Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks

Personalized drug response has received public awareness in recent years. How to combine gene test result and drug sensitivity records is regarded as essential in the real-world implementation. Research articles are good sources to train machine predicting, inference, reasoning, etc. In this project, we combine the patterns mined from biological research articles and categorical data to construct a drug-gene interaction network. Then we use the cell line experimental records on gene and drug sensitivity to estimate the edge embeddings in the network. Our model provides white-box explainable predictions of drug response based on gene records, which achieves 94.74% accuracy in binary drug sensitivity prediction task.
Joint Pruning on Activations and Weights for Efficient Neural Networks

With rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency. This work aims to advance the compression beyond the weights to neuron activations. We propose an end-to-end Joint Pruning (JP) technique which integrates the activation pruning with the weight pruning. By distinguishing and taking on the different significance of neuron responses and connections during learning, the generated network, namely JPnet, optimizes the sparsity of activations and weights for improving execution efficiency. To our best knowledge, JP is the first technique that simultaneously explores the redundancy in both weights and activations. The derived deep sparsification in the JPnet reveals more optimizing potentialities for the existing DNN accelerators dedicated for sparse matrix operations. The effectiveness of JP technique is thoroughly evaluated through various network models with different activation functions and on different datasets. With $<0.4\%$ degradation on testing accuracy, a JPnet can save $71.1\% \sim 96.35\%$ of computation cost, compared to the original dense models with up to $5.8\times$ and $10\times$ reductions in activation and weight numbers, respectively. Compared to state-of-the-art weight pruning technique, JPnet can further reduce the computation cost $1.2\times \sim 2.7\times$.
A Grasping-Centered Analysis for Cloth Manipulation

Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This article proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation.
A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

Porting code from CPU to GPU is costly and time-consuming; Unless much time is invested in development and optimization, it is not obvious, a priori, how much speed-up is achievable or how much room is left for improvement. Knowing the potential speed-up a priori can be very useful: It can save hundreds of engineering hours, help programmers with prioritization and algorithm selection. We aim to address this problem using machine learning in a supervised setting, using solely the single-threaded source code of the program, without having to run or profile the code. We propose a static analysis-based cross-architecture performance prediction framework (Static XAPP) which relies solely on program properties collected using static analysis of the CPU source code and predicts whether the potential speed-up is above or below a given threshold. We offer preliminary results that show we can achieve 94% accuracy in binary classification, in average, across different thresholds
TSN Algorithms for Large Scale Networks: A Survey and Conceptual Comparison

This paper provides a comprehensive survey of queueing and scheduling mechanisms for supporting large scale deterministic networks (LDNs). The survey finds that extensive mechanism design research and standards development for LDNs has been conducted over the past few years. However, these mechanism design studies have not been followed up with a comprehensive rigorous evaluation. The main outcome of this survey is a clear organization of the various research and standardization efforts towards queueing and scheduling mechanisms for LDNs as well as the identification of the main strands of mechanism development and their interdependencies. Based on this survey, it appears urgent to conduct a comprehensive rigorous simulation study of the main strands of mechanisms.
The Second DIHARD Diarization Challenge: Dataset, task, and baselines

This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain. The challenge comprises four tracks evaluating diarization performance under two input conditions (single channel vs. multi-channel) and two segmentation conditions (diarization from a reference speech segmentation vs. diarization from scratch). In order to prevent participants from overtuning to a particular combination of recording conditions and conversational domain, recordings are drawn from a variety of sources ranging from read audiobooks to meeting speech, to child language acquisition recordings, to dinner parties, to web video. We describe the task and metrics, challenge design, datasets, and baseline systems for speech enhancement, speech activity detection, and diarization.
Cooperative Rate Splitting for MISO Broadcast Channel With User Relaying, and Performance Benefits Over Cooperative NOMA

