Miracle-dz/UniFAR-FTUs huggingface dataset:
An Expression Space Model for Facial Expression Analysis

Modeling of facial expression plays a very important role in the research of synthesis and recognition offacial expression. However the current facial expression model can not correctly represent the daily facial expressions. The nature of the facial expressions is analyzed. A qualitative description of the corresponding facial expression space is presented. And then a new facial expression space model is proposed with the characters of both discrete affective space model and continuous affective space model. To validate the rationality of the model, the experiments on a facial expression database JAFFE have been conducted using Gaussian mixture model based on the Gabor wavelet and principal component analysis. The distribution of different expressions is analyzed, and the qualitative and quantitative description of the facial expression space is accomplished. The experimental results show that the facial expression model proposed can rationally represent the daily facial expressions.
Engineering a Distributed Infrastructure for Large-Scale Cost-Effective Content Dissemination over Urban Vehicular Networks

This paper proposes a practical and cost-effective approach to construct a fully distributed roadside communication infrastructure to facilitate the localized content dissemination to vehicles in the urban area. The proposed infrastructure is composed of distributed lightweight low-cost devices called roadside buffers (RSBs), where each RSB has the limited buffer storage and is able to transmit wirelessly the cached contents to fast-moving vehicles. To enable the distributed RSBs working toward the global optimal performance (e.g., minimal average file download delays), we propose a fully distributed algorithm to determine optimally the content replication strategy at RSBs. Specifically, we first develop a generic analytical model to evaluate the download delay of files, given the probability density of file distribution at RSBs. Then, we formulate the RSB content replication process as an optimization problem and devise a fully distributed content replication scheme accordingly to enable vehicles to recommend intelligently the desirable content files to RSBs. The proposed infrastructure is designed to optimize the global network utility, which accounts for the integrated download experience of users and the download demands of files. Using extensive simulations, we validate the effectiveness of the proposed infrastructure and show that the proposed distributed protocol can approach to the optimal performance and can significantly outperform the traditional heuristics.
nso-HSVI: A Not-So-Optimistic Heuristic Search Value Iteration Algorithm for POMDPs

Point-based value iteration methods improve computational efficiency by reducing the search space size. Although global optimization can be obtained by algorithms such as HSVI and GapMin, their exploration of the optimal action is overly optimistic which therefore slows down the efficiency. In this paper, we propose a novel heuristic search method nso-HSVI (not-so-optimistic Heuristic Search Value Iteration) which uses a Monte-Carlo method to estimate the probabilities that actions are optimal according to the distribution of actions' Q-value function and applies the action of the maximum probability. Experimental results show that nso-HSVI outperforms HSVI, and by a large margin when the scale of the POMDP increases.
Using Simulation to Characterize Topology of Peer to Peer Botnets

Future network intruders are constantly changing, improving and extending the capabilities of their botnets. Peer to peer bots are now under widespread development and are quickly evolving into a much tougher species to kill. However, partly due to the lack of understanding of the structural potential of command and control mechanism a botnets can have, countering peer to peer botnets has been ineffective. In this paper, we explore the characteristic of communication networks created by peer to peer botnets. We propose a simulation approach to characterize the structural properties and robustness of P2P botnets, corresponding to different paradigms of forming botnets. Through calculation and simulation for topology construction procedures, we show that extremely resilient peer to peer botnets can be formed to deliver attack code quickly. Such a theory would help predict botnets containment for a given topology and help develop strategies to improve defense against P2P botnets, because applying those strategies can always translate into some network topology transformation.
A Moving Control Strategy Based on RSSI Real-time Localization in Multi-tier Mobile Sensor Networks

R esearch on communication collection pattern for traditional fixed nodes with some mobile ones has been already extensive. However, almost nothing is done in tiered movement policy while movement track is unknown and sensor nodes drift at random direction. To fit the development of “smart dust” wireless sensor network, a new moving control strategy based on RSSI real-time localization is proposed in the multi-tier mobile sensor networks. First, we divide the monitoring field into several sub-regions so that fusion nodes can collect data in their own parts respectively, and use constraints-based mobile localization (CML) algorithm to calculate the location information of each sensor node. Then, a new Maximum Likelihood Nearest Neighbor (MLNN) algorithm is proposed to design optimal routes for fusion nodes. Finally, each fusion node can collect sensed data along this route. Simulation results show that the moving control strategy can provide a good solution of the problem on tiered mobile network of instable communication and high-fraction of event loss . Only layouting a small number of fusion nodes, the network can receive higher data success rate and lower data transmission delay, and is very suitable for the large-scale industry real-time monitoring application of wireless sensor networks.
A Markov Decision Process model for elective inpatient admission with delay announcement

