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49db18f0-1a28-4616-b816-916882ca445a | In the motion conditions tested here, both proposed approaches (ratio with Gaussian smoothing, or generative modelling) produced comparable improvements in \(R_1\) reproducibility in the presence of inter-scan motion. Equally importantly, when there was no overt motion neither method decreased reproducibility, which w... | d |
2455b594-99a5-4e5a-b485-625ae7a173a9 | The simpler ratio method defines one calibration image as the reference (denominator in equation (REF )), and may therefore be vulnerable to low SNR. The alternative generative modelling approach inherently adapts to variable SNR by estimating the position-specific net sensitivity modulation relative to a common image,... | d |
2044270a-88c6-453a-89b9-dc097d15b76c | The impact of movement on the effective transmit field has previously been investigated in the context of specific absorption rate management [1]}, [2]}, [3]}, [4]}. An important additional finding of the present work is the impact this can have on \(R_1\) estimates at 7T, which was negligible at 3T as demonstrated pr... | d |
c2307a2a-bf05-4266-bfc5-ef27f16295e4 | Inter-scan motion causes serially acquired weighted volumes to be differentially modulated by position-specific coil sensitivities leading to substantial errors when they are combined to compute quantitative metrics. We have demonstrated the efficacy of two methods at reducing these artefacts in the context of \(R_1\) ... | d |
3b17c371-6854-4aac-8783-7040df4102f7 | Approximation algorithms were proposed for the first time in the 1960s to solve challenging optimization problems [1]}. They are called approximation algorithms because they generate near-optimal solutions. They were mainly used to solve optimization problems that could not be solved efficiently using computational tec... | i |
a4f7490b-84de-4fe7-a895-f67a6f3594d6 | Approximation algorithms using probabilistic and randomized techniques had known tremendous advances between the 1980s and 1990s. They were named metaheuristic algorithms [1]}. Famous metaheuristic algorithms might include, for example, Simulated Annealing [2]}, Ant Colony Optimization [3]}, Evolutionary Computation [4... | i |
03865a8c-749a-4ee6-947f-a07e1b918868 | Mainly, there are two groups of metaheuristic algorithms: population-based and individual-based algorithms [1]}. Population-based algorithms use several agents, whereas individual-based algorithms use one agent. In the first group, several individuals swarm in the search space and cooperatively evolve towards the globa... | i |
e7609c50-8885-42f1-b972-1a82f1a4e4a5 | Metaheuristic algorithms have two fundamental components: exploration and exploitation [1]}. The exploration is called global optimization or diversification. The exploitation is named local optimization or intensification. The exploration allows metaheuristic algorithms to discover new search space regions and avoid b... | i |
183161b0-b385-422b-be17-631461007c27 | We propose a novel swarm-based metaheuristic algorithm for global optimization, named the Archerfish Hunting Optimizer (AHO). The algorithm is inspired by the shooting and jumping behaviors of archerfish when catching prey. The prominent features of AHO are:
| i |
30544115-1d41-429a-9e81-ba658d0c573f |
AHO has three controlling parameters to set, the population size, the swapping angle between the exploration and exploitation phases, and the attractiveness rate between the archerfish and the prey.
AHO uses elementary laws of physics (i.e., equations of the general ballistic trajectory) to determine the positions of... | i |
52c290db-2c4c-4c31-90e3-a2b4152fc656 | The performance of AHO is assessed using the benchmark CEC 2020 for unconstrained optimization. The considered benchmark contains ten challenging single objective test functions. For further information on this benchmark, the reader is referred to [1]}. The obtained results are compared to 12 most recent state-of-the-a... | i |
76b8e509-6f53-4422-9fb1-dfca361f74bd | The rest of the paper is organized as follows. Section illustrates the hunting behavior of archerfish and provides the source of inspiration for AHO. Section describes the proposed metaheuristic algorithm and its mathematical model. Sections and represent and discuss the statistical results and comparative study on... | i |
f21743af-9663-4884-9073-5b63b2036b86 | In this paper, a new population-based optimization algorithm termed the Archerfish Hunting Optimizer is introduced to handle constrained and unconstrained optimization problems. AHO is founded on the shooting and jumping behaviors of archerfish when hunting aerial insects in nature. Some equations are outlined to model... | d |
d662e83f-f376-4286-bdd7-452e35b40b4d | AHO is straightforwardly explained with uncomplicated exploration and exploitation techniques. It is desirable to introduce other evolutionary schemes such as mutation, crossover, or multi-swarm, which we plan to do in the future. In addition, we plan to develop the binary and multi-objective versions of AHO.