Due to its promising performance in a wide range of practical scenarios, Rate-Splitting (RS) has recently received significant attention in academia for the downlink of communication systems. In this letter, we propose and analyse a Cooperative Rate-Splitting (CRS) strategy based on the three-node relay channel where the transmitter is equipped with multiple antennas. By splitting user messages and linearly precoding common and private streams at the transmitter, and opportunistically asking the relaying user to forward its decoded common message, CRS can efficiently cope with a wide range of propagation conditions (disparity of user channel strengths and directions) and compensate for the performance degradation due to deep fading. The precoder design and the resource allocation are optimized by solving the Weighted Sum Rate (WSR) maximization problem. Numerical results demonstrate that our proposed CRS scheme can achieve an explicit rate region improvement compared to its non-cooperative counterpart and other cooperative strategies (such as cooperative NOMA).
Group Key Managements in Wireless Sensor Networks

A sensor network operating in open environments requires a network-wide group key for confidentiality of exchanged messages between sensor nodes. When a node behaves abnormally due to its malfunction or a compromise attack by adversaries, the central sink node should update the group key of other nodes. The major concern of this group key update procedure will be the multi-hop communication overheads of the rekeying messages due to the energy constraints of sensor nodes. Many researchers have tried to reduce the number of rekeying messages by using the logical key tree. In this chapter, we propose an energy-efficient group key management scheme called Topological Key Hierarchy (TKH). TKH generates a key tree by using the underlying sensor network topology with consideration of subtree-based key tree separation and wireless multicast advantage. Based on our detailed analysis and simulation study, we compare the total rekeying costs of our scheme with the previous logical key tree schemes and demonstrate its energy efficiency.
Adaptive data fusion using finite-set statistics

Real-time fusion algorithms are often patchworks of loosely integrated sub-algorithms, each of which addresses a separate fusion objective and each of which may process only one kind of evidence. Because these objectives are often in conflict, adaptive methods (e.g. internal monitoring and feedback control to dynamically reconfigure algorithms) are often necessary to ensure optimal performance. This paper describes a different approach to adaptive fusion in which explicit algorithm reconfiguration is largely unnecessary because conflicting objectives are simultaneously resolved within a self-reconfiguring, optimally integrated algorithm. This approach is based on Finite-Set Statistics (FISST), a special case of random set theory that unifies many aspects of multisource-multitarget data fusion, including detection, tracking, identification, and evidence accrual. This paper describes preliminary results in applying a FISST-based filtering approach to a ground-based, single-target identification scenario based on the fusion of several types of synthetic message-based data from several sensors.
Brain Tumor Segmentation using Genetic Algorithm and FCM Clustering Approach

Image processing is any type of signal processing in which we take any abnormal image of brain tumor and then produce an output which is extracted portion of tumor by applying genetic algorithm with fuzzy clustering means method. FCM is superior over different clustering approaches. This combined approach is used to improve segmentation efficiency and obtain higher value of true positive pixels belong to tumorous region. Genetic algorithm is a stochastic global optimization algorithm, their combination can prevent FCM being trapped in local optimum and give more better results in comparison to neural networks and CAD approaches.
Changing software correctly

Modifications of code that do not alter the functioning of object-oriented software are called refactorings. Refactoring tools, not programmers, should guarantee correctness to be most useful. When do refactorings retain equivalence? To tackle this question, I conceive a semantic model of refactorings. It aims at making it simple to prove refactorings correct, i.e., to prove that equivalence is retained. It is parameterized over an operational semantics. The crucial point is that the correspondence between original and refactored program need not be defined for all states and all program points. The formalism is crafted so as to offer a solution to a second problem: How can refactorings be applied in practice without giving up equivalence guarantees? The idea is this: The proof procedure for refactorings yields minimal obligations that must be satisfied by the transformed program. These conditions can be formulated as pre-and postconditions of statements in the code – they are local. Every program you want to apply the refactoring to needs to satisfy those conditions. It is not practical to check them by hand. I suggest that a refactoring tool does it for you: It will check simple conditions before applying the refactoring as " preconditions " and it will add specification and instrumentation to the transformed program for all those properties that cannot be readily checked or sensibly approximated – these are called " postconditions ". It will also add all conditions as specifications that encode architectural constrains. With every refactoring, the programmer thus codifies part of the tacit knowledge that is needed to know that the respective refactoring is applicable for the program. The idea to extract specifications from interactions between program and programmer can be applied to any change with defined semantics. A refactoring catalogue that is included does not mainly serve as an illustration of theoretical concepts. It shows that most refactorings can be reduced to individual code changes that are easy to verify using the theoretical framework and then be composed to the conventional refactorings as we know them. I.e., refactorings in this text hardly ever affect an unbounded number of code locations. The exception are data-refactorings. They are covered separately.
WHMS4: An integrated model for health remote monitoring: A case study in nursing homes for the elderly

