Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical MCRT Simulation and Inter-Symbol Interference Analysis of Laser Multipath Transmission in Airborne Air-to-Air Channels

To address the issues of signal waveform distortion and inter-symbol interference, both of which lead to performance degradation in airborne laser communication due to multipath transmission in airborne air-to-air channels, theoretical analysis and Monte Carlo ray tracing (MCRT) simulation methods were employed. Based on airborne application conditions, this research conducted numerical simulations of the Gaussian beam multipath transmission in an air-to-air channel, with a focus on analyzing the impact of meteorological conditions, communication distance, transmitter power, and initial pulse width on the pulse time spreading characteristics of received optical signals. In addition, an analysis of these parameters’ impact on the inter-symbol interference (ISI) and bit error rate (BER) was conducted. The research findings can serve as a reference for the design of anti-interference techniques in airborne laser communication links.
Improving the Quality of Multi-resolution Volume Rendering

We propose a novel method to improve the quality of multi-resolution visualizations. We reduce aliasing artifacts by approximating the data distribution with a Gaussian basis function at each level of detail for more accurate rendering at coarser levels of detail. We then show an efficient implementation of our novel Gaussian based approximation scheme and show its superiority using numerical tests and compelling renderings.
Generating Compiler Optimizations from Proofs * Technical Report

We present an automated technique for generating compiler optimizations from examples of concrete programs before and after improvements have been made to them. The key technical insight of our technique is that a proof of equivalence between the original and transformed concrete programs informs us which aspects of the programs are important and which can be discarded. Our technique therefore uses these proofs, which can be produced by translation validation or a proof-carrying compiler, as a guide to generalize the original and transformed programs into broadly applicable optimization rules. We present a category-theoretic formalization of our proof generalization technique. This abstraction makes our technique applicable to logics besides our own. In particular, we demonstrate how our technique can also be used to learn query optimizations for re-lational databases or to aid programmers in debugging type errors. Finally, we show experimentally that our technique enables programmers to train a compiler with application-specific optimizations by providing concrete examples of original programs and the desired transformed programs. We also show how it enables a compiler to learn efficient-to-run optimizations from expensive-to-run super-optimizers.
A simple methodology for soft cost-sensitive classification

Many real-world data mining applications need varying cost for different types of classification errors and thus call for cost-sensitive classification algorithms. Existing algorithms for cost-sensitive classification are successful in terms of minimizing the cost, but can result in a high error rate as the trade-off. The high error rate holds back the practical use of those algorithms. In this paper, we propose a novel cost-sensitive classification methodology that takes both the cost and the error rate into account. The methodology, called soft cost-sensitive classification, is established from a multicriteria optimization problem of the cost and the error rate, and can be viewed as regularizing cost-sensitive classification with the error rate. The simple methodology allows immediate improvements of existing cost-sensitive classification algorithms. Experiments on the benchmark and the real-world data sets show that our proposed methodology indeed achieves lower test error rates and similar (sometimes lower) test costs than existing cost-sensitive classification algorithms.
sEMG-Controlled Soft Exo-Glove for Assistive Rehabilitation Therapies

The movement of the human hand offers various degrees of freedom, enabling efficient performance of dynamic tasks and robust interaction with the environment in a compliant and continuous manner. However, the rigid exoskeleton used in hand rehabilitation limits the user’s freedom of movement, complicating their natural interaction with the environment. In this study, we present a soft exo-glove for assistive rehabilitation actuated by Shape Memory Alloys (SMA), controlled by a surface electromyography (sEMG) hand gesture classifier. Thanks to the actuator type, the soft exo-glove enables slow, smooth, and controlled movements when activated and provides complete control transparency when the device is not active. This advantage enhances the comfort and acceptance of the exo-glove by the patient. On the other hand, the classifier, in conjunction with the control algorithm and the soft exo-glove, offers the potential to use the exo-glove in assistive hand rehabilitation therapy. For user-friendly use, an interface has been developed, enabling the acquisition of new sEMG data from new users, retraining of the classifier, and connection with the soft exo-glove for rehabilitation therapy. The main objective of this study is to demonstrate that the proposed wearable soft exo-glove, along with the control algorithm and the employed classifier, constitutes an effective solution for assistive rehabilitation tasks, as demonstrated with healthy subjects. Furthermore, this solution can be easily adapted to the users’ characteristics and requirements.
Meeting Report: The Role of Environmental Lighting and Circadian Disruption in Cancer and Other Diseases

