Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial Integration and China's Global Impact

Product and financial market integration determine the global implications of China’s recent growth surge and its on-going transition from export led growth. These alter China’s structural imbalance (its excess product supply and excess saving), which in turn shifts the international terms of trade, changing asset yields causing deflationary and then inflationary pressures abroad. The effects are here quantified using a global macro model with national portfolio rebalancing, in which asset differentiation is used to index financial integration. The growth surge is found to have conferred on the advanced economies gains in their terms of trade, incompletely offset by structural unemployment. By contrast, the global effects of the transition are shown to reverse some of these impacts and to be amplified by further financial integration, particularly for the US.
The effects of balance training on gait late after stroke: a randomized controlled trial

Objective: To investigate the effects of balance training, using force platform biofeedback, on quantitative gait characteristics of hemiparetic patients late after stroke. Design: Randomized, controlled, assessor-blinded trial. Setting: Rehabilitation ward and gait laboratory of a university hospital. Subjects: Forty-one patients (mean (standard deviation; SD) age of 60.9 (11.7) years) with hemiparesis late after stroke (median time since stroke six months) were randomly assigned to an experimental or a control group. Interventions: The control group (n = 19) participated in a conventional stroke inpatient rehabilitation programme, whereas the experimental group (n = 22) received 15 sessions of balance training (using force platform biofeedback) in addition to the conventional programme. Main outcome measures: Selected paretic side time-distance, kinematic and kinetic gait parameters in sagittal, frontal and transverse planes were measured using a three-dimensional computerized gait analysis system, one week before and after the experimental treatment programme. Results: The control group did not show any statistically significant difference regarding gait characteristics. Pelvic excursion in frontal plane improved significantly (P = 0.021) in the experimental group. The difference between before-after change scores of the groups was significant for pelvic excursion in frontal plane (P = 0.039) and vertical ground reaction force (P = 0.030) in favour of experimental group. Conclusion: Balance training using force platform biofeedback in addition to a conventional inpatient stroke rehabilitation programme is beneficial in improving postural control and weight-bearing on the paretic side while walking late after stroke.
A NOVEL APPROACH FOR SATELLITE IMAGERY STORAGE BY CLASSIFYING THE NON-DUPLICATE REGIONS

Everyday satellite is capturing thousands of images which needs to be classified in a proper way. In this paper, we address the problem of replacing the existing images with the captured one. We provide a new solution by storing only the non-existing part of the image. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. In order to overcome this difficulty, we propose an efficient approach, which consists of an algorithm that can adopt robust feature kernel principle component analysis (KPCA) to reduce dimensionality of image. Concerning image clustering, we utilize Fuzzy N-Means algorithm. Finally data is stored into
3D localization of multiple audio sources utilizing 2D DOA histograms

Steered response power (SRP) techniques have been well appreciated for their robustness and accuracy in estimating the direction of arrival (DOA) when a single source is active. However, by increasing the number of sources, the complexity of the resulting power map increases, making it challenging to localize the separate sources. In this work, we propose an efficient 2D histogram processing approach which is applied on the local DOA estimates, provided by SRP, and reveals the DOA of multiple audio sources in an iterative fashion. Driven by the results, we also apply the same methodology to local DOA estimates of a known subspace method and improve its accuracy. The performance of the presented algorithms is validated with numerical simulations and real measurements with a rigid spherical microphone array in different acoustical conditions: for multiple audio sources with different angular separations, various reverberation and signal-to-noise ratio (SNR) values.
A New Supervised Over-Sampling Algorithm with Application to Protein-Nucleotide Binding Residue Prediction

Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at http://www.csbio. sjtu.edu. cn/bioinf/TargetSOS/.
Domain Adaptation of a Dependency Parser with a Class-Class Selectional Preference Model

When porting parsers to a new domain, many of the errors are related to wrong attachment of out-of-vocabulary words. Since there is no available annotated data to learn the attachment preferences of the target domain words, we attack this problem using a model of selectional preferences based on domain-specific word classes. Our method uses Latent Dirichlet Allocations (LDA) to learn a domain-specific Selectional Preference model in the target domain using un-annotated data. The model provides features that model the affinities among pairs of words in the domain. To incorporate these new features in the parsing model, we adopt the co-training approach and retrain the parser with the selectional preferences features. We apply this method for adapting Easy First, a fast non-directional parser trained on WSJ, to the biomedical domain (Genia Treebank). The Selectional Preference features reduce error by 4.5% over the co-training baseline.
Energy Efficiency Optimization with Simultaneous Wireless Information and Power Transfer in MIMO Broadcast Channels