Daily monitoring of vital signs using wearable devices is gaining more research and applications every day. This paper describes a medical monitoring system using a group of sensors coupled at the body of a patient. The system developed in this work aims to help health care professionals to monitor patients and also provide users with a treatment without interference in their routine.
PAPEL: A lexical ontology for Portuguese

PAPEL (Palavras Associadas Porto Editora Linguateca) is a lexical resource for natural language processing (NLP) of Portuguese which is being built by Linguateca, based on processing a major commercial Portuguese dictionary, the Dicionario da Ĺingua Portuguesa (DLP) [1] developed and owned by the largest Portuguese dictionary publisher, Porto Editora. There are known similar lexical resources for English and other languages. WordNet [10] is probably the most important reference and it is widely used in NLP research. WordNet was created from scratch, while we are creating the tools needed to build PAPEL (semi)automatically. We could say that this work is mainly inspired by the MindNet [11] project. MindNet is a knowledge representation resource that used a broad-coverage parser to build semantic networks from machine readable human dictionaries (MRDs). The process of using MRDs to do NLP started back in the 1970’s. Early known publications were from Nicoletta Calzolari [4]. A lot of work around MRDs took place during the 1980’s ([9], [3], [5], [8], [2] and many more), but it was not until MindNet that we got an independent lexical ontology with knowledge automatically extracted from MRDs. As far as we know, PAPEL is the first lexical ontology built by semi-automatic means for Portuguese, whose developments started October 2007. To start our research some preliminary studies were made and some information about the dictionary structure was collected. After an empirical analysis of the most frequent ngrams, some initial grammars including specific string patterns to extract semantic relations were created and tested with a small portion of the dictionary (about 5000 entries). The grammars were developed for the PEN parser 1 which is a parser that implements the Earley’s algorithm [7]. The first grammar extracted Cause-of/Caused-by relations between words. Currently we are working on grammars to extract the Part-of and Hypernymy relations. In the PAPEL project we have also developed a system for automatically identifying differences between the output of different versions of the same grammar, for regression testing. In the future we intend to have a lexical resource whose base structures are concepts and not words. In order to accomplish our purpose we are hoping that, after having a network consisting of relations between words, some different senses of the same word might emerge. We are also planning is using some disambiguation methods (similar to [6]) to identify clustering related senses. This presentation will start with a quick overview of existing similar resources, focusing on the the relations they provide. We will then show several patterns consisting of Portuguese words that can be used to identify some of those relations in the definitions of the DLP. Some examples of extracted relations and relations we believe we can extract will also be shown, as well as the tools developed. We will end with some discussion about the directions our work can take in the next months.
A Virtual Backbone Construction Algorithm Based on Connected Dominating Set in Wireless Sensor Networks

For the lack of the backbone network in wireless sensor networks,this paper proposes a virtual backbone construction algorithm based on connected dominating set in wireless sensor networks(IPCDS). The algorithm gives priority to more energy and closer nodes in the choice of backbone nodes,which makes network life longer and delay smaller.This paper proves that in unit disk graph the connected dominating set generated by the algorithm is at most 7.6opt+1.4,the message complexity and the time complexity are O(n). Simulation results show that the IPCDS algorithm is significantly better than other algorithms. Index Tterms - Wireless Sensor Networks, Virtual Backbone, Maximal Independent Set, Connected Dominating Set
A Region-Growing Adaptation-Based Algorithm for the Smart Detection of Remote Sensing Images