Light, including artificial light, has a range of effects on human physiology and behavior and can therefore alter human physiology when inappropriately timed. One example of potential light-induced disruption is the effect of light on circadian organization, including the production of several hormone rhythms. Changes in light–dark exposure (e.g., by nonday occupation or transmeridian travel) shift the timing of the circadian system such that internal rhythms can become desynchronized from both the external environment and internally with each other, impairing our ability to sleep and wake at the appropriate times and compromising physiologic and metabolic processes. Light can also have direct acute effects on neuroendocrine systems, for example, in suppressing melatonin synthesis or elevating cortisol production that may have untoward long-term consequences. For these reasons, the National Institute of Environmental Health Sciences convened a workshop of a diverse group of scientists to consider how best to conduct research on possible connections between lighting and health. According to the participants in the workshop, there are three broad areas of research effort that need to be addressed. First are the basic biophysical and molecular genetic mechanisms for phototransduction for circadian, neuroendocrine, and neurobehavioral regulation. Second are the possible physiologic consequences of disrupting these circadian regulatory processes such as on hormone production, particularly melatonin, and normal and neoplastic tissue growth dynamics. Third are effects of light-induced physiologic disruption on disease occurrence and prognosis, and how prevention and treatment could be improved by application of this knowledge.
Impact of data dimensionality reduction on neural based classification: Application to industrial defects

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component’s quality. To complete optical devices diagnosis, a classification phase is mandatory since a number of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can however decrease problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evaluate their possible impact on classification tasks performances.
Improved Gait Recognition using Gradient Histogram Energy Image

We present a new spatio-temporal representation for Gait Recognition, which we call Gradient Histogram Energy Image (GHEI). Similar to the successful Gait Energy Image (GEI), information is averaged over full gait cycles to reduce noise. Contrary to GEI, where silhouettes are averaged and thus only edge information at the boundary is used, our GHEI computes gradient histograms at all locations of the original image. Thus, also edge information inside the person silhouette is captured. In addition, we show that GHEI can be greatly improved using precise segmentation techniques (we use α-matte segmentation). We demonstrate great effectiveness of GHEI and its variants in our experiments on the large and widely used HumanID Gait Challenge dataset. On this dataset we reach a significant performance gain over the current state of the art.
Distributed Robust Learning Based Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics

This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. Furthermore, to address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed. This controller provides real time parameter estimates while maintaining its robustness against disturbances. The overall stability of the proposed method is proved with rigorous mathematical analysis. At last, multiple comprehensive simulation studies have shown the advantages and effectiveness of the proposed method.
Data Extraction as Text Categorization: An Experiment With the MUC-3 Corpus

The data extraction systems studied in the MUC-3 evaluation perform a variety of subtasks in filling out templates. Some of these tasks are quite complex, and seem to require a system to represent the structure of a text in some detail to perform the task successfully. Capturing reference relations between slot fillers, distinguishing between historic and recent events, and many other subtasks appear to have this character.
Update on Multiple-Gene Expression Systems in Plants Delivery of Multiple Transgenes to Plant Cells 1(C)

The use of transgenic technologies for the genetic manipulation of plant species has had a profound impact on basic plant research and biotechnology. Overexpression of heterogonous genes, for example, is widely used for the introduction of novel traits into transgenic crop plants. Overexpression can also be used in combination with down-regulation and controlled expression (e.g. induced, developmentally regulated, or tissue specific) studies as a tool for basic plant research and for functional analysis of native genes in various model plants. Two crucial components are required for the transient and/or stable expression of foreign genes in plant cells. The first is a plasmid vector capable of carrying the foreign gene-encoding sequence and the regulatory elements, i.e. promoter and terminator sequences, needed for its expression in plant cells (thus collectively referred to as an expression cassette). The second is the biological or physical methods by which the vector can be delivered into the target plant cells. With the exception of virusmediated gene expression, where the distinction between the plasmid vector and the vehicle itself is rather blurry, biolistics, polyethylene glycol, and Agrobacteriummediated gene transfer all require plasmid vectors, some of which have been specifically designed for optimal use by a particular method. Thus, for example, small and multicopy plasmids are most useful vectors for biolistics and polyethylene glycol-mediated genetic transformation where large quantities of DNA are typically used in each experiment. On the other hand, single-copy plasmids carrying certain biological features have been specifically designed for use with Agrobacterium as the transformation vehicle. In the past several decades, a remarkable variety of plasmids suitable for the cloning, transfer, and expression of foreign genes in plant cells has been developed (e.g. Simoens et al., 1986; Hajdukiewicz et al., 1994; Hamilton, 1997; Xiang et al., 1999; Hellens et al., 2000b; Earley et al., 2006; Coutu et al., 2007). While these plasmids have proven instrumental in plant biology research and biotechnology, the basic designs of many of these vectors are quite restrictive and rarely permit the cloning and transfer of more than a single target gene (excluding the selection gene, which may or may not be present on the transformation vector) as a single unit into plant cells. The growing interest in dissecting and analyzing complex metabolic pathways and the need to exploit the full potential of multigene traits for plant biotechnology (for review, see Halpin et al., 2001; Halpin and Boerjan, 2003; Capell and Christou, 2004; Tyo et al., 2007) mandate the development of new methods and tools for the delivery and stacking of multiple genes in plant cells. Here, we review some of the methods that can be used for the delivery of multiple genes into plant cells, while focusing mainly on systems that allow the assembly of multigene plant transformation vectors.
UCAN: a unified cellular and ad-hoc network architecture