| d |
cac3b46a-76cb-4024-869b-5025189ba465 | Recent advances in deep learning [1]}, [2]} have led to state-of-the-art performance for varied classification tasks in natural language processing, computer vision and speech recognition. Traditional Artificial Neural Networks (ANN) use idealized computing units which have a differentiable, non-linear activation funct... | i |
44a259c8-448c-433a-a75a-fc96a4385182 | Spiking Neural Networks (SNN) have been proposed as an energy-efficient alternative to ANNs as they simulate the event-based information processing of the brain [1]}. These bio-inspired SNNs follow an asynchronous method of event processing using spiking neurons. The internal state of a spiking neuron is updated when i... | i |
84596410-e23d-4f0c-93cb-b8c51de276a8 | Although SNNs are considered as the third generation of neural networks holding the potential for sparse and low-power computation, their classification performance is considerably lower than those of ANNs. This can be attributed to the fact that gradient optimization techniques like the backpropagation algorithm can't... | i |
27c70b98-e516-464c-9773-b3064119752f | As shown by Camunas-Mesa et al. [1]}, the efficiency gain of SNNs from event-based processing can be further improved through the use of inputs from event-based sensors like a neuromorphic Dynamic Vision Sensor (DVS) [2]}. Event driven sensors represent the information dynamically by asynchronously transmitting the add... | i |
c64fdd4a-aaa3-470e-a897-a014392496e2 | In our previous work [1]}, we had demonstrated the effect of foveal-pit inspired filtering for synthetically generated datasets like MNIST [2]} and Caltech [3]}. In this work, we present the results of applying similar neural filtering to data generated by the DVS. In our proposed model, we process DVS outputs using bi... | i |
283c80bd-b48d-4edf-929c-5249d2106e53 | The processed features are then used to perform the classification using a Spiking Convolutional Neural Network (SCNN). The SCNN architecture is inspired by two previous works viz. Diehl et al. [1]} and Kheradpisheh et al. [2]}, while the model is implemented as in Gupta et al. [3]}. Each input is presented to the netw... | i |
2d97520a-875c-4bf0-9ecd-a4e56283cb16 | The rest of the paper is organized as follows: Section II describes the architecture of the proposed model including the response generation and filtering, Section III provides the results of the experiments and Section IV contains the conclusion and future directions.
| i |
9ebc8b74-6632-44d7-903f-01f446806279 | The overall architecture of our model consists of two main stages: the first stage is made up of the DVS response generation and neural filtering of output spikes; the second stage consists of performing classification using the SCNN. The proposed model is shown in Fig. REF and each of the individual stages are covere... | m |
c29e3d6a-0952-42e5-92dc-d55d91eee15a | In this paper, we have presented a novel method for processing the DVS spike responses of a visual pattern with foveal-pit inspired DoG filters that simulate the primate retinal system. The pattern was composed of varying number of vertical white and black bars of different spatial frequencies moving at a fixed velocit... | d |
1a82c139-374a-4e37-81d2-08b314a661a9 | The proposed model demonstrates the effect of applying neural filtering to real DVS data generated from a neuromorphic vision sensor. This builds upon our previous work [1]} that depicted the results of foveal-pit inspired filtering for synthetically generated datasets like MNIST [2]} and Caltech [3]}. Our model achiev... | d |
9066f30a-ccf8-4479-a413-c02e5656cdef | For our proposed network, the asynchronous DVS recordings generated from the first stage of the model were converted to an analog vector representation for training the frame-based classifier composed of convolution layers. As future work, we plan to adapt our spiking convolutional network architecture to directly proc... | d |
38a58f49-9718-4871-85b6-3dbcf1fa85a1 | Recent advances in pre-trained language models (PLMs) have created new state-of-the-art results on many natural language processing (NLP) tasks. While scaling up PLMs with billions or trillions of parameters [1]}, [2]}, [3]}, [4]}, [5]} is a well-proved way to improve the capacity of the PLMs, it is more important to e... | i |
fbddb126-d857-4edf-9559-ef5ca02a7b23 | Towards this direction, there are a few works that significantly improve the efficiency of PLMs.
