Miracle-dz/UniFAR-FTUs huggingface dataset:
Infant Word Segmentation: AnIncremental, Integrated Model

For adults, the segmentation of continuous speech into words is generally effortless. For an infant with limited knowledge of the language being learned, the task is far from trivial. To succeed, she must identify the words of her language from a stream of input that rarely presents them in isolation and provides little feedback that she has segmented correctly. Although the problem of infant word segmentation received only minor attention in early studies of language development (Brown, 1973; Clark, 1974; MacWhinney, 1978), it has become an active area of research in the last fifteen years primarily because of its ability to serve as a testing ground for the type of statistical information that infants can use. While experimental work provides insight into the types of cues infants may be using, computational modeling of the task provides a unique opportunity to test proposed cues on representative data and validate potential approaches to using them. A model of word segmentation must integrate knowledge of infants’ experimental performance and developmental trajectory with a computational modeling framework to build as complete a picture of word segmentation as possible. While the benefit of this integration should be obvious, the connection between computational models and experimental and longitudinal studies of word segmentation is thus far tenuous. The most well-known modern computational models (Goldwater et al., 2009; Johnson & Goldwater, 2009) heavily focus on the role of transitional probabilities and Bayesian inference but have not connected with infant development in a meaningful way. In those models and many others, the focus has been on Marr’s computational level of analysis, investigating what a learner may be trying to optimize when trying to learn to segment rather than how it may reach its goal (Marr, 1983). In contrast, the approach discussed in this paper focuses on Marr’s algorithmic level, developing a specific, cognitively plausible strategy based on infants’ performance in word segmentation experiments and comparing its predictions to the patterns observed in infant development. We use experimental evidence to suggest the operations the segmenter should perform and propose a simple learning algorithm that predicts the type of developmental changes observed in child language development. We demonstrate the effectiveness of this model at the task of word segmentation and at replicating patterns of infant development.
Group and Team Coaching: The Essential Guide

Preface. Foreword. PART I. Introduction: What is Group Coaching? PART II. Learning, Holding and Exchange. Looking Deeper: The Secret Life of Groups. Eight Group Factors Influencing Learning and Change. PART III. Understanding Organizations, Groups and Teams: Systems Thinking. PART IV. Team Coaching. Learning Group Coaching. Supervision Groups. PART V. Strategies for Tackling Problem Behaviour. Groups that Do Not Work: Understanding and Tackling Dysfunctional Patterns in Group Behaviour. Managing Beginnings, Middles and Endings: Boundaries of The Group. Afterword. PART VI. Suggested Further Reading. Continuous Professional Development in the Unconscious Dynamics of Groups and Organizations. Frequently Asked Questions.
Distribution, ecology, and natural history of the recently rediscovered and critically endangered Santa Marta Sabrewing

The Santa Marta Sabrewing is a critically endangered (CR) hummingbird endemic to the Sierra Nevada de Santa Marta, Colombia. Prior to 2022, there were only three documented sightings of the sabrewing since it was described in 1879, including only one record between 1946 and 2022. As a result, this “lost” species has long been one of the most poorly-known birds in Colombia. We located a resident population of Santa Marta Sabrewing along the Guatapurí River near the Chemesquemena and Guatapurí villages in July 2022, and at its type locality, San José, in January 2023. Based on historical data and newly-collected field observations, we assess the species’ status and describe aspects of its natural history and ecology. Our review indicates the species has been frequently misidentified in the past, and that to date, documented evidence of its presence is limited to five localities, almost all of them restricted to the south-eastern slope of the Sierra Nevada de Santa Marta, along the mid Guatapurí river basin. Consequently, this bird appears to represent a case of microendemism. Following IUCN criteria, our data suggest that Santa Marta Sabrewing should remain listed as CR. Field observations indicate that the species is highly associated with watercourses, where males hold year-round territories and form leks. We obtained records of males in mid-elevation habitats (1,150–1,850 m) for 16 consecutive months between July 2022 and October 2023, suggesting that the species might not be an elevational migrant, as previously speculated. More information is needed to understand the species’ ecology so that effective conservation actions can be designed in collaboration with the indigenous communities with which the species coexists.
Prehistory of the West Indies.

