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The dichotomy is stark. When it comes to the heroin and prescription opioid epidemic, the priority is saving lives. In contrast, the remedy for crack was mass arrests and stiff prison sentences. One state even seriously considered bringing back the whipping post to punish drug offenders.

The signature law of that era was the federal Anti-Drug Abuse Act of 1986, which singled out crack offenders for the most severe punishment. Under the law, possession of five grams of crack cocaine incurred the same five-year minimum sentence as 500 grams of powder cocaine. That 100-to-1 quantity disparity, the equivalent of 2 cups vs. 1 teaspoons of sugar, remained in place until 2010, when the federal ratio was reduced to 18 to 1.

[This story is part of our Crack vs. Heroin investigation. The project examines how the responses to crack and opioids created an unfair justice system; what needs to be done to fix the inequity; the role that culture and comedy play; and our top 5 takeaways from the project.]

\"The crack cocaine epidemic of the '80s was something that was looked at like this kind of a virus that needed to be stamped out, and the people who were involved in it where these subhumans who needed to be put away, \" Morello said.

By the time that happened to Lapinski, he was using crack in addition to Xanax, heroin and prescription opioids. One break after another saved him from a lengthy prison sentence. In 2010, he was sentenced to three years in state prison but was released on intensive supervised probation after about a year behind bars.

After Congress acted in '86, state lawmakers were quick to follow the federal government's lead. Fifteen states singled out crack offenders for more severe punishment, with quantity disparities between powder cocaine and crack ranging from 2-to-1 in California to 100-to-1 in Iowa and North Dakota.

Over the years, rates of crack use among blacks have only been slightly higher than among whites, but since whites are the majority of the population, most crack users are white. For example, in 2017, 4.5% of blacks and 3.9% of whites reported ever using crack in their lives, according to the federal drug use survey.

Yet nine times as many blacks as whites went to federal prison for crack offenses, from 1991 to 2001, the Network found. Black sentences for crack were double that for white crack offenders in federal court during those years: 148 months vs. 84 months. Among arrests, the largest racial disparity came in 1992 when blacks were arrested for cocaine at a rate nearly eight times higher than whites.

Those districts sent a total of 6,775 black crack defendants and 226 white defendants to prison during that time, nearly 30 times as many blacks compared to whites, according to a Network analysis of federal sentencing data.

In New Jersey, no white defendants were sent to federal prison on crack charges between 1991 and 1998, records show. Sixty black defendants were imprisoned on federal crack charges during those years.

The drug laws that Congress rushed to enact to battle crack in the 1980s, and were replicated throughout the states, in many ways made the horrible conditions in neighborhoods like the one Van Peebles filmed in even worse.

Drugs destroyed his family. His younger brother Jamil was gunned down in a drug-related altercation. He said his sister Monica died with a crack pipe in her hand. Billups himself was shot, cycled in and out of jail and contracted HIV.

In December 2018, President Donald Trump signed the bipartisan FIRST STEP Act that, among other reforms, made the law that reduced the 100-to-1 disparity between powder cocaine and crack retroactive. That meant that federal crack offenders, who were given five- and 10-year minimum sentences for crack amounts that were 100 times lower than the minimums for powder cocaine, saw their sentences reduced.

The good times ended when Wood was arrested, convicted of gang-related conspiracy charges to distribute more than 20 kilograms of crack as well as murder conspiracy under the federal racketeering law. He was sentenced in 2002 to 25 years in federal prison. His girlfriend was six months pregnant at the time.

Wood said he turned his life around in prison, earning several college degrees. Like many of his fellow inmates facing long sentences for crack offenses, Wood said he was deeply discouraged when the 2010 Fair Sentencing Act closed the door on his early release because the law was not retroactive.

A few months later in Dec 2019, I would join the Super Silly Hackathon for the second time with fellow retrocomputing enthusiast Hui Jing. We would develop a small game for Win 3.1 with great help from the knowledge gained from this first project.

Crack detection is one of the most important links of concrete structure maintenance, and it directly reflects how safe, durable, and applicable the concrete structure is. Conventional human-based crack detection method relies on trained inspectors to find cracks on the surface of a concrete structure based on their expertise and years of experiences. They assess the concrete structure through analysing position and width of cracks. Although human-based crack detection method is an effective way to detect cracks, the detection results are subjective and vary from one to another because inspectors only make evaluation of current condition according to existing guidelines and their experiences.

To improve the performance of image-based crack inspection methods, researchers turn to machine learning (ML) algorithms [15]. The ML-based methods first extract crack features using the IPTs, then evaluate whether or not the extracted features indicate cracks [16]. The artificial neural networks (ANNs) and Support Vector Machine (SVM) are typical ML algorithms, and they were adopted to detect concrete cracks, spalling, and other structural damages. However, the performance of this method relies on the extracted crack features, so the results of them have inevitably been affected by false feature extraction using IPTs.

To discard the extracting process of crack features, convolutional neural networks (CNNs) are imported to detect crack in images [17, 18]. CNNs are deep learning algorithms developed from the ANNs, and they are highlighted in image classification and object recognition [19]. Compared to the ANNs, the CNNs learn image features using fewer parameters computations due to the partial connections, sharing weights, and pooling process between neurons. The CNNs need to be trained using large number of manually classified images. The building of a database requires lots of human resources and computations, but the good news is that the existing well-annotated image databases (ImageNet [20], CIFIA-10 and CIFAR-100 [21], MNIST [22]) and parallel computations using graphic processing units (GPU) have solved the problems.

In this paper, a deep CNN is proposed to establish an image classifier for crack detection. The outstanding advantage of the proposed CNN-based crack detection is that it spares multifarious work from features preextraction and calculation compared to traditional methods [23]. Besides, the CNN needs not to convert the format of input images, but automatically learns crack features from images, which reduces workload of crack detection [24]. Moreover, our CNN-based crack detection approach achieves higher accuracy than existing method [25], because our CNN was trained using a large crack database with 60000 images taken from real concrete surfaces. Thanks to the large database, the detection results of our method will not be affected by noises of concrete surfaces such as roughness, light, shadows, or stain and so on.

The content of this research is described as follows. Section 2 introduces the methodology of the proposed method. Section 3 explains the CNN used and its related theories. Section 4 lists the training details of the CNN and results. Section 5 demonstrates testing results of the trained CNN on concrete crack images in realistic situations, and Section 6 is the conclusion of this paper.

Figure 1 shows a flow chart of using a CNN to detect cracks. It includes three steps: building crack database, training the CNN, and testing the trained CNN classifier. To train a CNN, a large amount of raw images are taken from concrete surface. The collected raw images are cropped into smaller images, and then, cropped small images are manually