The first is RoBERTa [1]} which improves the model capacity with a larger batch size and more training data. Based on RoBERTa, DeBERTa [2]} further improves the pre-training efficiency by incorporating disentangled attentio... | i |
fe73e194-40c7-4b43-80ed-9d3c645574d8 | In this paper, we explore two methods of improving the efficiency of pre-training DeBERTa.
Following ELECTRA-style training, we replace MLM in DeBERTa with RTD where the model is trained as a discriminator to predict whether a token in the corrupted input is either original or replaced by a generator. We show that DeBE... | i |
bd839b1c-0933-477e-927c-cd6bced4b5fd | The second is a new embedding sharing method.
In ELECTRA, the discriminator and the generator share the same token embeddings. However, our analysis shows that embedding sharing hurts training efficiency and model performance, since the training losses of the discriminator and the generator pull token embeddings into d... | i |
af131e2a-f580-4208-8e6c-e93c1e88aa76 | We pre-train three variants of DeBERTaV3 models, i.e., DeBERTaV3large, DeBERTaV3base and DeBERTaV3small.
We evaluate them on various representative natural language understanding (NLU) benchmarks and set new state-of-the-art numbers among models with a similar model structure. For example, DeBERTaV3large surpasses prev... | i |
615a9294-697a-40cf-84ab-526f0c779c11 | In this paper we explored methods to further improve the pre-training efficiency of PLMs. We start with combining DeBERTa with ELECTRA which shows a significant performance jump. Next, we perform extensive analysis and experiments to understand the interference issues between the generator and the discriminator which i... | d |
c1b437a1-5aa1-41bb-818c-e6af92380ef6 | We evaluate the DeBERTaV3 on a broad range of representative NLU tasks and show the significant performance improvements over previous SOTA models, e.g., DeBERTaV3large outperforms other models with a similar model structure by more than 1.37% with regards to GLUE average score and mDeBERTabase outperforms XLM-Rbase by... | d |
5c56b29b-0368-42f6-aacf-0ff8b50043da | Huge language models (LMs) such as BERT , GPT-3 , Jurassic-1 , PaLM , and others , , , , , have taken AI by storm, with the promise of serving as versatile, general-purpose foundations for many applications. Indeed, partly for this reason, they have been rebranded by some as “foundation models” . The term is meant broa... | i |
bda8db17-e050-4d70-9bd7-0028bdb1c118 | When viewed this way, it becomes clear that, despite their value, current LMs have inherent limitations. While versatile and impressive, the output of even huge LMs is in many cases wrong, and often ridiculously so . Here is a sample output of GPT-3 on some simple queries. (To be clear, this is not a critique of GPT-3 ... | i |
2506229b-0a04-4cbb-abe9-d594d2c2ce78 | For example, LMs can struggle to understand that there are no US cities with more than 20m citizens, that a math teacher is a person, don’t know what today’s date is, nor can they engage in even simple (e.g., mathematical) reasoning.
| i |
aad9cea3-0120-45ad-8b0e-074da2eab70f | When you look for the root cause, you realize the core limitations of LMs: They don’t have access to all relevant knowledge, and neural models are ill-suited for certain types of calculation. More specifically:
| i |
04adc775-abba-4763-a4be-de0039b8fb4f |
Lack of access to current information. Certain data constantly change – the exchange rate between the dollar and the Moroccan Dirham, current COVID numbers, the stock price of AAPL, the weather in Vancouver (OK, not so much), or even the current date. It’s impossible, by their design, for pretrained language models to... | i |
94b6209d-5736-470c-9226-4668482ed0cd |
Model explosion. Today’s LM’s zero-shot performance trails that of fine-tuned models. One can fine-tune the LM to a specific task, but then lose versatility. Contemporary efforts to mitigate the problem focus on training a huge LM jointly on many sets of curated NLP tasks in a massive multi-task setting (several leadi... | i |
c400821e-fb4f-40e3-8ae0-1cb9399dd04b | Despite all these shortcomings, large language models are an essential backbone of any future AI system. So the question is how to have our cake and eat it too, enjoying the benefits of self-supervised deep language models without suffering these drawbacks. The solution we offer takes the form of a flexible architectur... | i |
e8094f74-f0aa-4433-bbb9-62180ea1de96 | Thus a MRKL system consists of an extendable set of modules, which we term 'experts', and a router that routes every incoming natural language input to a module that can best respond to the input (the output of that module can be the output of the MRKL system, or be routed to another module). These modules can be:
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9446ce30-8b3c-4214-aab1-52fd7eca7260 |
Safe fallback: In case the input doesn’t match any existing expert module, the router sends the input directly to the general-purpose huge LM.
