Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-organ segmentation in abdominal CT images

Automated segmentation of multiple organs in CT data of the upper abdomen is addressed. In order to explicitly incorporate the spatial interrelations among organs, we propose a method for finding and representing the interrelations based on canonical correlation analysis. Furthermore, methods are developed for constructing and utilizing the statistical atlas in which inter-organ constraints are explicitly incorporated to improve accuracy of multi-organ segmentation. The proposed methods were tested to perform segmentation of eight abdominal organs (liver, spleen, kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from various imaging conditions of CT datasets. 87 datasets acquired at two institutions were used for the validation. Significant accuracy improvement was observed for several organs in comparison with the conventional method.
Wishing Upon a Star: How apprentice-master similarity, status and career stage affect critics’ evaluations of former apprentices in the haute cuisine industry

This article explores how the similarity between a master’s and former apprentice’s products influences critics’ evaluations of creative professionals’ work. Through apprenticeships with well-known masters, creative professionals manage the competing demands for novelty and familiarity typical of creative industries and find their optimal balance. To gain positive evaluations, creative workers must demonstrate their offerings’ comparability with their former master’s, yet some degrees of novelty. An analysis of international haute cuisine chefs reveals an inverted U-shaped relationship between similarity of apprentice’s and master’s products, and critics’ evaluations. Furthermore, the analysis shows that apprenticeships with high-status masters and those that occur late in the apprentice’s career change this inverted U-shaped relationship into a positive one. The article concludes by highlighting the consequences of being a mainstream or a maverick with respect to the master in the creative industry and by discussing possible strategies for creative professionals to gain critics’ recognition.
A Visual Criterion for Identifying Ito Diffusions as Martingales or Strict Local Martingales

It is often important, in applications of stochastic calculus to financial modelling, to know whether a given local martingale is a martingale or a strict local martingale. We address this problem in the context of a time-homogenous diffusion process with a finite lower boundary, presented as the solution of a driftless stochastic differential equation. Our main theorem demonstrates that the question of whether or not this process is a martingale may be decided simply by examining the slope of a certain increasing function. Further results establish the connection between our theorem and other results in the literature, while a number of examples are provided to illustrate the use of our criterion.
Modality independence of order coding in working memory: Evidence from cross-modal order interference at recall

Working memory researchers do not agree on whether order in serial recall is encoded by dedicated modality-specific systems or by a more general modality-independent system. Although previous research supports the existence of autonomous modality-specific systems, it has been shown that serial recognition memory is prone to cross-modal order interference by concurrent tasks. The present study used a serial recall task, which was performed in a single-task condition and in a dual-task condition with an embedded memory task in the retention interval. The modality of the serial task was either verbal or visuospatial, and the embedded tasks were in the other modality and required either serial or item recall. Care was taken to avoid modality overlaps during presentation and recall. In Experiment 1, visuospatial but not verbal serial recall was more impaired when the embedded task was an order than when it was an item task. Using a more difficult verbal serial recall task, verbal serial recall was also more impaired by another order recall task in Experiment 2. These findings are consistent with the hypothesis of modality-independent order coding. The implications for views on short-term recall and the multicomponent view of working memory are discussed.
Beamforming via Large and Dense Antenna Arrays Above a Clutter

The paper sheds light on the beamforming (BF) performance of large (potentially unconstrained in size) as well as dense (but physically constrained in size) antenna arrays when equipped with arbitrarily many elements. Two operational modes are investigated: Single-layer BF and multi-layer BF. In the first mode, a realistic BF criterion namely the average BF gain is revisited and employed to understand the far-field and the near-field effects on the BF performance of large-scale antennas above a clutter. The diminishing throughput returns in a single-layer BF mode versus the number of antennas necessitate multi-layering. In the multi-layer BF mode, the RF coverage is divided into a number of directive non-overlapping sector-beams in a deterministic manner within a multi-user multi-input multi-output (MIMO) system. The optimal number of layers that maximizes the user's sum-rate given a constrained antenna array is found as a compromise between the multiplexing gain (associated with the number of sector-beams) and the inter-beam interference, represented by the side lobe level (SLL).
Casa de la Lhéngua: a set of language resources and natural language processing tools for Mirandese