Electrophysiology of the Heart (McGraw-Hill, New York, 1960 ), p. 323. 19. K. Frank and M. G. F. Fuortes, J. Physiol. London 130, 625 ( 1955 ); , ibid . 131, 424 (1956); , fbid. 134, 451 (1956); W. H. Freygang, Jr., and K. Frank, J. Ge}t. Physiol. 42, 749 ( 1959 ); I. Tasaki, E. H. Polley, F. Orrego, J. Neuro physiol. 20, 374 ( 1954); P. Fatt, ibid . 23, 61 (1957). 20. J. S. Coombs, J. C. Eccles, P. Patt, J. Physiol. Londo} 130, 291 ( 1955 ) . 21. D. R. Curtis and J. C. Watkins, J. Neurochem. 6, 1 17 ( 1960 ); D. R. Curtis, J. W. Phillis, J. C. Watkins, J. Physiol. London 150, 656 ( 1960 ) * D. R. Curtis and J. C. Watkins, in Inhibition fn the Nervous System and Gamma Amino Butyric Acid, E. Roberts, Ed. (Pergamon, New York, 1960), p. 424; D. R. Curtis, Intern. J. Neuropharmacol. 1, 239 (1962); D. R. Curtis and J. C. Watkins, J. Physiol. Lo}don 166! 1 ( 1963 ) . 22. D. R. Curtis, J. W. Phillis, J. C. Watkins, J. Physiol. London 158, 296 ( 1961 ) . 23. J. Robbins, ibid. 148, 39 (1959); A. Van Harreveld, J. Neurochem. 3, 300 ( 1959 ) . 24. D. R. Curtis and R. M. Eccles, J. Physfot. Londo}t 141, 435 (1958). 25. , ibid., p. 446. 26. B. Renshaw, J. Neurophysiol. 4, 167 ( 1941 ); J. C. Eccles, Arch. Ges. Physiol. 260, 385 ( 1955 ); , Proc. Roy. Soc. London, Ser. B 153, 445 ( 1961 ) . 27. J. C. Eccles, R. M. Eccles, K. Koketsu, J. Physiol. London 126, 524 ( 1954); J. C.Eccles, R. M. Eccles, P. Fatt, ibid. 131, 154 ( 1956); D. R. Curtis, J. C. Eccles, R. M. Eccles, ibid. 136, 420 ( 1957 ) . 28. H. McLennan, Experientia 18, 278 (1962), Synaptic Transmission (Saunders, Philadelphia, 1963 ), p. 98. 29. D. R. Curtis and R. Davis, J. Physiol. Lon don 165, 62 ( 1963 ); D. R. Curtis and J. C. Watkins, ibid . 166, 1 ( 1963 ) . 30. D. R. Curtis and R. Davis, Brit. J. Pharmacol. 18 217 (1962). 31. D. R. Curtis, Pharmacol. Rev. 15, 333 (1963) . 32. R. Spehlmann and H. Kapp, Arch. Ges. Physiol. 274, 37 ( 1961 ); R. Spehlmann, J. Neurophysiol. 26, 127 ( 1963 ); R. Jung, Klin. Woch. 36, 1153 ( 1958 ); r' s in Neuropsychopharmacology, P. B. Bradley, P. Deniker, C. Raduco-Thomas, Eds. (Elsevier, Amsterdam 1959 ), pp. 133-138. 33. K. Krnjevic and J. W. Phillis, Experie}ttia 17, 469 (1961); ibid. 18, 170 (1962); J. Physiol. London 165, 274 ( 1963 ); ibid . 166, 296 ( 1963 ); ibid., p. 328; Brit. J. Pharmacol. 20, 471 (1963). 34. K. A. C. Elliott, R. L. Swank, N. Henderson, Am. J. Physiol . 162, 469 ( 1950 ); F. C. MacIntosh and P. E. Oborin, Abstr. Intern. Physiol. ('ongr., l 9th ( 1953 ), p. 580; J. F. Mitchell, J. Physiol. London 155, 22P (1960); , ibid. 158, 21P ( 1961 ); ibid. 165, 98 ( 1963 ) . 35. A. J. deLorenzo, Bull. Johns Hopkins Hosp, 108, 258 (1961 ); K. Krnievic and A. Silver, J. Physiol. London 165, 3P ( 1963 ) . 36. J. D. Green, M. Mancia R. von Baumgarten, J. Neuro physiol. 25, 467 ( 1962 ); C. Yamamoto, T. Yamamoto, K. Iwana, ibid. 26, 403 ( 1963 ); C. G. Phillips, T. P. S. Powell, G. M. Shephard, J. Physiol. Lond on 168, 65 ( 1963 ); G. M. Shephard, ibid., p.
Image Phylogeny by Minimal Spanning Trees