Robust extensibility: Since each expert is trained independently we are able to cheaply add new capabilities while guaranteeing that they do not compromise the performance of ... | i |
9d82e7d5-a7aa-490e-a71f-1fd6f64fc713 | We conducted our experiments with the \(7B\) parameters J1-large model using prompt-tuning with 10 prompt tokens. Across all experiments, we set our learning rate to be \(lr=0.3\) and used linear decay. The batch size was set to 32 and we defined a short warm-up depending on the number of steps in each experiment.
| m |
4ffc9c79-ab29-4dbc-8da5-97efc7a69fe9 | Experiments 1 and 2 below were trained for 3000 steps, and the reported results here were the test accuracy evaluated on the final model.
For the remaining experiments we used linear weight decay (\(0.001\) ), which we found to be crucial for the model's performance, and selected the best checkpoint using a validation ... | m |
1be5deb0-a097-4405-b91e-eab13d8c9945 | In all experiments we verified that there was no overlap between the problems included in the training set and those included in the test set. This also includes avoiding cases with the same underlying arithmetic expression, but using different wordings for training and testing. For a detailed description of the sizes ... | m |
f5d485f3-62c5-4096-84ec-cf7a6e1cb07f | In the following results we report the accuracy we achieve in different experiments, by which we mean the percent of problems in the relevant test set on which the system gave the correct answer. (We note again that, since the actual calculation is done by the calculator module, all errors are due to passing the wrong ... | r |
5e43bac3-b7b4-4909-b031-c696479424d8 | This paper introduces the concept of Modular Reasoning, Knowledge and Language (MRKL) systems, which embraces large language models (LMs) and augments them with an easily extensible set of external knowledge and reasoning modules. This flexible, neuro-symbolic design retains all the advantages of modern LMs, but avoids... | d |
ccda51ba-9486-4a25-a006-7f8bff72898e |
How does ZeRO-Offload scale the trainable model size compared to existing multi-billion parameter training solutions on a single GPU/DGX-2 node?
What is the training throughput of ZeRO-Offload on single GPU/DGX-2 node?
How does the throughput of ZeRO-Offload scale on up to 128 GPUs?
What is the impact of our CPU-Ad... | m |
2dfb4385-0a8c-4b97-9d42-2872a759fe69 | We presented ZeRO-Offload, a powerful GPU-CPU hybrid DL training technology with high compute efficiency and near linear throughput scalability, that can allows data scientists to train models with multi-billion parameter models even on a single GPU, without requiring any model refactoring. We open-sourced ZeRO-Offload... | d |
720bfacc-5d37-4ad0-a6ac-96cb123dabec | Quantum teleportation [1]}, [2]} is a strikingly curious quantum phenomenon with myriads of applications ranging from secure quantum communication to distributed quantum computing [3]}, [4]}, [5]}, [6]}, [7]}. First presented by Bennett et al. [1]}, quantum teleportation allows one to recreate an arbitrary qubit from j... | i |
75236d97-5d27-48be-9c0a-7f5e8846beae | It is known that, given a pre-shared Bell pair, the teleportation of an unknown qubit, requires the transmission of two classical bits [1]}. More generally, the teleportation of an \(N\) -state requires 2\(log_2 N\) classical bits [2]}. This, however, assumes that the sender has access to only one copy of the arbitrar... | i |
dd096c68-c893-48b3-8891-32041a2e22c8 | In this paper, we consider the case when the sender has more than one copy of an (unknown) arbitrary qubit \(\mathinner {|{\phi }\rangle }=a\mathinner {|{0}\rangle }+b\mathinner {|{1}\rangle }\) . This may happen in several practical situations where the unknown qubit is either the result of a periodic or on-demand nat... | i |
189b42a4-80c6-4715-ba27-ca194238fb3b | The probability of success of the reset attempts vary with the values of \(a\) and \(b\) and is given by \(2|ab|^2\) for the first reset attempt. Since, \(|ab|^2=|a|^2|b|^2 = |a|^2(1-|a|^2) = |a|^2-|a|^4\) and \(0\le |a|^2\le 1\) , by plotting the function (figure REF ) we see that \(|ab|^2\le \frac{1}{4}\) and \(... | d |
a076c5ac-7b31-4fb4-ac7b-4a11f9cdec95 | For example, when \(a=\frac{i}{\sqrt{2}}\) and \(b=\frac{1}{2}+\frac{i}{2}\) , the probability that the first reset attempt will succeed is \(\frac{1}{2}\) . After a successful reset, Alice makes a second attempt to teleport the qubit. The probability that entangled qubits will collapse into the desirable state (\(a\m... | d |