In third-generation (3G) wireless data networks, mobile users experiencing poor channel quality usually have low data-rate connections with the base-station. Providing service to low data-rate users is required for maintaining fairness, but at the cost of reducing the cell's aggregate throughput. In this paper, we propose the Unified Cellular and Ad-Hoc Network (UCAN) architecture for enhancing cell throughput, while maintaining fairness. In UCAN, a mobile client has both 3G cellular link and IEEE 802.11-based peer-to-peer links. The 3G base station forwards packets for destination clients with poor channel quality to proxy clients with better channel quality. The proxy clients then use an ad-hoc network composed of other mobile clients and IEEE 802.11 wireless links to forward the packets to the appropriate destinations, thereby improving cell throughput. We refine the 3G base station scheduling algorithm so that the throughput gains of active clients are distributed proportional to their average channel rate, thereby maintaining fairness. With the UCAN architecture in place, we propose novel greedy and on-demand protocols for proxy discovery and ad-hoc routing that explicitly leverage the existence of the 3G infrastructure to reduce complexity and improve reliability. We further propose a secure crediting mechanism to motivate users to participate in relaying packets for others. Through extensive simulations with HDR and IEEE 802.11b, we show that the UCAN architecture can improve individual user's throughput by up to 310% and the aggregate throughput of the HDR downlink by up to 60%.
Impact of controlled language on translation quality and post-editing in a statistical machine translation environment

This paper investigates the relationships among controlled language (CL), machine translation (MT) quality, and post-editing (PE). Previous research has shown that the use of CL improves the quality of MT. By extension, we assume that the use of CL will lead to greater productivity or reduced PE effort. The paper examines whether this three-way relationship among CL, MT quality, and PE holds. Beginning with a set of CL rules, we determine what types of CL rules have the greatest cross-linguistic impact on MT quality. We create two sets of English data, one which violates the CL rules and the other which conforms to them. We translate both sets of sentences into four typologically different languages (Dutch, Chinese, Arabic, and French) using MSR-MT, a statistical machine translation system developed at Microsoft. We measure the degree of impact of CL rules on MT quality based on the difference in human evaluation as well as BLEU scores between the two sets of MT output. Finally, we examine whether the use of CL improves productivity in terms of reduced PE effort, using character-based edit-distance.
Definition 2 , Skyline Point : Given a set of data items in a database D , a data item

The rapid growth of data is inevitable, and retrieving the best results that meet the user’s preferences is essential. To achieve this, skylines were introduced in which data items that are not dominated by the other data items in the database are retrieved as results (skylines). In most of the existing skyline approaches, the databases are assumed to be static and complete. However, in real world scenario, databases are not complete especially in multidimensional databases in which some dimensions may have missing values. The databases might also be dynamic in which new data items are inserted while existing data items are deleted or updated. Blindly performing pairwise comparisons on the whole data items after the changes are made is inappropriate as not all data items need to be compared in identifying the skylines. Thus, a novel skyline algorithm, DInSkyline, is proposed in this study which finds the most relevant data items in dynamic and incomplete databases. Several experiments have been conducted and the results show that DInSkyline outperforms the previous works by reducing the number of pairwise comparisons in the range of 52% to 73%.
Methods of Protein Digestive Stability Assay - State of the Art