This paper describes the efforts for the construction of Language Resources and NLP tools for Mirandese, a minority language spoken in North-eastern Portugal, now available on a community-led portal, Casa de la Lhéngua. The resources were developed in the context of a collaborative citizenship project led by Microsoft, in the context of the creation of the first TTS system for Mirandese. Development efforts encompassed the compilation of a corpus with over 1M tokens, the construction of a GTP system, syllable-division, inflection and a Part-of-Speech (POS) tagger modules, leading to the creation of an inflected lexicon of about 200.000 entries with phonetic transcription, detailed POS tagging, syllable division, and stress mark-up. Alongside these tasks, which were made easier through the adaptation and reuse of existing tools for closely related languages, a casting for voice talents among the speaking community was conducted and the first speech database for speech synthesis was recorded for Mirandese. These resources were combined to fulfil the requirements of a well-tested statistical parameter synthesis model, leading to an intelligible voice font. These language resources are available freely at Casa de la Lhéngua, aiming at promoting the development of real-life applications and fostering linguistic research on Mirandese.
QoS-Aware Packet Chunking Schemes for M2M Cloud Services

With the emergence of machine-to-machine (M2M) communications, huge numbers of devices have been connected and massive amounts of traffic are exchanged. However, since M2M applications typically generate packets of very small size, the number of incoming packets is becoming larger than the maximum number of packets that a router can handle per second, and network throughput is decreased. That is, network resources cannot be used efficiently. Therefore, the M2M cloud network operator makes chunks of these small packets, but a waiting time for chunking is inevitable, and thus application requirements may not be satisfied. This paper proposes new packet chunking schemes to both meet the QoS application requirement and improve the router's achievable throughput. The proposed schemes need multiple buffers and then classify the arrival packets to one buffer based on their acceptable waiting time. After that, the chunk packet is made based on an attribute of each packet to meet the application requirements. Through simulation experiments, we showed that the proposed scheme attains excellent performance even in cases of overloaded and imbalanced traffic.
Intravesical radiofrequency induced hyperthermia enhances mitomycin C accumulation in tumour tissue

Abstract Introduction: Non-muscle invasive bladder cancer (NMIBC) is a highly recurrent disease with potential progression to muscle invasive disease despite the standard bladder instillations with mitomycin C (MMC) or Bacille Calmette–Guérin immunotherapy. Therefore, alternatives such as radiofrequency-induced chemohyperthermia (RF-CHT) with MMC are being investigated. The mechanism explaining the efficacy of RF-CHT is only partly understood. We examined whether RF-CHT results in higher MMC tissue concentrations as compared to cold MMC instillation. Patients and methods: Prior to a planned transurethral resection of bladder tumour (TURBT), patients with stage Ta NMIBC were allocated to either (1) cold MMC instillation or (2) RF-CHT. After MMC instillation, three biopsies were taken of both normal and tumour tissue. Biopsies were snap-frozen and MMC tissue concentrations were analysed using ultra-performance liquid chromatography. Results: Eleven patients were included of which six received RF-CHT. Ten patients had TaG2-LG/HG papillary tumours at pathology. One patient in the RF-CHT group appeared to be free of malignancy and was excluded from the analysis as no tumour biopsies were available. The median MMC concentration in tumour tissue was higher in the RF-CHT group (median 665.00 ng/g vs. 63.75 ng/g, U = 51.0, p = 0.018). Moreover, in both techniques the MMC concentration was lower in normal tissue compared to tumour tissue. Tissue MMC concentration measurements varied substantially within, and between, different patients from the same group. Conclusion: Intravesical RF-CHT results in higher tumour MMC concentrations vs. cold MMC instillation which contributes to its superior efficacy.
Electricity Cost Minimization in Distributed Clouds by Exploring Heterogeneity of Cloud Resources and User Demands

Distributed clouds, consisting of multiple data centers located at different geographical locations, provide a plethora of services to users. They however consume enormous amounts of electricity to power their data centers. The electricity bill is almost 30%-50% of their operational costs. Minimizing the electricity cost of distributed clouds thus is crucial to reduce the operational cost of their cloud service providers. In this paper, we study the problem of minimizing the electricity cost of a distributed cloud, by exploring the heterogeneities of cloud resources and user demands, and time-varying electricity prices, for which we first propose a two-stage optimization framework: dispatching user task requests to different data centers by incorporating the resource demands of the task requests, the workload, and the electricity price in each data center, and energy consumption profiles of different servers in each data center; followed by further energy optimization within each data center through consolidating Virtual Machines (VMs) to different servers to improve the resource utilization ratio. One critical constraint on such task dispatch and VM consolidation is to meet various user Service Level Agreements (SLAs), which include average task scheduling delays and resource demand violation limitations. Under the proposed framework, we then devise efficient scheduling algorithms for task dispatching and VM consolidations, while keeping both the average scheduling delay and resource demand violation limitation of each admitted task met. We finally evaluate the performance of the proposed algorithms through experimental simulations, using real data sets - the real electricity prices and task traces. Experimental simulation results demonstrate that the proposed algorithms are promising.
Three-Skips CNN for road scene semantic segmentation