This paper investigates the improvement of the hospitalization admission process of elective inpatients by offering delay announcement to free patients from blind and long wait. We propose a Markov Decision Process (MDP) model for optimization of delay announcement strategies by taking into account patients' behavior with respect to waiting for hospitalization. Numerical experiments are conducted to study the hospital dominant case in which the delay announcement only tries to maximize the revenue of the hospital. Properties of the optimal delay announcement strategies were observed and conjectured.
Multistage nanoparticle delivery system for deep penetration into tumor tissue

Current Food and Drug Administration-approved cancer nanotherapeutics, which passively accumulate around leaky regions of the tumor vasculature because of an enhanced permeation and retention (EPR) effect, have provided only modest survival benefits. This suboptimal outcome is likely due to physiological barriers that hinder delivery of the nanotherapeutics throughout the tumor. Many of these nanotherapeutics are ≈100 nm in diameter and exhibit enhanced accumulation around the leaky regions of the tumor vasculature, but their large size hinders penetration into the dense collagen matrix. Therefore, we propose a multistage system in which 100-nm nanoparticles “shrink” to 10-nm nanoparticles after they extravasate from leaky regions of the tumor vasculature and are exposed to the tumor microenvironment. The shrunken nanoparticles can more readily diffuse throughout the tumor's interstitial space. This size change is triggered by proteases that are highly expressed in the tumor microenvironment such as MMP-2, which degrade the cores of 100-nm gelatin nanoparticles, releasing smaller 10-nm nanoparticles from their surface. We used quantum dots (QD) as a model system for the 10-nm particles because their fluorescence can be used to demonstrate the validity of our approach. In vitro MMP-2 activation of the multistage nanoparticles revealed that the size change was efficient and effective in the enhancement of diffusive transport. In vivo circulation half-life and intratumoral diffusion measurements indicate that our multistage nanoparticles exhibited both the long circulation half-life necessary for the EPR effect and the deep tumor penetration required for delivery into the tumor's dense collagen matrix.
Neovascularization in an arterio-venous loop-containing tissue engineering chamber: role of NADPH oxidase

Using an in vivo arterio‐venous loop‐containing tissue‐engineering chamber, we have created a variety of vascularized tissue blocks, including functional myocardium. The viability of the transplanted cells is limited by the rate of neovascularization in the chamber. A Nox2‐containing nicotinamide adenine dinucleotide phosphate (NADPH) oxidase is thought to have a critical role in ischaemic angiogenesis. In this study we investigated whether NADPH oxidase is involved in the neovascularization process in the tissue‐engineering chamber. New blood vessels originating from the venous and the arterial ends of the loop could be identified after 3 days, and the vessel density (by lectin staining) peaked after 7 days and was maintained for at least 14 days. This was accompanied by granulation tissue formation and concomitant increase in the mRNA level of Nox4 NADPH oxidase. Although the total level of Nox2 mRNA in the chamber tissue decreased from day 3 to day 7, immunohistochemistry identified a strong expression of Nox2 in the endothelial cells of the new vessels. In human microvascular endothelial cells, the NADPH oxidase inhibitor apocynin reduced NADPH oxidase activity and inhibited the angiogenic responses in vitro. Local treatment with the NADPH oxidase inhibitors apocynin or gp91ds‐tat peptide significantly suppressed the vessel growth in the chamber. In conclusion, NADPH oxidase‐dependent redox signalling is important for neovascularization in this novel tissue‐engineering chamber in vivo, and boosting this signalling might be a new approach to extending vascularization and tissue growth.
Supporting Component Oriented Development with Reusable Autonomous Classes

Reuse during development of software systems has long been touted as a mechanism of reducing costs, increasing quality and speeding up development. At the same time the ability to develop systems using a predominantly graphical approach has long been promised but has never really delivered. The described development approach aims to address both of these issues, by providing a development framework designed to maximize reuse, while simplifying the development effort. A secondary aim, to include mechanisms that implicitly offer support for contemporary software development processes, is also addressed by the proposed development framework.
Affine Refinement Types for Secure Distributed Programming