The evaluation of protein stability in digestive tract is an important topic in various fields of biomedical sciences. The most straightforward task within it is the evaluation of food quality, as during the food processing the digestibility of a protein could fall dramatically. Another task, tightly connected with the previous one, is the evaluation of protein allergenicity. This parameter is shown to be dependent on protein’s digestibility, the less digestible ones having the highest probability to induce immune response. Finally, the task of development of new peptide drugs also requires digestive stability check for per os drug forms that are the most convenient for a patient.
From monoids to near-semirings: the essence of MonadPlus and alternative

It is well-known that monads are monoids in the category of endofunctors, and in fact so are applicative functors. Unfortunately, the benefits of this unified view are lost when the additional nondeterminism structure of MonadPlus or Alternative is required. This article recovers the essence of these two type classes by extending monoids to near-semirings with both additive and multiplicative structure. This unified algebraic view enables us to generically define the free construction as well as a novel double Cayley representation that optimises both left-nested sums and left-nested products.
Effects of non-invasive cervical spinal cord neuromodulation by trans-spinal electrical stimulation on cortico-muscular descending patterns in upper extremity of chronic stroke

Background: Trans-spinal electrical stimulation (tsES) to the intact spinal cord poststroke may modulate the cortico-muscular control in stroke survivors with diverse lesions in the brain. This work aimed to investigate the immediate effects of tsES on the cortico-muscular descending patterns during voluntary upper extremity (UE) muscle contractions by analyzing cortico-muscular coherence (CMCoh) and electromyography (EMG) in people with chronic stroke.Methods: Twelve chronic stroke participants were recruited to perform wrist-hand extension and flexion tasks at submaximal levels of voluntary contraction for the corresponding agonist flexors and extensors. During the tasks, the tsES was delivered to the cervical spinal cord with rectangular biphasic pulses. Electroencephalography (EEG) data were collected from the sensorimotor cortex, and the EMG data were recorded from both distal and proximal UE muscles. The CMCoh, laterality index (LI) of the peak CMCoh, and EMG activation level parameters under both non-tsES and tsES conditions were compared to evaluate the immediate effects of tsES on the cortico-muscular descending pathway.Results: The CMCoh and LI of peak CMCoh in the agonist distal muscles showed significant increases (p < 0.05) during the wrist-hand extension and flexion tasks with the application of tsES. The EMG activation levels of the antagonist distal muscle during wrist-hand extension were significantly decreased (p < 0.05) with tsES. Additionally, the proximal UE muscles exhibited significant decreases (p < 0.05) in peak CMCoh and EMG activation levels by applying tsES. There was a significant increase (p < 0.05) in LI of peak CMCoh of proximal UE muscles during tsES.Conclusion: The cervical spinal cord neuromodulation via tsES enhanced the residual descending excitatory control, activated the local inhibitory circuits within the spinal cord, and reduced the cortical and proximal muscular compensatory effects. These results suggested the potential of tsES as a supplementary input for improving UE motor functions in stroke rehabilitation.
Generalized roof duality for pseudo-boolean optimization

The number of applications in computer vision that model higher-order interactions has exploded over the last few years. The standard technique for solving such problems is to reduce the higher-order objective function to a quadratic pseudo-boolean function, and then use roof duality for obtaining a lower bound. Roof duality works by constructing the tightest possible lower-bounding submodular function, and instead of optimizing the original objective function, the relaxation is minimized. We generalize this idea to polynomials of higher degree, where quadratic roof duality appears as a special case. Optimal relaxations are defined to be the ones that give the maximum lower bound. We demonstrate that important properties such as persistency still hold and how the relaxations can be efficiently constructed for general cubic and quartic pseudo-boolean functions. From a practical point of view, we show that our relaxations perform better than state-of-the-art for a wide range of problems, both in terms of lower bounds and in the number of assigned variables.
A sharing-aware actively pushing cache on CMP

With the increasing amounts of parallel programs, produced to effectively use the parallel computing resources on CMP (chip multi-processor), it is becoming more important to study these programs' data access characteristics on CMP. In this paper, the cache behaviors of parallel programs running on CMP are simulated and analyzed . Based on the the great degree of shared data accesses among different threads and the locality of the shared accesses, an actively pushing cache strategy based on the awareness of shared data is proposed. The shared data is actively pushed to L1 caches of the slower threads before it is needed. This strategy takes the advantage of the potential high on-chip bandwidth while avoids the increased off-chip bandwidth demand of the prefetch technique due to the inaccuracy. Experimental results show that this strategy enhances the processing speeds of the slower threads. The improvement in performance of the parallel programs can be up to 15.1%.