In this paper we propose a deep learning architecture to make the best use of global and local information for pixel-wise semantic segmentation. The architecture of three-skips CNN is built with convolutional layers in VGG16 network and its mirrored convolutional layers. Our architecture aims to road scene understanding. In order to save memory and computational time, we use unpooling layers to map low resolution feature maps to the input resolution. We introduce three skip architectures which combine local information and global information to produce accurate and detailed segmentations. Besides, we present the median balance method to deal with class unbalance problem in road scene datasets. Thorough evaluations on CamVid dataset demonstrate our approach has state-of-the-art performance and less computational time.
The Moser-Tardos Framework with Partial Resampling

The resampling algorithm of Moser & Tardos is a powerful approach to develop versions of the Lovasz Local Lemma. We develop a partial resampling approach motivated by this methodology: when a bad event holds, we resample an appropriately-random subset of the set of variables that define this event, rather than the entire set as in Moser & Tardos. This leads to several improved algorithmic applications in scheduling, graph transversals, packet routing etc. For instance, we improve the approximation ratio of a generalized D-dimensional scheduling problem studied by Azar & Epstein from O(D) to O(log D/ log log D), and settle a conjecture of Szabo & Tardos on graph transversals asymptotically.
The Effects of Avatars on Presence in Virtual Environments for Persons with Mobility Impairments

The main question we ask is: How do avatars affect presence specifically for Persons with Mobility Impairments (PMIs)? For example, PMIs' deficits in the proprioceptive sense could affect their body perception in immersive virtual reality, which could impact presence. To investigate this we replicated the classic virtual pit experiment and included a responsive full body avatar (or lack thereof) as a 3D user interface. We recruited from two different populations: 11 PMIs and another 11 Persons without Mobility Impairments (PNMIs) as a control. Each PNMI was matched to a PMI based on age, weight, height, and prior VE exposure. Results of this study indicate that avatars elicit a higher sense of presence for PMIs than for PNMIs. In addition, results suggest that PMIs are easier to immerse in VEs than PNMIs, which may further motivate the future use of VE technology for PMIs.
Comparing RNNs and log-linear interpolation of improved skip-model on four Babel languages: Cantonese, Pashto, Tagalog, Turkish

Recurrent neural networks (RNNs) are a very recent technique to model long range dependencies in natural languages. They have clearly outperformed trigrams and other more advanced language modeling techniques by using non-linearly modeling long range dependencies. An alternative is to use log-linear interpolation of skip models (i.e. skip bigrams and skip trigrams). The method as such has been published earlier. In this paper we investigate the impact of different smoothing techniques on the skip models as a measure of their overall performance. One option is to use automatically trained distance clusters (both hard and soft) to increase robustness and to combat sparseness in the skip model. We also investigate alternative smoothing techniques on word level. For skip bigrams when skipping a small number of words Kneser-Ney smoothing (KN) is advantageous. For a larger number of words being skipped Dirichlet smoothing performs better. In order to exploit the advantages of both KN and Dirichlet smoothing we propose a new unified smoothing technique. Experiments are performed on four Babel languages: Cantonese, Pashto, Tagalog and Turkish. RNNs and log-linearly interpolated skip models are on par if the skip models are trained with standard smoothing techniques. Using the improved smoothing of the skip models along with distance clusters, we can clearly outperform RNNs by about 8-11 % in perplexity across all four languages.
Eurographics Symposium on Geometry Processing 2009 a Concise and Provably Informative Multi-scale Signature Based on Heat Diffusion

We propose a novel point signature based on the properties of the heat diffusion process on a shape. Our signature, called the Heat Kernel Signature (or HKS), is obtained by restricting the well-known heat kernel to the temporal domain. Remarkably we show that under certain mild assumptions, HKS captures all of the information contained in the heat kernel, and characterizes the shape up to isometry. This means that the restriction to the temporal domain, on the one hand, makes HKS much more concise and easily commensurable, while on the other hand, it preserves all of the information about the intrinsic geometry of the shape. In addition, HKS inherits many useful properties from the heat kernel, which means, in particular, that it is stable under perturbations of the shape. Our signature also provides a natural and efficiently computable multi-scale way to capture information about neighborhoods of a given point, which can be extremely useful in many applications. To demonstrate the practical relevance of our signature, we present several methods for non-rigid multi-scale matching based on the HKS and use it to detect repeated structure within the same shape and across a collection of shapes.
Band selection for gas detection in hyperspectral images