89cd5dfd-d525-4369-9e21-62facc83f46f | Consequently, in general, considering only the first reset attempt and an unknown qubit \(\mathinner {|{\phi }\rangle }\) the probability of ending up in the desired state (\(a\mathinner {|{00}\rangle }+b\mathinner {|{11}\rangle }\) ), that is a successful teleportation with only one-classical bit, is \(\frac{1}{2}+\f... | d |
82f32e76-a744-46df-b904-7e849ba77edf | Alice, however, does require the values of \(a\) and \(b\) for the reset attempts beyond the first one. This latter case is useful when the precision required for classical representation of the \(a\) and \(b\) values exceeds the available bandwidth or resources available on the classical channel. In other words, i... | d |
923e1a05-7247-4be1-bf43-b6dbbd0ef2a1 | The reset procedure presented above, when successful, reduces the teleportation protocol to one stage protocol under the standard setting of pre-shared Bell states. Furthermore, Alice knows at each stage whether the reset has succeeded or not. If the values of \(a\) and \(b\) are not known, and the reset fails, she c... | d |
9da23ff3-51d4-41f1-9743-87d5bba5a0f2 | It is easy to see that \(1.25\le H(T)\le 1.5\) depending on the values of \(a\) and \(b\) . For comparison, one of the protocols by Kak [1]} reduces the computational burden to 1.5 bits when the Bell states are pre-shared. The cost of reducing the computational burden to 1.25 bits, therefore, comes from the expenditu... | d |
7e8d6d2e-adc1-49fd-8d2d-d79a77935192 | The coronavirus disease 2019 (COVID-19) was identified in Wuhan city of China in December 2019 that arises due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]}. It is categorized as an infectious disease and spreads among people through coming in close contact with infected people generally via smal... | i |
881e7866-4bf8-4de4-9819-536d9b1e48b9 | Prediction of COVID-19 new cases per day will help the administration and planners to take the proper decision and help them in making effective policy to tackle the pandemic situation. The epidemiological models are very helpful to understand the trend of COVID-19 spread and useful in predicting the spread rate of the... | i |
f62e87ac-0a5f-4556-ab37-6fb66ba543a2 | Recurrent neural network (RNN) [1]} derived from the feedforward neural networks can use their interval states (memory) to process variable length sequences of data suitable for the sequential data. Long Short-Term Memory (LSTM) has been introduced by Hochreiter and Schmidhuber [2]} which overcomes the vanishing and ex... | i |
67385be2-84cc-42bf-8611-e2dddd9308c5 | From Equation REF to Equation REF , \( i,o,f \) and \( c \) represent the input gate, output gate, forget gate and cell activation vector respectively, \( m \) depicts hidden state vector also known as output vector of the LSTM unit. \( \mathbf {W} \) denotes the weight matrix, for example \( \mathbf {W}_{ix} \) ... | i |
3642ff71-177a-4854-b7a9-e948994f2453 | LSTM is a method having multiple layers which can map the input sequence to a vector having fixed dimensionality, in which the deep LSTM decodes the target sequence from the vector. This deep LSTM is essential for a recurrent neutral network model except on the input sequence. The LSTM can solve problems with long term... | i |
a8cd76b6-65ce-4a1c-936e-fd5d71b87f06 | Deep learning models such as LSTM and CNN are well suited for understanding and predicting the dynamical trend of COVID-19 spread and have recently been used in prediction by several researchers [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. Chandra et al. [8]} used the LSTM and its variants for ahead prediction of COVID-19... | i |
272d30f2-1132-49c7-bd73-75167f8411aa | In this paper, we employ the vanilla LSTM, stacked LSTM, ED-LSTM, Bi-LSTM, CNN, and hybrid CNN+LSTM model to capture the dynamic trend of COVID-19 spread and predict the COVID-19 daily confirmed cases for 7, 14 and 21 days for India and its four most affected states: Maharashtra, Kerala, Karnataka, and Tamil Nadu. To d... | i |
a7365bca-f1a3-41cc-b722-a4b5e9b8e639 | The rest of the manuscript is organized as follows. Section , describes the deep learning model along with experimental setup and evaluation metrics. In Section , we present the COVID-19 dataset and experimental results and discussions. Finally, the conclusion is made in Section .