Recent research has shown that it is possible to leverage general-purpose theorem-proving techniques to develop powerful type systems for the verification of a wide range of security properties on application code. Although successful in many respects, these type systems fall short of capturing resource-conscious properties that are crucial in large classes of modern distributed applications. In this article, we propose the first type system that statically enforces the safety of cryptographic protocol implementations with respect to authorization policies expressed in affine logic. Our type system draws on a novel notion of “exponential serialization” of affine formulas, a general technique to protect affine formulas from the effect of duplication. This technique allows formulate of an expressive logical encoding of the authentication mechanisms underpinning distributed resource-aware authorization policies. We discuss the effectiveness of our approach on two case studies: the EPMO e-commerce protocol and the Kerberos authentication protocol. We finally devise a sound and complete type-checking algorithm, which is the key to achieving an efficient implementation of our analysis technique.
The iron chelator deferasirox induces apoptosis by targeting oncogenic Pyk2/β-catenin signaling in human multiple myeloma

Deregulated iron metabolism underlies the pathogenesis of many human cancers. Recently, low expression of ferroportin, which is the only identified non-heme iron exporter, has been associated with significantly reduced overall survival in multiple myeloma (MM); however, the altered iron metabolism in MM biology remains unclear. In this study we demonstrated, by live cell imaging, that MM cells have increased intracellular iron levels as compared with normal cells. In experiments to test the effect of iron chelation on the growth of MM cells, we found that deferasirox (DFX), an oral iron chelator used to treat iron overload in clinical practice, inhibits MM cell growth both in vivo and in vitro. Mechanistically, DFX was found to induce apoptosis of MM cells via the inhibition of proline-rich tyrosine kinase 2 (Pyk2), which is known to promote tumor growth in MM. Inhibition of Pyk2 is caused by the suppression of reactive oxygen species, and leads to downregulation of the Wnt/β-catenin signaling pathway. Taken together, our findings indicate that high levels of intracellular iron, which might be due to low ferroportin expression, play a role in MM pathophysiology. Therefore, DFX may provide a therapeutic option for MM that is driven by deregulated iron homeostasis and/or Pyk2/Wnt signaling.
A TV Program Discovery Dialog System using recommendations

We present an end-to-end conversational system for TV program discovery that uniquely combines advanced technologies for NLU, Dialog Management, Knowledge Graph Inference and Personalized Recommendations. It uses a semantically rich relational representation of dialog state and knowedge graph inference for queries. The recommender combines evidence for user preferences from multiple modalities such as dialog, user viewing history and activity logs. It is tightly integrated with the Dialog System, especially for explanations of recommendations. A demo of the system on a iPad will be shown.
Toward a tenant-aware query rewriting engine for Universal Table schema-mapping

In software as a service (SaaS) environments, designing a multi-tenant data architecture that supports shared database with custom extension is a non-trivial task. A general approach to support such an architecture is a middleware-level facility that supports the mapping of multiple single-tenant logical schemas in the application to one multi-tenant physical schema in the database. In this paper we follow this approach and report our preliminary results on the design and analysis of a query rewriting engine that can transparently transform tenant-specific logical queries into corresponding physical queries for Universal Table, a widely adopted and industry verified schema-mapping technique. A prototype and a sample SaaS application are implemented to verify the feasibility of the design of the query rewriting engine. Besides, performance analysis results that can be used to predict the overhead of schema-mapping in the engine are also reported.
A probabilistic framework for logo detection and localization in natural scene images

This paper presents a probabilistic approach for logo detection and localization in natural scene images. Two probability distributions are computed, one considering the features extracted from the key points located inside a region and the second refers to shape geometry defined by the key points. The barycentric co-ordinates are considered to define the shape statistics. The performance of the proposed approach has been reported on two publicly available datasets: BelgaLogos and Flickr Logos27. It is shown that statistically significant improvement is achieved over a recently proposed method.
On the Performance of Adaptive Packetized Wireless Communication Links Under Jamming