Nowadays, digital content is widespread and also easily redistributable, either lawfully or unlawfully. Images and other digital content can also mutate as they spread out. For example, after images are posted on the Internet, other users can copy, resize and/or re-encode them and then repost their versions, thereby generating similar but not identical copies. While it is straightforward to detect exact image duplicates, this is not the case for slightly modified versions. In the last decade, some researchers have successfully focused on the design and deployment of near-duplicate detection and recognition systems to identify the cohabiting versions of a given document in the wild. Those efforts notwithstanding, only recently have there been the first attempts to go beyond the detection of near-duplicates to find the structure of evolution within a set of images. In this paper, we tackle and formally define the problem of identifying these image relationships within a set of near-duplicate images, what we call Image Phylogeny Tree (IPT), due to its natural analogy with biological systems. The mechanism of building IPTs aims at finding the structure of transformations and their parameters if necessary, among a near-duplicate image set, and has immediate applications in security and law-enforcement, forensics, copyright enforcement, and news tracking services. We devise a method for calculating an asymmetric dissimilarity matrix from a set of near-duplicate images and formally introduce an efficient algorithm to build IPTs from such a matrix. We validate our approach with more than 625000 test cases, including both synthetic and real data, and show that when using an appropriate dissimilarity function we can obtain good IPT reconstruction even when some pieces of information are missing. We also evaluate our solution when there are more than one near-duplicate sets in the pool of analysis and compare to other recent related approaches in the literature.
On the use of cluster validity for evaluation of MIGSOM clustering

Organizing data into homogeneous groups (clusters) is one of the most fundamental tasks of recognizing interesting repartitions in the data set. However, the challenge of clustering is how to evaluate the obtained results. In addition, clustering directly a data set, can be computationally heavy according to the data dimension. For this reason, it is convenient to cluster quantization prototypes to reduce computational cost. Self-Organizing Map (SOM) technique is appropriate for vector quantization and clustering high dimensional data. Nevertheless, SOM suffers from its static structure. Therefore, we have proposed a dynamic method called MIGSOM (Multilevel Interior Growing Self-Organizing Maps) which presents a different architecture of those which are proposed in the literature. The irregular and multilevel structure of MIGSOM is developed during the training process according to the data direction. Thus, the main objective of this paper is to evaluate clustering of MIGSOM prototypes with k-means. Therefore, in this paper we review our learning method MIGSOM. Then, we compare the use of three cluster evaluation measurements (DB, F_measure, Conn_Index) applied to the trained neural map by MIGSOM, GG and SOM.
An inequality for tactical configurations

This note is devoted to proving a theorem and a generalisation of it which were originally suggested by our group-theoretical studies but which in fact are purely combinatorial. After it was accepted for publication, we learned that the theorems are not new, although our proofs appear to be substantially simpler than those appearing elsewhere. We had checked the literature on combinatorial designs, whereas it was in the theory of hypergraphs that the results had already emerged, expressed in a very different language (see [6, 7, 8] and references quoted there). The proofs given in those papers use a considerable amount of sophisticated machinery, however. Even Calderbank's short proof [3] requires the theory of association schemes and their Bose-Mesner algebras. With the permission of the Editors we have decided to proceed with the publication of our proofs because they use only elementary counting techniques. A tactical configuration consists of a finite set Q of 'points', a finite set 3d of ' blocks' and an incidence relation between them, so that all blocks are incident with the same number k of points, and all points are incident with the same number r of blocks. (See, for example, Dembowski [4, p. 4].) If v := \Cl\ and b := \38\, then v, k, b, r are known as the parameters of the configuration. Counting incident point-block pairs, one sees that vr = bk. In what follows we shall consider blocks to be sets of points and use suggestive set-theoretic notation correspondingly. Nevertheless, the definition does not exclude the possibility that a tactical configuration may have ' repeated' blocks in the sense that two or more blocks may be incident with precisely the same set of k points. Indeed, from a configuration (Q, 3fi) with parameters v, k, b, r, a configuration may be obtained simply by repeating each block ft times (compare Fischer [5, p. 52]). This new configuration has parameters v, k, fib, fir, and we shall refer to it as the fith multiple of (€1,38). Going in the opposite direction, starting from (Q, 3d) we define % to be 3d with repetitions deleted; equivalently, it is the collection of subsets of Q that occur as the underlying point-sets of blocks. Echoing Fisher, we shall call (Q, 3SQ) the primary configuration underlying (Q, 3#). Note, however, that although all of its blocks are A;-sets, (Q., 3SQ) need not itself be a tactical configuration. In the language of hypergraphs, (Q,^o) * s a ^-uniform hypergraph (see [6,8]). The main focus of this note concerns tactical configurations (Q, 38) in which every pair of blocks meet (in the sense that there is a point incident with both of them). It is easy to see that the same is then true of the primary configurations (Q, 3S0). And it is not at all difficult to prove that v < k. In the language of hypergraphs, such tactical configurations are known as intersecting fc-uniform regular hypergraphs, and according to Z. Fiiredi (see [7, p. 158]), the conjecture that they have at most k—k +1 points was made by P. Erdos and by B. Bollobas, and was first proved by L. Lovasz
SPECTRUM: a software defined predictable many-core architecture for LTE baseband processing

