the conventional information retrieval method of Okapi. This is true both when the answers are paragraphs and when they are sentences. Experimental results also show that SVM or Ranking SVM models trained in one domain can be adapted to another domain, indicating that generic models for definition ranking can be constructed.
What's in this paper?: Combining Rhetorical Entities with Linked Open Data for Semantic Literature Querying

Finding research literature pertaining to a task at hand is one of the essential tasks that scientists face on daily basis. Standard information retrieval techniques allow to quickly obtain a vast number of potentially relevant documents. Unfortunately, the search results then require significant effort for manual inspection, where we would rather select relevant publications based on more fine-grained, semantically rich queries involving a publication's contributions, methods, or application domains. We argue that a novel combination of three distinct methods can significantly advance this vision: (i) Natural Language Processing (NLP) for Rhetorical Entity (RE) detection; (ii) Named Entity (NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic generation of RDF triples for both NEs and REs using semantic web ontologies to interconnect them. Combined in a single workflow, these techniques allow us to automatically construct a knowledge base that facilitates numerous advanced use cases for managing scientific documents.
Two-dimensional resonance frequency tuning approach for vibration-based energy harvesting

Vibration-based energy harvesting seeks to convert ambient vibrations to electrical energy and is of interest for, among other applications, powering the individual nodes of wireless sensor networks. Generally it is desired to match the resonant frequencies of the device to the ambient vibration source to optimize the energy harvested. This paper presents a two-dimensionally (2D) tunable vibration-based energy harvesting device via the application of magnetic forces in two-dimensional space. These forces are accounted for in the model separately, with the transverse force contributing to the transverse stiffness of the system while the axial force contributes to a change in axial stiffness of the beam. Simulation results from a COMSOL magnetostatic 3D model agree well with the analytical model and are confirmed with a separate experimental study. Furthermore, analysis of the three possible magnetization orientations between the fixed and tuning magnets shows that the transverse parallel magnetization orientation is the most effective with regards to the proposed 2D tuning approach. In all cases the transverse stiffness term is in general significantly larger than the axial stiffness contribution, suggesting that from a tuning perspective it may be possible to use these stiffness contributions for coarse and fine frequency tuning, respectively. This 2D resonant frequency tuning approach extends earlier 1D approaches and may be particularly useful in applications where space constraints impact the available design space of the energy harvester.
A new verification method for embedded systems

Verification of embedded systems is complicated by the fact that they are composed of digital hardware, analog sensors and actuators, and low level software. In order to verify the interaction of these heterogeneous components, it would be beneficial to have a single modeling formalism that is capable of representing all of these components. To address this need, this paper describes an extended labeled hybrid Petri net (LHPN) model that includes constructs for Boolean, discrete, and continuous variables as well as constructs to model timing. This paper also presents a method to verify these extended LHPNs. Finally, this paper presents a case study to illustrate the application of this model to the verification of a fault-tolerant temperature sensor.