<FIGURE> | i |
7f94a5ae-80aa-4594-af5d-7510b8933b7b | The COVID-19 outbreak trend is highly dynamic and depends on imposing various intervention strategies. To capture the complex trend, in this study, we proceed the following steps during the training, testing and forecasting.
| m |
e5debdc2-0b92-4a21-92a4-980b4de7db11 |
We used early COVID-19 data up to July 10, 2021, and split the COVID-19 time series data into training and testing data by taking the last 20 days data as testing data and remaining data as training data.
To avoid the inconsistency in COVID-19 time series data, the data is normalized in the interval[0,1] using 'MinMa... | m |
7b1ce2ab-65cb-4f5c-b7aa-ce2e77417fcb | RNN abd CNN approaches viz. vanilla LSTM, stacked LSTM, ED-LSTM, Bi-LSTM, CNN, and hybrid CNN+LSTM have been implemented in Python using Keras module of Tensorflow and consider the prediction by taking univariate approaches.
| m |
80675cc1-d2b3-4a89-a503-dbd85023c64d | The COVID-19 outbreak is a potential threat due to its dynamical behaviour and more threatening in a country like India because it is very densely populated. The researchers are engaged in seeking new approaches to understand the COVID-19 dynamics that will overcome the limitation of existing epidemiological models. In... | d |
a2323630-a5a6-4601-8ac4-66ad38eb3ee6 | The constraint satisfaction problem (CSP) is the widely studied combinatorial problem of determining whether a set of constraints admits at least one solution. It is common to parameterise this problem by a set of relations (a constraint language) which determines the allowed types of constraints, and by choosing diffe... | i |
8768a9b9-a2c9-408c-af92-c340f5a6f02d | The vast expressibility of infinite-domain CSPs makes the search for efficient solution methods extremely worthwhile.
While worst-case complexity results indicate that many interesting problems should be insurmountably hard to solve, they are nevertheless
solved in practice on a regular basis.
The discrepancy between t... | i |
025dfb7e-428b-4b76-afb7-0a445b77c0c2 | We begin by recapitulating the standard definition of backdoors for finite-domain CSPs.
Let \(\alpha \colon X \rightarrow D\) be an assignment. For a \(k\) -ary
constraint \(c = R(x_1, \ldots , x_k)\)
we denote by \(c_{\mid \alpha }\) the constraint over the relation \(R_0\) and with scope \(X_0\) obtained from \(... | i |
7ca70b25-85e0-42d1-b333-7423b5660a19 |
removing \((d_1 ,\ldots , d_k)\) from \(R\) if there exists \(1 \le i \le k\) such that
\(x_i \in X\) and \(\alpha (x_i) \ne d_i\) , and
removing from all remaining tuples all coordinates \(d_i\) with
\(x_i \in X\) .
| i |
5f7fde02-17b9-42ab-810b-0331f79a5dd9 | The scope \(X_0\) is obtained from \(x_1, \ldots , x_k\) by removing every \(x_i \in X\) . For a set \(C\) of
constraints we define \(C_{\mid \alpha }\) as \(\lbrace c_{\mid \alpha } \colon c \in C\rbrace \) . We now have everything in place to define the standard notion of a (strong) backdoor, in the context of Bo... | i |
46e2649d-0f55-4b7c-8a7f-ed24c7df3870 | Definition 1 (See, for instance, [1]} or [2]})
Let \({\mathcal {H}}\) be a set of CSP instances. A \({\mathcal {H}}\) -backdoor
for a CSP\((\Gamma _D)\) instance \((V,C)\) is a set \(B \subseteq V\) where
\((V \setminus B, C_{\mid \alpha }) \in {\mathcal {H}}\) for each \(\alpha \colon B \rightarrow D\) .
| i |
59a031c9-a2be-4e64-832d-dd5d11e1d657 | In practice, \({\mathcal {H}}\) is typically defined as a
polynomial-time solvable subclass of CSP and one is thus interested in finding a backdoor into the tractable class \({\mathcal {H}}\) .