Wireless communication standards such as Long Term Evolution (LTE) are rapidly changing to support the high data rate of wireless devices. The physical layer baseband processing has strict real-time deadlines, especially in the next-generation applications enabled by the 5G standard. Existing base station transceivers utilize customized Digital Signal Processing (DSP) cores or fixed-function hardware accelerators for physical layer baseband processing. However, these approaches incur significant non-recurring engineering costs and are inflexible to newer standards or updates. Software programmable processors offer more adaptability. However, it is challenging to sustain guaranteed worst-case latency and throughput at reasonably low-power on shared-memory many-core architectures featuring inherently unpredictable design choices, such as caches and network-on chip. We propose SPECTRUM, a predictable software defined many-core architecture that exploits the massive parallelism of the LTE baseband processing. The focus is on designing a scalable lightweight hardware that can be programmed and defined by sophisticated software mechanisms. SPECTRUM employs hundreds of lightweight in-order cores augmented with custom instructions that provide predictable timing, a purely software-scheduled on-chip network that orchestrates the communication to avoid any contention and per-core software controlled scratchpad memory with deterministic access latency. Compared to a many-core architecture like Skylake-SP (average power 215W) that drops 14% packets at high traffic load, 256-core SPECTRUM by definition has zero packet drop rate at significantly lower average power of 24W. SPECTRUM consumes 2.11x lower power than C66x DSP cores+accelerator platform in baseband processing. SPECTRUM is also well-positioned to support future 5G workloads.
Reform in Biology Education: Teachers' Implementation of a New Biology Curriculum

HASSE, ELEANOR ENTHOVEN. Reform in Biology Education: Teachers' Implementation of a New Biology Curriculum. (Under the direction of Glenda Carter.) Biology: A Community Context (BACC) (Leonard & Penick, 1998) is a National Science Foundation funded high school biology curriculum designed to meet the goals of the reform movement in science education as envisioned in the National Science Education Standards (National Research Council, 1996) and the Benchmarks for Science Literacy (American Association for the Advancement of Science (AAAS), 1993). This study of a pilot implementation of the BACC curriculum illustrates some of the issues that emerged for teachers as they used an inquiry curriculum in the context of a district emphasis on meeting standards. In the first paper, “Teaching biology content using an inquiry curriculum,” I discuss the issues involved with teaching biology content using this inquiry curriculum. Teachers had difficulty bridging the gaps they perceived between the content in the curriculum and the content in their district standards, they had difficulty using the inquiry activities in the curriculum to teach content, and they did not accept some aspects of the instructional strategy. Professional development for teachers implementing inquiry curricula in the context of accountability for content oriented standards needs to address these issues directly and in an on-going fashion. In the second paper, “Is Science Education Reform Reaching the Classroom? ”, I use a framework developed by the National Research Council (2002) to investigate the ways in which these teachers and their classroom practices were affected by national standards. This framework has three channels of influence: ways in which national standards might affect classroom practice and thus student learning. These channels are curriculum, teacher development, and assessment and accountability practices. In this pilot implementation of a reform curriculum, Biology: A Community Context (BACC) (Leonard & Penick, 1998), all three channels had been influenced by “Standards” based reform, but the channels were not completely internally aligned nor were they completely coordinated with one another. This led to implementation issues including increased breadth of objectives, increased time pressure and inadequate resources. These issues were barriers to teachers’ full acceptance of BACC. Addressing alignment in more depth prior to implementation of new curricula could lead to better classroom practice. Reform in Biology Education: Teachers' Implementation of a New
Pattern recognition in interdisciplinary perception and intelligence 1