The objective of this work is to find the minimal number of spectral bands needed to detect a specific gas in a hyperspectral cube, in order to develop a hyperspectral gas sensor in the future. In the framework of the research, a band selection method is developed in order to correctly detect a specific gas, using few of the hundreds of spectral bands available in the data. It shows that for a synthetic cube, using a correlation matrix built only from non-gas pixels, the detection improves as more spectral bands are in use, but with relatively few bands we still get good detection.
Thuniopsis: A New Orchid Genus and Phylogeny of the Tribe Arethuseae (Orchidaceae)

An investigation of a questionable orchid led to the discovery of a new genus and species Thuniopsis cleistogama, endemic to Yunnan province, China. It is characterized by having a subglobose corm, a spike-like (racemose) inflorescence, half opened and spurless flowers, a collar-shaped stigma and subglobose capsules. Based on DNA sequence data from three gene regions (nuclear ribosomal ITS, chloroplast matK and trnL), we investigated its phylogenetic position within the tribe Arethuseae. Phylogenies using maximum likelihood and Bayesian inference support the recognition of Thuniopsis as a distinct genus, and suggest its close relationship to the genera Bletilla, Dilochia, and Thunia. The new genus is circumscribed and a description and illustrations of the new species are provided. The phylogenetic relationships among the genera in Arethuseae are accessed. Moreover, our phylogeny also shed light on the phylogenetic positions of several genera which, to date, remain uncertain.
Effects of pre-session centering for therapists on session presence and effectiveness

Abstract The present study tested whether engaging in a mindfulness centering exercise 5 minutes before a session could have a positive impact on therapy, in particular on the therapists' ability to remain present in session and on session outcomes. Results indicated that therapists perceived themselves as being more present in session when they prepared for their sessions by engaging in a mindfulness centering exercises (d=.45), while clients perceived their therapists as being highly present regardless of whether their therapist completed the mindfulness centering exercise. Clients did, however, perceive the sessions as being more effective when their therapists engaged in the mindfulness centering exercise prior to the start of the session (d=.52).
Multi-View and 3D Deformable Part Models

As objects are inherently 3D, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).
Robust methods for reconstructing brain activity and functional connectivity between brain sourceswith MEG/EEG data

The synchronous brain activity measured via magentoencephalography (MEG) or electroencephalography (EEG) arises from current dipoles located throughout the cortex. The number, location, time-course, and orientation of these dipoles, called sources, are estimated using a source localization algorithm. Source localization remains a challenging task, one that is significantly compounded by the effects of source correlations and interference from spontaneous brain activity and sensor noise. Likewise, assessing the interactions between the individual sources, known as functional connectivity, is also confounded by noise and correlations in the sensor recordings. In addition, computational complexity has been an obstacle to computing functional connectivity. This paper derives an empirical Bayesian method for performing source localization with MEG and EEG data that includes noise and interference suppression. We demonstrate that this method surpasses standard methods of localization. In addition, we demonstrate that brain source activity inferred from this algorithm is better suited to uncover the interactions between brain areas.
Limited Efficacy and Unacceptable Toxicity of Cyclophosphamide for the Induction of Mixed Chimerism and Renal Allograft Tolerance in Cynomolgus Monkeys

To induce mixed chimerism and renal allograft tolerance in cynomolgus monkeys, cyclophosphamide (CP) and total body irradiation (TBI) were compared as part of a nonmyeloablative conditioning regimen. CP induced dose-dependent neutropenia and lymphopenia, but hematopoietic recovery was more rapid than that observed in the TBI group. Absolute B cell counts after CP were significantly higher (P<0.01) than those in the TBI group. With CP, a total dose of 200 mg/kg with CD154 blockade regularly induced multilineage chimerism. Nevertheless, the recipients failed to achieve long-term survival because of rejection (3 of 5), posttransplantation B cell lymphoma (1 of 5), and toxicities of CP (1 of 5). As previously reported, 3 Gy of TBI with either splenectomy or CD154 blockade induced mixed chimerism and renal allograft tolerance, with significantly less morbidity and mortality than that produced by CP. Thus, TBI is more effective and less toxic than CP as part of a nonmyeloablative regimen for the induction of mixed chimerism and renal allograft tolerance in cynomolgus monkeys.