If the CSP instance \(I\) has a backdoor of size \(k\) , then it can be solved in \(|D|^k \cdot {\rm poly}(||I||)\) time.
T... | i |
cd640a51-a7d8-4230-b0f5-e410422478b6 | Example 2 Let us first see why Definition REF is less
impactful for infinite-domain CSPs. Naturally, the most obvious problem is that one, even for a fixed \(B \subseteq V\) , need to consider infinitely many functions \(\alpha \colon V \rightarrow D\) , and there is thus no general argument which resolves the backdoo... | i |
1d1636cd-8fbd-4835-bdf7-e1da91877a7d | Hence, the usual definition of a backdoor fails to compensate for a
fundamental difference between finite and infinite-domain CSPs: that
assignments to variables are typically much less important
than the relation between variables.
| i |
0975d4ef-7d5a-41de-a641-0c0ef8d6d624 | The stylish Chinese font generation has attracted rising attention within recent years [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, since it has a wide range of applications including but not limited to the automatic generation of artistic Chinese calligraphy [13]}, art font design [14]} a... | i |
7bb4b8b2-f4ba-4265-997d-8dfd144c6646 | The existing Chinese font generation methods can be generally divided into two categories. The first category is firstly to extract some explicit features such as strokes and radicals of Chinese characters and then utilize some traditional machine learning methods to generate new characters [1]}, [2]}. The quality of f... | i |
73f61471-3f28-4098-9980-07444e7ae47c | The second category of Chinese font generation methods has been recently studied in [1]}, [2]}, [3]}, [4]}, [5]}, [6]} with the development of deep learning [7]}, particularly the generative adversarial networks (GAN) [8]}. Due to the powerful expressivity and approximation ability of deep neural networks, feature extr... | i |
488fcd39-2b63-49a4-95f2-7a83c116d976 | Due to the artificial nature of Chinese characters, the explicit stroke information contains amount of mode information of Chinese characters (see Figure REF ). This is very different from natural images, which are usually regarded to be generated according to some probability distributions at some latent spaces.
Inspi... | i |
8c280f25-a199-4268-b2b2-194a22233736 |
We propose an effective method called StrokeGAN for the generation of Chinese fonts with unpaired data. Our main idea is firstly to introduce a one-bit stroke encoding to capture the mode information of Chinese characters and then incorporate it into the training of CycleGAN [1]}, in the purpose of alleviating the mod... | i |
b32349e2-f601-4ad0-97f6-349c05d08566 | In recent years, many generation methods of stylish Chinese fonts have been suggested in the literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} with the development of deep learning. In [1]}, the authors adapted pix2pix model developed in [10]} for the image style translation problem to Chinese font generation a... | w |
47b877e7-716f-43e1-877c-bbd5728a0aaa | Motivated by the observation from traditional Chinese character generation and recognition methods (see [1]}, [2]}) that the explicit stroke feature can provide much mode information for a Chinese character, in this paper, we incorporate such stroke information into the training of CycleGAN for Chinese font generation ... | w |
0df8a46c-f781-4c3e-b49c-531d3fe88b1f | The rest of this paper is organized as follows. In Section 2, we present some preliminary work. In Section 3, we introduce the proposed method in detail. In Section 4, we provide a series of experiments to demonstrate the effectiveness of the proposed method. We conclude this paper in Section 5.