This study proposes an adaptive region-growing-based method that efficiently detects remote sensing images. To achieve efficient classification, the pixels in the regions produced by the region-growing procedure are used to classify various objects. An adaptive algorithm based on the proposed efficient classification method can automatically detect the remote images through Wi-Fi. Experiments show that the adaptive region-growing algorithm can achieve smart detection. The proposed method achieves better results than existing methods.
Automated Verification of State-based specifications against scenarios (A step toward relating in

While designing a system, it is critical to ensure that its behavioral speci cation is correct with respect to its requirements. These requirements are often described as a set of scenarios instantiating use-cases. Formal and automated veri cation of this correctness is desirable in this context. To formally describe scenarios, we use LSCs [Harel 98a], an extension of Message Sequence Charts (MSCs) allowing the expression of both safety and liveness conditions. An algorithm to automatically translate LSCs into temporal logic is presented. The obtained formulae can then be used by a model-checker to prove the correctness of the speci cation. Hence, a total automation of the veri cation process is obtained. To achieve a better e ciency in veri cation, we then re ne the translation, by splitting the formula into several smaller formulae. Again, we exhibit an algorithmic solution to support these methods.
Recognition of the spatial position of industrial 3D-objects using relaxation techniques

The determination of the spatial position and orientation of industrial objects by optical sensors is important for the application of image processing methods in industrial automation. All approach is described which employs the extraction of a 2D structural description from a monocular view of the scene in a first step and a comparison with the 3D structural description of prototypes in a second step in order to find the object position. The structural comparison employs a relaxation method. <<ETX>>
Modelling French Micromelody: Analysis and Synthesis

Abstract Research into microvariations of fundamental frequency (F₀) of speech (intrinsic and co-intrinsic effects) carried out on a large number of languages has brought to light universal tendencies which raise a number of problems, both from the point of view of perception and from that of production, for the interpretation and modelling of intonation. After a survey of these problems, experimental data for French are presented, concerning the intrinsic fundamental frequency (IF₀) of oral and nasal vowels and the co-intrinsic effect (CF₀) of consonants on the onset and pitch point of the F₀ of the following stressed vowels. It is shown that despite considerable intersubject variation, IF₀ differences between the classes of close, open and nasal vowels are significant in both prosodic contexts analysed, but that variations within these classes were not significant (except for the nasal vowels). The principal source of variation for the co-intrinsic effect (as has been observed in many languages) was the opposition voiced/voiceless for the preceding consonant. This effect was greater at the onset of the vowel (4 STs) than at the pitch point where it was comparable to the IF₀ effect (1 ST). A relative independence of the IF₀ and CF₀ effects was noted which tend either to combine or to cancel each other out depending on the phonetic context. A preliminary attempt to incorporate these effects into a synthetic model of intonation, by factoring out observed F₀ contours into segmental and suprasegmental components, suggests that unlike IF₀ and CF₀ effects of consonants, which intervene at a fairly late level of phonetic implementation, IF₀ effects of vowels are quite probably programmed at a higher level.
Comparing progression molecular mechanisms between lung adenocarcinoma and lung squamous cell carcinoma based on genetic and epigenetic networks: big data mining and genome-wide systems identification

Non-small-cell lung cancer (NSCLC) is the predominant type of lung cancer in the world. Lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) are subtypes of NSCLC. We usually regard them as different disease due to their unique molecular characteristics, distinct cells of origin and dissimilar clinical response. However, the differences of genetic and epigenetic progression mechanism between LADC and LSCC are complicated to analyze. Therefore, we applied systems biology approaches and big databases mining to construct genetic and epigenetic networks (GENs) with next-generation sequencing data of LADC and LSCC. In order to obtain the real GENs, system identification and system order detection are conducted on gene regulatory networks (GRNs) and protein-protein interaction networks (PPINs) for each stage of LADC and LSCC. The core GENs were extracted via principal network projection (PNP). Based on the ranking of projection values, we got the core pathways in respect of KEGG pathway. Compared with the core pathways, we found significant differences between microenvironments, dysregulations of miRNAs, epigenetic modifications on certain signaling transduction proteins and target genes in each stage of LADC and LSCC. Finally, we proposed six genetic and epigenetic multiple-molecule drugs to target essential biomarkers in each progression stage of LADC and LSCC, respectively.