This special issue came to our mind to celebrate the 50th anniversary of the field of Artificial Intelligence (AI) (Casals and Fernández-Caballero, 2007; Fernández-Caballero et al., 2008), with the aim of showing the interdisciplinarity of the fields of Pattern Recognition (PR), Intelligence and Perception, being them artificial or natural. Pattern Recognition is a field with a strong relation to Artificial Intelligence, although basically oriented to solve engineering problems in a large number of applications, and other systems like perception where the processing of sensory data is required for its interpretation. The name of Artificial Intelligence was coined by J. McCarthy (Dartmouth College, New Hampshire), M.L. Minsky (Harvard University), N. Rochester (I.B.M. Corporation) and C.E. Shannon (Bell Telephone Laboratories) in 1955, when they proposed to hold the ‘‘Dartmouth summer research project on ARTIFICIAL INTELLIGENCE”, known now as the Dartmouth Conference. The Dartmouth Conference was held during the summer of 1956, to discuss various aspects of learning and intelligence that could be simulated on machines. Pattern Recognition had been one of the important fields in AI (Minsky, 1961) until the early 1970’s when the first IJCPR (International Joint Conference on Pattern Recognition) was organised. The name of Pattern Recognition appeared later on with the objective of developing techniques in the area of classification oriented to solve engineering problems. Both areas share objectives and applications that can be solved in different ways. Pattern Recognition is the research area which studies the operation and design of systems that recognise patterns in data. It embraces sub-disciplines like discriminant analysis, feature extraction, error estimation, cluster analysis (together sometimes called statistical pattern recognition); grammatical inference, parsing and matching (sometimes called syntactical and structural pattern recognition). Pattern Recognition is largely related to other techniques such as Computer Vision, Fuzzy Sets, Neural Networks and Kernel Classifiers and to fields like Speech Recognition and Biological Perception, among others.
The economics of innovation : an introduction

Contents: Preface Part I: Context 1. What is the Economics of Innovation About? 2. Innovation in the History of Economic Thought Part II: Aspects of Innovation 3. Basic Concepts in Innovation 4. Process Innovation 5. Product Innovation 6. Innovative Pricing 7. Network Effects and Standards 8. Intellectual Property Part III: How Firms Achieve Innovation 9. Innovation and Creativity 10. The Entrepreneur and Innovation 11. Organisation for Innovation 12. Vision and Innovation 13. Clusters and Networks 14. Division of Labour Part IV: Innovation and the Consumer 15. The Passive Consumer and the Active Consumer 16. The Diffusion of Innovations Part V: Effects of Innovation 17. Innovation and Trade 18. Innovation and Market Structure 19. Innovation and Wealth Creation 20. Innovation and Competitiveness 21. Innovation and Sustainability Part VI: Innovation and Government 22. Innovation and Policy References Index
Pipelined Scheduler for Unicast and Multicast Traffic in Input-Queued Switches

We focus on designing efficient integrated schedulers for handling mixed unicast and multicast traffic. We consider an input-queued switch with a multicast-capable switch fabric. At each input port of the switch, there are N dedicated unicast VOQs and one shared multicast queue (MQ). An existing approach to the design of integrated scheduler (i.e., a sequential scheduler) is to run two component schedulers, one for multicast and one for unicast, sequentially in each time slot. To minimize the head-of-line blocking of multicast traffic, the multicast scheduler always runs first. But sequentially running two schedulers in each time slot is challenging, especially when the slot duration is small. In this paper, we first propose a pipelined integration of the two component schedulers (i.e., a pipelined scheduler), which allows twice the amount of time for each scheduler to execute. We then extend an existing single-bit- single-iteration unicast scheduler to ensure that even in the presence of multicast traffic, unicast traffic will be starvation-free. This is achieved by giving unicast traffic priority over multicast periodically. Finally, we present arguably the first single-bit-single-iteration multicast scheduling algorithm. Extensive simulation results show that our pipelined scheduler is efficient and provides delay-throughput performance comparable to the sequential scheduler.