<FIGURE> | w |
9563e0fe-8f00-401f-9bed-54f8ce16cade | In this section, we provide a series of experiments to demonstrate the effectiveness of the suggested StrokeGAN. All experiments were carried out in Pytorch environment running Linux, AMD(R) Ryzen 7 2700x eight-core processor \(\times 16\) CPU, GeForce RTX 2080 GPU. Our codes are available in https://github.com/Jinsha... | m |
2f2ebd63-9938-4398-879c-47378eaf96b8 | This paper proposes an effective Chinese font generation method called StrokeGAN by incorporating a one-bit stroke encoding into CycleGAN
to tackle the mode collapse issue. The key intuition of our idea is that the stroke encodings of Chinese characters contain amount of mode information of Chinese characters, unlike t... | d |
f917755b-f95a-4bec-9050-6d69aedf46ac | Data analysis is a critical and dominant stage of the machine learning
lifecycle. Once the data is collected, most of the work goes into
studying and wrangling the data to make it fit for training. A highly
experimental phase follows where a model is selected and tuned for
optimal performance. The final model is then p... | i |
71af00c8-de7f-413f-9042-649c46389d2e | When compared to traditional software, the feedback loop of a machine
learning system is longer. While traditional software primarily
experiences change in code, a machine learning system matures
through changes in data, model & code
[1]}. Given the highly tangled nature of machine
learning systems, a change in any of ... | i |
2a86a1cc-a0a3-4edd-bb62-bf7340cb7d55 | AI has had a significant impact on the technology sector due to the
presence of large quantities of unbiased data [1]}. But
AI's true potential lies in its application in critical sectors such
as healthcare, wildlife preservation, autonomous driving, and criminal
justice system [2]}. Such high-risk domains almost
never... | i |
5627f9ed-a59e-4824-8260-924b06f6c56f | Since the study of software systems with machine learning components
is a fairly young discipline, resources are lacking to aid
practitioners in their day-to-day activities. The highly data-driven
nature of machine learning makes data equivalent to code in
traditional software. The notion of code smells is critical in
... | i |
bf78b321-0601-405f-89ed-7a85e8493a9c |
RQ1. What are the recurrent data quality
issues that appear in public datasets?
Analogous to code smells, we introduce the notion of data smells.
Data smells are anti-patterns in datasets that indicate early signs
of problems or technical debt.
RQ2. What is the prevalence of such data quality issues in
public dataset... | i |
419fba26-a9ef-4ae3-b3a1-563a2b9feda6 | The remainder of the paper is structured as follows. Section
provides an overview of related concepts and prior
work that has been done. The methodology followed by this paper is
presented in Section followed by the results in
Section . The paper concludes with a discussion of
the results, limitations and future work... | i |
898863d5-ced0-467c-91b3-c12fe0bc5c13 | Code smells were originally proposed by Kent Beck in the 1900s and
later popularised by [1]} in his
book Refactoring
[1]}, [3]}. Code smells are
indications of potential problems in the code and require engineers to
investigate further. Common code smells include presence of bloated
code such as large classes & long me... | w |
2776f17f-11e2-4970-aef4-5fd84cbdd92c | Data validation is a well established field of research with roots in
Database Management Systems (DBMS). With the wide adoption of
data-driven decision-making by businesses, significant efforts have
been made towards automated data cleaning and quality
assurance [1]}, [2]}, [3]}, [4]}. In
the context of machine learni... | w |
7d47baf7-2fab-4d59-85b6-1685f032f042 | AI engineering is a relatively young discipline of software
engineering (SE) research. The primary focus of the field is to
compare and contrast machine learning systems to traditional software
systems and adopt best practices from the SE community. The seminal
paper by [1]} was the first to
recognise that machine lear... | w |
db76fec5-9630-407b-afc9-6bb8e1101698 | Data scientists spend the majority of their time working with data,
yet unlike in software engineering, lack tools that can aid them in
their analysis [1]}, [2]}. This
study proposes a catalogue of data smells that can be beneficial to
practitioners and used as a framework for development of tools in the
future.
<TABLE... | w |
0c952056-114c-4fd8-9bac-e2ad108a12d9 | This section presents the results obtained from the analysis of public
datasets. The most recurrent data quality issues are presented first.
A catalogue of data smells showing the prevalence of such data quality
issues is presented next (See RQ1 and RQ2 in Section ).
This study analysed 25 public datasets from which 14... | r |
d7cc7695-fab0-44f7-9cd7-6de163df70bd |
Redundant value smells or smells which occur due to
presence of features that do not contribute any new information.
Categorical value smells or smells which occur due to
presence of features containing categorical data.
Missing value smells or smells which occur due to
absence of values in a dataset.
String value ... | r |
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