Simultaneous wireless information and power transfer (SWIPT) is anticipated to have great applications in fifth-generation (5G) and beyond communication systems. In this paper, we address the energy efficiency (EE) optimization problem for SWIPT multiple-input multiple-output broadcast channel (MIMO-BC) with time-switching (TS) receiver design. Our aim is to maximize the EE of the system whilst satisfying certain constraints in terms of maximum transmit power and minimum harvested energy per user. The coupling of the optimization variables, namely, transmit covariance matrices and TS ratios, leads to a EE problem which is non-convex, and hence very difficult to solve directly. Hence, we transform the original maximization problem with multiple constraints into a min-max problem with a single constraint and multiple auxiliary variables. We propose a dual inner/outer layer resource allocation framework to tackle the problem. For the inner-layer, we invoke an extended SWIPT-based BC-multiple access channel (MAC) duality approach and provide two iterative resource allocation schemes under fixed auxiliary variables for solving the dual MAC problem. A sub-gradient searching scheme is then proposed for the outer-layer in order to obtain the optimal auxiliary variables. Numerical results confirm the effectiveness of the proposed algorithms and illustrate that significant performance gain in terms of EE can be achieved by adopting the proposed extended BC-MAC duality-based algorithm.
Objects from Animacy: Joint Discovery in Shape and Haar Feature Space

We propose that appearance descriptors derived from the complete animacy of an object during its scene presence more comprehensively capture the essence of an object than descriptors that merely encode uncorrelated sets of its instantaneous appearances. During its frame presence, an object presents itself in many poses with differing frequencies, thus generating multiple modes of varying strengths in the appearance feature space. Further, we utilize tracking information to extract the set of all appearances of the object, while excluding those intervals where the object is partly or fully occluded by other objects or background entities. This allows for completely unsupervised computation of the descriptors that consist of time-indexed vectors from shape and Haar feature templates which are then clustered to obtain appearance modes. These lead to the construction of object-animacy models as probability distributions over the space of co-occurrent shape and Haar templates. These object models are clustered further in an unsupervised manner by using different spatial clustering algorithms with a Bhattacharya distance metric between object models. Unsupervised categorization results on simple (PETS2000) and complex traffic scenes consisting of a wide variety of objects show robust performance of the proposed approach.
PHONETIC AND LINGUISTIC ASPECTS OF VOWEL REDUCTION

Suppose you are having dinner and you want the salt. There are various ways of asking your dish companions for the salt, but the shortest one is calling for "Salt! ". Apparently, such a request, that will normally be expressed in several words, can also be said in one single word (if we disregard the aspect of decency). Other examples of condensed messages are telegrams, the speech of young children, or the way you talk in it foreign country when you have little knowledge of the language. These examples suggest that in a natural communicative situation the important information in spoken or written messages is conveyed in only a few key words. If the message is not condensed, it is often not necessary to receive it literally, as long as the key words are properly understood. Ifwe restrict our attention to spoken messages, we may ask ourselves how much of the acoustic information of an utterance is really needed by the listener to grasp its meaning and how much of it is redundant. In view of what has been mentioned above, the answer to this question must probably be that listeners need little acoustic information to understand a message. In a natural speech situation listeners can use knowledge sources at very different levels: phonetic, phonological, morphological, grammatical, semantic and pragmatic. The use of these knowledge sources is guided by a strategy ofprediction and elimination. A prediction of words is possible, because the listener knows what the conversation is about, so he can focus on a small set of words with a high probability of occurrence for the particular subject of conversation. Elimination occurs in several stages going alternately 'bottom-up' and 'top-down'. That is, hypotheses of words based on acoustic information may be eliminated on the basis of constraints at a higher knowledge level which leads to the construction of alternative hypotheses. These are compared with the acoustic information which may lead in turn to new hypotheses and so on until the most likely sequence of words is selected. The process of listening in my view is basically of a probabilistic nature: Prediction of words with a high probability and elimination of words with a low probaollity, given the acoustic information. Perhaps this explains the success of speech recognition with probabilistic models such as Markov chains (Van Alphen and Van Bergem, this volume), although the approach of these models may differ considerably from the human way of perception. From the talker's point of view, the most economic way of communicating is to increase his articulatory effort just enough for words that contain important information, Le. content words, and decrease his articulatory effort for function words. It has been shown (Zue, 1985; McAllister, 1989) that the amount of phonetic richness is much greater in stressed than in unstressed syllables. Therefore phonemes in unstressed syllables provide little lexical constraint compared to those in stressed syllables. This implies that a talker is allowed to restrict his articulatory effort in content words to the
A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage. SIGNIFICANCE STATEMENT Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data.
CPSC 525 Using SIFT Features for Scene Geometry

This project tackles the problem of tracking object pose using image features. I attempted to calculate a particular object’s pose using SIFT image features extracted from multiple viewpoints. Later, I tried bundle adjustment with these points but failed to have a convergence at bundle adjustment. In the mean time I also used SIFT features for stitching images which I reported.
Structural Analysis of Chinese Dialect Speakers and Their Automatic Classification