We employ a game theoretic approach to formulate communication between two nodes over a wireless link in the presence of an adversary. We define a constrained, two-player, zero-sum game between a transmitter/receiver pair with adaptive transmission parameters and an adversary with average and maximum power constraints. In this model, the transmitter's goal is to maximize the achievable expected performance of the communication link, defined by a utility function, while the jammer's goal is to minimize the same utility function. Inspired by capacity/rate as a performance measure, we define a general utility function and a payoff matrix which may be applied to a variety of jamming problems. We show the existence of a threshold (JTH) such that if the jammer's average power exceeds JTH, the expected payoff of the transmitter at Nash Equilibrium (NE) is the same as the case when the jammer uses its maximum allowable power, Jmax, all the time. We provide analytical and numerical results for transmitter and jammer optimal strategies and a closed form expression for the expected value of the game at the NE. As a special case, we investigate the maximum achievable transmission rate of a rate-adaptive, packetized, wireless AWGN communication link under different jamming scenarios and show that randomization can significantly assist a smart jammer with limited average power.
Intervening with care – varying outcomes of a training and development programme in elderly care in Sweden

Abstract Awareness of the mechanisms underlying training and development (T&D) programmes is crucial in creating sustainable learning conditions in organisations. The organisational and psychosocial aspects of the work environment in Swedish elderly care is the focus of this longitudinal study, and the relation between process and results of a T&D programme is investigated. A mixed-methods design enabled exploration of the content of the quality improvement efforts, followed by an examination of how aspects of the work environment are influenced. The perceived learning climate, workload and resource adequacy are shown to be influenced differently by different contents. Insights into the complexity surrounding T&D programmes are then offered. It is concluded that even if a seemingly sound method of quality improvement is implemented, this is not enough to guarantee success. Also, it is apparent that changes may sometimes be at the expense of employees’ work environment, which may hamper continuous learning.
Classifying Unseen Instances by Learning Class-Independent Similarity Functions

In this paper we formulate ZSR as a binary prediction problem. Our resulting classifier is class-agnostic. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We develop a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains, which ultimately leads to our class-agnostic classifier. Many of the existing embedding methods can be viewed as special cases of our probabilistic model. On ZSR our method shows 4.90% improvement over the state-of-the-art in accuracy averaged across four benchmark datasets. We also adapt ZSR method for zeroshot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).
Chinese unknown word identification as known word tagging

This work presents a tagging approach to Chinese unknown word identification based on lexicalized hidden Markov models (LHMMs). In this work, Chinese unknown word identification is represented as a tagging task on a sequence of known words by introducing word-formation patterns and part-of-speech. Based on the lexicalized HMMs, a statistical tagger is further developed to assign each known word an appropriate tag that indicates its pattern in forming a word and the part-of-speech of the formed word. The experimental results on the Peking University corpus indicate that the use of lexicalization technique and the introduction of part-of-speech are helpful to unknown word identification. The experiment on the SIGHAN-PK open test data also shows that our system can achieve state-of-art performance.
Does a community-based stress management intervention affect psychological adaptation among underserved black breast cancer survivors?

BACKGROUND In this randomized trial, Project CARE, we examined whether participation in a cognitive-behavioral stress management and breast cancer wellness and education program improved psychological outcomes among a sample of underserved black breast cancer survivors. METHODS Both complementary medicine interventions were 10-sessions, manualized, group-based, and were culturally adapted for black women in the community from evidence-based interventions. Participants were 114 black women (mean age = 51.1, 27-77 years) who had completed breast cancer treatment 0-12 months before enrollment (stages 0-IV, mean time since cancer diagnosis = 14.1 months). Women were enrolled upon completion of curative treatment (ie, surgical, chemotherapy, radiation oncology) and randomized to receive cognitive-behavioral stress management or cancer wellness and education program. RESULTS There was a remarkable 95% retention rate from baseline to 6-month follow-up. Participants in both conditions showed statistically significant improvement on indices of psychological well-being, including overall quality of life (Functional Assessment of Cancer Therapy-Breast), intrusive thoughts (Impact of Event Scale-Revised), depressive symptoms (Center for Epidemiologic Studies-Depression), and stress levels (Perceived Stress Scale) over the 6-month postintervention follow-up (all repeated measures analysis of variance within-subjects time effects: P < .05, except for overall mood; Profile of Mood States-Short Version). Contrary to hypotheses, however, condition × time effects were not statistically significant. CONCLUSIONS Findings suggest that improvements in multiple measures over time may have been due to intensive training in stress management, extensive provision of breast cancer information, or participation in an ongoing supportive group of individuals from a similar racial background. Implications bear on decisions about appropriate control groups, the timing of intervention delivery during the treatment trajectory, and perceived support from the research team.