In China, there are many different kinds of dialects and sub-dialects. Because there are many grammatical, lexical, phonological, and phonetic differences among them in varying degrees, people from different dialect regions always have difficulties in oral communica- tion. Since 1956, standard Mandarin has been popularized all over the country as official language and almost every dialect speaker began to learn Mandarin just as a second language. But affected by their native dialects, many of them speak Mandarin with regional accents. In modern speech processing technologies, speech is represented by spectrum which contains not only the dialectal linguistic information but also extra-linguistic information such as the gender and age of the speaker. In order to focus exclusively on the linguistic features of dialec- tal utterances, a speaker-invariant structural representation of speech, which was originally proposed by the second author inspired by in- fants' language acquisition (1, 2), is proposed to represent the pronunciation of Chinese dialect speakers. Since the purely dialectal informa- tion can be extracted by removing the extra-linguistic information from dialect speech, this pronunciation structure can be applied to esti- mate which dialect or sub-dialect region a speaker belongs to and to assess the pronunciation. In order to testify the validity of our approach, speaker classification based on the dialectal utterances of 38 Chinese finals are investigated especially in terms of robustness to speaker variability. The result is linguistically reasonable and highly independent of age and gender. After that, a sub-dialect corpus is developed with a list of characters as reading materials, which is originally used for linguists' investigation of dialect speakers' pronunciation. Then after the sub-dialect pronunciation structure is built for every speaker, their pronunciations are classified based on the distances among their structures. The result shows that the sub-dialect speakers can also be linguistically classified with little influence of their age and gender. In conclusion, this structural representation of Chinese dialects can extract the purely dialectal and sub-dialectal information from speech and works well on dialect-based and sub-dialect-based speaker classification. Index Terms: Chinese dialects, extra-linguistic feature, pronunciation structure, Bhattacharyya distance, speaker classification
Using the Model-Strategy-Application Approach to Developing Pre-Service Teachers’ Knowledge and Assessing Their Progress in Math Methods Courses

The purpose of this research was to contribute to a better understanding of how to enhance pre-service teachers’ knowledge of mathematics teaching through Model, Strategy, and Application (MSA). 153 pre-service teachers from six math methods courses (K-8) at two universities participated in this study. Both qualitative and quantitative methods were used to measure teacher learning and understanding of the MSA approach and to identify relationships between components of the MSA model. The results of this study show that using the MSA model can enhance pre-service teachers’ knowledge of mathematics teaching. It can be used as a measurable and practical model in assessing pre-service teachers’ learning progress in models, strategy, and application. In addition, the findings indicate that the three components of the MSA model are all interrelated and equally important; ignoring one or the other will result in the lack of proficiency in teachers’ knowledge of mathematics teaching. INTRODUCTION In recent years, many studies have focused on building mathematics teachers’ content knowledge, pedagogical knowledge, and pedagogical content knowledge (Shulman, 1987; Fennema, 1992; Ma, 1999; Hill & Ball, 2004; An, Kulm, & Wu, 2004). However, research has paid insufficient attention to systematically designing a repertoire of measurable and practical models of knowledge of mathematics teaching that allow pre-service teachers to not only build conceptual understanding and procedural skills, but also to be able to make connections between mathematics concepts, procedures, and applications. In order to teach effectively, pre-service teachers need to develop the knowledge of creating various representations to unpack abstract mathematics content and make meaningful and visible to students the ideas behind the concepts and procedures (National Research Council [NRC], 2001); they also need to perform computations efficiently and know when and how to apply computations and various models in real world situations. To achieve the goals above, this study explored an effective approach that comprises: 1) creating variou visual and meaningful mathematics models to convey abstract math ideas thru conceptual understanding; 2) developing various strategies to achieve fluency in computations; and 3) developing strategic competence in word problem applications. In the Model-Strategy-Application (MSA) approach, the three aspects of it form the critical components of teachers’ knowledge of mathematics teaching in this study.
Randomized decision forests for static and dynamic hand shape classification

This paper proposes a novel algorithm to perform hand shape classification using depth sensors, without relying on color or temporal information. Hence, the system is independent of lighting conditions and does not need a hand registration step. The proposed method uses randomized classification forests (RDF) to assign class labels to each pixel on a depth image, and the final class label is determined by voting. This method is shown to achieve 97.8% success rate on an American Sign Language (ASL) dataset consisting of 65k images collected from five subjects with a depth sensor. More experiments are conducted on a subset of the ChaLearn Gesture Dataset, consisting of a lexicon with static and dynamic hand shapes. The hands are found using motion cues and cropped using depth information, with a precision rate of 87.88% when there are multiple gestures, and 94.35% when there is a single gesture in the sample. The hand shape classification success rate is 94.74% on a small subset of nine gestures corresponding to a single lexicon. The success rate is 74.3% for the leave-one-subject-out scheme, and 67.14% when training is conducted on an external dataset consisting of the same gestures. The method runs on the CPU in real-time, and is capable of running on the GPU for further increase in speed.
Party Ballots, Reform, and the Transformation of America's Electoral System

1. An era in need of explanation 2. The puzzle of responsive elections 3. National forces in presidential elections 4. House of Representatives elections 5. Senate indirect and direct elections 6. State legislative elections 7. Gubernatorial elections 8. Dismantling the party-ticket system Appendix: states as bundles of electoral laws, 1840-1940.